diff --git a/.gitignore b/.gitignore
index 8f59f64d765a51f6c065725398becaa7482fed7a..69324b136eb21664c80c4f61fd29897924707275 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,9 +27,6 @@
# *.eps
# *.pdf
-## Generated if empty string is given at "Please type another file name for output:"
-.pdf
-
## Bibliography auxiliary files (bibtex/biblatex/biber):
*.bbl
*.bcf
diff --git a/bib.bib b/bib.bib
index 6aab437ec5f1041e804a49acd56ad55a1ff8a5b2..0d4059231271e0a1581853cccd8463890568fec9 100644
--- a/bib.bib
+++ b/bib.bib
@@ -1,9 +1,9 @@
@InProceedings{altmeyer2023endogenous,
author = {Altmeyer, Patrick and Angela, Giovan and Buszydlik, Aleksander and Dobiczek, Karol and van Deursen, Arie and Liem, Cynthia},
booktitle = {First {IEEE} {Conference} on {Secure} and {Trustworthy} {Machine} {Learning}},
- date = {2023},
title = {Endogenous {Macrodynamics} in {Algorithmic} {Recourse}},
file = {:altmeyerendogenous - Endogenous Macrodynamics in Algorithmic Recourse.pdf:PDF},
+ year = {2023},
}
%% This BibTeX bibliography file was created using BibDesk.
@@ -16,2413 +16,2648 @@
-@article{abadie2002instrumental,
- author = {Abadie, Alberto and Angrist, Joshua and Imbens, Guido},
- date = {2002},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Econometrica : journal of the Econometric Society},
- number = {1},
- pages = {91--117},
- shortjournal = {Econometrica},
- title = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
- volume = {70}}
-
-@article{abadie2003economic,
- author = {Abadie, Alberto and Gardeazabal, Javier},
- date = {2003},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {American economic review},
- number = {1},
- pages = {113--132},
- title = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
- volume = {93}}
-
-@inproceedings{ackerman2021machine,
- author = {Ackerman, Samuel and Dube, Parijat and Farchi, Eitan and Raz, Orna and Zalmanovici, Marcel},
- booktitle = {2021 {{IEEE}}/{{ACM Third International Workshop}} on {{Deep Learning}} for {{Testing}} and {{Testing}} for {{Deep Learning}} ({{DeepTest}})},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {1--8},
- publisher = {{IEEE}},
- title = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}}}
+@Article{abadie2002instrumental,
+ author = {Abadie, Alberto and Angrist, Joshua and Imbens, Guido},
+ title = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
+ number = {1},
+ pages = {91--117},
+ volume = {70},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometrica : journal of the Econometric Society},
+ shortjournal = {Econometrica},
+ year = {2002},
+}
-@article{allen2017referencedependent,
- author = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
- date = {2017},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Management Science},
- number = {6},
- pages = {1657--1672},
- title = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
- volume = {63}}
-
-@article{altmeyer2018option,
- author = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
- date = {2018},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation}}
+@Article{abadie2003economic,
+ author = {Abadie, Alberto and Gardeazabal, Javier},
+ title = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
+ number = {1},
+ pages = {113--132},
+ volume = {93},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {American economic review},
+ year = {2003},
+}
-@article{altmeyer2021deep,
- author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Deep {{Vector Autoregression}} for {{Macroeconomic Data}}},
- url = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
- bdsk-url-1 = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf}}
+@InProceedings{ackerman2021machine,
+ author = {Ackerman, Samuel and Dube, Parijat and Farchi, Eitan and Raz, Orna and Zalmanovici, Marcel},
+ booktitle = {2021 {{IEEE}}/{{ACM Third International Workshop}} on {{Deep Learning}} for {{Testing}} and {{Testing}} for {{Deep Learning}} ({{DeepTest}})},
+ title = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}},
+ pages = {1--8},
+ publisher = {{IEEE}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
-@book{altmeyer2021deepvars,
- author = {Altmeyer, Patrick},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Deepvars: {{Deep Vector Autoregession}}}}
+@Article{allen2017referencedependent,
+ author = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
+ title = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
+ number = {6},
+ pages = {1657--1672},
+ volume = {63},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Management Science},
+ year = {2017},
+}
-@misc{altmeyer2022counterfactualexplanations,
- author = {Altmeyer, Patrick},
- date = {2022},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
- url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
- bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl}}
+@Article{altmeyer2018option,
+ author = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
+ title = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2018},
+}
-@software{altmeyerCounterfactualExplanationsJlJulia2022,
- author = {Altmeyer, Patrick},
- date = {2022},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
- url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
- version = {0.1.2},
- bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl}}
-
-@unpublished{angelopoulos2021gentle,
- archiveprefix = {arXiv},
- author = {Angelopoulos, Anastasios N. and Bates, Stephen},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {2107.07511},
- eprinttype = {arxiv},
- file = {/Users/FA31DU/Zotero/storage/RKSUMYZG/Angelopoulos and Bates - 2021 - A gentle introduction to conformal prediction and .pdf;/Users/FA31DU/Zotero/storage/PRUEKRR3/2107.html},
- title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification}}
+@Article{altmeyer2021deep,
+ author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
+ title = {Deep {{Vector Autoregression}} for {{Macroeconomic Data}}},
+ url = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
+ bdsk-url-1 = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@Book{altmeyer2021deepvars,
+ author = {Altmeyer, Patrick},
+ title = {Deepvars: {{Deep Vector Autoregession}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@Misc{altmeyer2022counterfactualexplanations,
+ author = {Altmeyer, Patrick},
+ title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
+ url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+ bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2022},
+}
+
+@Software{altmeyerCounterfactualExplanationsJlJulia2022,
+ author = {Altmeyer, Patrick},
+ title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
+ url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+ version = {0.1.2},
+ bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2022},
+}
+
+@Unpublished{angelopoulos2021gentle,
+ author = {Angelopoulos, Anastasios N. and Bates, Stephen},
+ title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2107.07511},
+ eprinttype = {arxiv},
+ file = {:/Users/FA31DU/Zotero/storage/RKSUMYZG/Angelopoulos and Bates - 2021 - A gentle introduction to conformal prediction and .pdf:;:/Users/FA31DU/Zotero/storage/PRUEKRR3/2107.html:},
+ year = {2021},
+}
@Misc{angelopoulos2022uncertainty,
author = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
- date = {2022-09-03},
title = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
eprint = {2009.14193},
eprinttype = {arxiv},
- url = {http://arxiv.org/abs/2009.14193},
- urldate = {2022-12-07},
abstract = {Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90\%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.},
archiveprefix = {arXiv},
bdsk-url-1 = {http://arxiv.org/abs/2009.14193},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
- file = {/Users/FA31DU/Zotero/storage/5BYIRBR2/Angelopoulos et al. - 2022 - Uncertainty Sets for Image Classifiers using Confo.pdf;/Users/FA31DU/Zotero/storage/2QJAKFKV/2009.html},
+ file = {:/Users/FA31DU/Zotero/storage/5BYIRBR2/Angelopoulos et al. - 2022 - Uncertainty Sets for Image Classifiers using Confo.pdf:;:/Users/FA31DU/Zotero/storage/2QJAKFKV/2009.html:},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning},
+ month = sep,
number = {arXiv:2009.14193},
primaryclass = {cs, math, stat},
publisher = {{arXiv}},
+ year = {2022},
}
-@article{angelucci2009indirect,
- author = {Angelucci, Manuela and De Giorgi, Giacomo},
- date = {2009},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {American economic review},
- number = {1},
- pages = {486--508},
- title = {Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?},
- volume = {99}}
-
-@article{angrist1990lifetime,
- author = {Angrist, Joshua D},
- date = {1990},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The American Economic Review},
- pages = {313--336},
- title = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records}}
-
-@unpublished{antoran2020getting,
- archiveprefix = {arXiv},
- author = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {2006.06848},
- eprinttype = {arxiv},
- title = {Getting a Clue: {{A}} Method for Explaining Uncertainty Estimates}}
+@Article{angelucci2009indirect,
+ author = {Angelucci, Manuela and De Giorgi, Giacomo},
+ title = {Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?},
+ number = {1},
+ pages = {486--508},
+ volume = {99},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {American economic review},
+ year = {2009},
+}
-@article{arcones1992bootstrap,
- author = {Arcones, Miguel A and Gine, Evarist},
- date = {1992},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The Annals of Statistics},
- pages = {655--674},
- title = {On the Bootstrap of {{U}} and {{V}} Statistics}}
+@Article{angrist1990lifetime,
+ author = {Angrist, Joshua D},
+ title = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records},
+ pages = {313--336},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The American Economic Review},
+ year = {1990},
+}
-@article{ariely2003coherent,
- author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
- date = {2003},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The Quarterly journal of economics},
- number = {1},
- pages = {73--106},
- title = {``{{Coherent}} Arbitrariness'': {{Stable}} Demand Curves without Stable Preferences},
- volume = {118}}
-
-@article{ariely2006tom,
- author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
- date = {2006},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of Economic Behavior \& Organization},
- number = {1},
- pages = {1--10},
- title = {Tom {{Sawyer}} and the Construction of Value},
- volume = {60}}
-
-@article{arrieta2020explainable,
- author = {Arrieta, Alejandro Barredo and Diaz-Rodriguez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garcia, Salvador and Gil-Lopez, Sergio and Molina, Daniel and Benjamins, Richard and others},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Information Fusion},
- pages = {82--115},
- title = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
- volume = {58}}
-
-@article{auer2002finitetime,
- author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
- date = {2002},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Machine learning},
- number = {2},
- pages = {235--256},
- title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
- volume = {47}}
-
-@article{barabasi2016network,
- author = {Barab{\'a}si, Albert-L{\'a}szl{\'o}},
- date = {2016},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Network Science},
- title = {Network {{Science}}}}
+@Unpublished{antoran2020getting,
+ author = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
+ title = {Getting a Clue: {{A}} Method for Explaining Uncertainty Estimates},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2006.06848},
+ eprinttype = {arxiv},
+ year = {2020},
+}
-@unpublished{bastounis2021mathematics,
- archiveprefix = {arXiv},
- author = {Bastounis, Alexander and Hansen, Anders C and Vla{\v c}i{\'c}, Verner},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {2109.06098},
- eprinttype = {arxiv},
- title = {The Mathematics of Adversarial Attacks in {{AI}}--{{Why}} Deep Learning Is Unstable despite the Existence of Stable Neural Networks}}
+@Article{arcones1992bootstrap,
+ author = {Arcones, Miguel A and Gine, Evarist},
+ title = {On the Bootstrap of {{U}} and {{V}} Statistics},
+ pages = {655--674},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The Annals of Statistics},
+ year = {1992},
+}
-@article{bechara1997deciding,
- author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
- date = {1997},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Science (New York, N.Y.)},
- number = {5304},
- pages = {1293--1295},
- shortjournal = {Science},
- title = {Deciding Advantageously before Knowing the Advantageous Strategy},
- volume = {275}}
-
-@book{berlinet2011reproducing,
- author = {Berlinet, Alain and Thomas-Agnan, Christine},
- date = {2011},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Springer Science \& Business Media}},
- title = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics}}
+@Article{ariely2003coherent,
+ author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+ title = {``{{Coherent}} Arbitrariness'': {{Stable}} Demand Curves without Stable Preferences},
+ number = {1},
+ pages = {73--106},
+ volume = {118},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The Quarterly journal of economics},
+ year = {2003},
+}
-@misc{bernanke1990federal,
- author = {Bernanke, Ben S},
- date = {1990},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{National Bureau of Economic Research Cambridge, Mass., USA}},
- title = {The Federal Funds Rate and the Channels of Monetary Transnission}}
+@Article{ariely2006tom,
+ author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+ title = {Tom {{Sawyer}} and the Construction of Value},
+ number = {1},
+ pages = {1--10},
+ volume = {60},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Economic Behavior \& Organization},
+ year = {2006},
+}
-@article{besbes2014stochastic,
- author = {Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
- date = {2014},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Advances in neural information processing systems},
- pages = {199--207},
- title = {Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards},
- volume = {27}}
-
-@article{bholat2020impact,
- author = {Bholat, D and Gharbawi, M and Thew, O},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Bank of England Quarterly Bulletin, Q4},
- title = {The {{Impact}} of {{Covid}} on {{Machine Learning}} and {{Data Science}} in {{UK Banking}}}}
+@Article{arrieta2020explainable,
+ author = {Arrieta, Alejandro Barredo and Diaz-Rodriguez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garcia, Salvador and Gil-Lopez, Sergio and Molina, Daniel and Benjamins, Richard and others},
+ title = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
+ pages = {82--115},
+ volume = {58},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Information Fusion},
+ year = {2020},
+}
-@book{bishop2006pattern,
- author = {Bishop, Christopher M},
- date = {2006},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{springer}},
- title = {Pattern Recognition and Machine Learning}}
+@Article{auer2002finitetime,
+ author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
+ title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
+ number = {2},
+ pages = {235--256},
+ volume = {47},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Machine learning},
+ year = {2002},
+}
-@article{blaom2020mlj,
- abstract = {Blaom et al., (2020). MLJ: A Julia package for composable machine learning. Journal of Open Source Software, 5(55), 2704, https://doi.org/10.21105/joss.02704},
- author = {Blaom, Anthony D. and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J.},
- date = {2020-11-07},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- doi = {10.21105/joss.02704},
- file = {/Users/FA31DU/Zotero/storage/7AY87FGP/Blaom et al. - 2020 - MLJ A Julia package for composable machine learni.pdf;/Users/FA31DU/Zotero/storage/D69YSMVF/joss.html},
- issn = {2475-9066},
- journaltitle = {Journal of Open Source Software},
- langid = {english},
- number = {55},
- pages = {2704},
- shorttitle = {{{MLJ}}},
- title = {{{MLJ}}: {{A Julia}} Package for Composable Machine Learning},
- url = {https://joss.theoj.org/papers/10.21105/joss.02704},
- urldate = {2022-10-27},
- volume = {5},
- bdsk-url-1 = {https://joss.theoj.org/papers/10.21105/joss.02704},
- bdsk-url-2 = {https://doi.org/10.21105/joss.02704}}
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- publisher = {{PMLR}},
- title = {Weight Uncertainty in Neural Network}}
+@Article{barabasi2016network,
+ author = {Barab{\'a}si, Albert-L{\'a}szl{\'o}},
+ title = {Network {{Science}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Network Science},
+ year = {2016},
+}
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- title = {Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading}}
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- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {2110.01889},
- eprinttype = {arxiv},
- title = {Deep Neural Networks and Tabular Data: {{A}} Survey}}
+@Unpublished{bastounis2021mathematics,
+ author = {Bastounis, Alexander and Hansen, Anders C and Vla{\v c}i{\'c}, Verner},
+ title = {The Mathematics of Adversarial Attacks in {{AI}}--{{Why}} Deep Learning Is Unstable despite the Existence of Stable Neural Networks},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2109.06098},
+ eprinttype = {arxiv},
+ year = {2021},
+}
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+ volume = {275},
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+ journal = {Science (New York, N.Y.)},
+ shortjournal = {Science},
+ year = {1997},
+}
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+ title = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics},
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2011},
+}
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+ date-added = {2022-12-13 12:58:01 +0100},
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+ year = {1990},
+}
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+@Article{besbes2014stochastic,
+ author = {Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
+ title = {Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards},
+ pages = {199--207},
+ volume = {27},
+ date-added = {2022-12-13 12:58:01 +0100},
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+ journal = {Advances in neural information processing systems},
+ year = {2014},
+}
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+@Article{bholat2020impact,
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+ title = {The {{Impact}} of {{Covid}} on {{Machine Learning}} and {{Data Science}} in {{UK Banking}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Bank of England Quarterly Bulletin, Q4},
+ year = {2020},
+}
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+ publisher = {{springer}},
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+ year = {2006},
+}
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+@Article{blaom2020mlj,
+ author = {Blaom, Anthony D. and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J.},
+ title = {{{MLJ}}: {{A Julia}} Package for Composable Machine Learning},
+ doi = {10.21105/joss.02704},
+ issn = {2475-9066},
+ number = {55},
+ pages = {2704},
+ urldate = {2022-10-27},
+ volume = {5},
+ abstract = {Blaom et al., (2020). MLJ: A Julia package for composable machine learning. Journal of Open Source Software, 5(55), 2704, https://doi.org/10.21105/joss.02704},
+ bdsk-url-1 = {https://joss.theoj.org/papers/10.21105/joss.02704},
+ bdsk-url-2 = {https://doi.org/10.21105/joss.02704},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ file = {:/Users/FA31DU/Zotero/storage/7AY87FGP/Blaom et al. - 2020 - MLJ A Julia package for composable machine learni.pdf:;:/Users/FA31DU/Zotero/storage/D69YSMVF/joss.html:},
+ journal = {Journal of Open Source Software},
+ langid = {english},
+ month = nov,
+ shorttitle = {{{MLJ}}},
+ year = {2020},
+}
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+@InProceedings{blundell2015weight,
+ author = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
+ booktitle = {International Conference on Machine Learning},
+ title = {Weight Uncertainty in Neural Network},
+ pages = {1613--1622},
+ publisher = {{PMLR}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2015},
+}
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+@Article{borch2022machine,
+ author = {Borch, Christian},
+ title = {Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading},
+ pages = {101852},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Technology in Society},
+ year = {2022},
+}
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- title = {Artificial Intelligence and Systemic Risk}}
+@Unpublished{borisov2021deep,
+ author = {Borisov, Vadim and Leemann, Tobias and Se{\ss}ler, Kathrin and Haug, Johannes and Pawelczyk, Martin and Kasneci, Gjergji},
+ title = {Deep Neural Networks and Tabular Data: {{A}} Survey},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2110.01889},
+ eprinttype = {arxiv},
+ year = {2021},
+}
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- title = {Laplace {{Redux-Effortless Bayesian Deep Learning}}},
- volume = {34}}
+@Article{bramoulle2009identification,
+ author = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+ title = {Identification of Peer Effects through Social Networks},
+ number = {1},
+ pages = {41--55},
+ volume = {150},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of econometrics},
+ year = {2009},
+}
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- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Diffeomorphic Explanations with Normalizing Flows}}
+@Article{bramoulle2020peer,
+ author = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+ title = {Peer Effects in Networks: {{A}} Survey},
+ pages = {603--629},
+ volume = {12},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Annual Review of Economics},
+ year = {2020},
+}
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+@Unpublished{branco2015survey,
+ author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita},
+ title = {A Survey of Predictive Modelling under Imbalanced Distributions},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1505.01658},
+ eprinttype = {arxiv},
+ year = {2015},
+}
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+ author = {Brock, William Allen and Brock, William A and Hsieh, David Arthur and LeBaron, Blake Dean and Brock, William E},
+ title = {Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence},
+ publisher = {{MIT press}},
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {1991},
+}
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- urldate = {2022-11-08},
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+@InProceedings{buolamwini2018gender,
+ author = {Buolamwini, Joy and Gebru, Timnit},
+ booktitle = {Conference on Fairness, Accountability and Transparency},
+ title = {Gender Shades: {{Intersectional}} Accuracy Disparities in Commercial Gender Classification},
+ pages = {77--91},
+ publisher = {{PMLR}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2018},
}
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+ journal = {Econometrica : journal of the Econometric Society},
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+}
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Data Science},
+ options = {useprefix=true},
+ year = {2020},
+}
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- eprinttype = {arxiv},
- title = {Diffusion {{Models}} for {{Counterfactual Explanations}}}}
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+ journal = {Applied Sciences},
+ options = {useprefix=true},
+ year = {2021},
+}
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+ author = {Dhurandhar, Amit and Chen, Pin-Yu and Luss, Ronny and Tu, Chun-Chen and Ting, Paishun and Shanmugam, Karthikeyan and Das, Payel},
+ title = {Explanations Based on the Missing: {{Towards}} Contrastive Explanations with Pertinent Negatives},
+ volume = {31},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Advances in neural information processing systems},
+ year = {2018},
+}
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+ title = {Diffeomorphic Explanations with Normalizing Flows},
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+ year = {2021},
+}
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+}
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+ journal = {Management Science},
+ year = {2019},
+}
-@inproceedings{lakkaraju2020how,
- author = {Lakkaraju, Himabindu and Bastani, Osbert},
- booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {79--85},
- title = {" {{How Do I Fool You}}?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}}}
+@Article{gama2014survey,
+ author = {Gama, Jo{\~a}o and {\v Z}liobait{\.e}, Indr{\.e} and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},
+ title = {A Survey on Concept Drift Adaptation},
+ number = {4},
+ pages = {1--37},
+ volume = {46},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {ACM computing surveys (CSUR)},
+ year = {2014},
+}
-@unpublished{lakshminarayanan2016simple,
- archiveprefix = {arXiv},
- author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
- date = {2016},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {1612.01474},
- eprinttype = {arxiv},
- title = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles}}
-
-@unpublished{laugel2017inverse,
- archiveprefix = {arXiv},
- author = {Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
- date = {2017},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {1712.08443},
- eprinttype = {arxiv},
- shortjournal = {arXiv preprint arXiv:1712.08443},
- title = {Inverse Classification for Comparison-Based Interpretability in Machine Learning}}
-
-@thesis{lawrence2001variational,
- author = {Lawrence, Neil David},
- date = {2001},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- institution = {{University of Cambridge}},
- title = {Variational Inference in Probabilistic Models},
- type = {phdthesis}}
+@Unpublished{garivier2008upperconfidence,
+ author = {Garivier, Aur{\'e}lien and Moulines, Eric},
+ title = {On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {0805.3415},
+ eprinttype = {arxiv},
+ year = {2008},
+}
-@article{lecun1998mnist,
- author = {LeCun, Yann},
- date = {1998},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- shortjournal = {http://yann. lecun. com/exdb/mnist/},
- title = {The {{MNIST}} Database of Handwritten Digits}}
+@Book{gelman2013bayesian,
+ author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
+ title = {Bayesian Data Analysis},
+ publisher = {{CRC press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2013},
+}
-@article{lee2003best,
- author = {Lee, Lung-fei},
- date = {2003},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Econometric Reviews},
- number = {4},
- pages = {307--335},
- title = {Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances},
- volume = {22}}
-
-@article{lerner2013financial,
- author = {Lerner, Jennifer S and Li, Ye and Weber, Elke U},
- date = {2013},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Psychological science},
- number = {1},
- pages = {72--79},
- title = {The Financial Costs of Sadness},
- volume = {24}}
-
-@article{list2004neoclassical,
- author = {List, John A},
- date = {2004},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Econometrica : journal of the Econometric Society},
- number = {2},
- pages = {615--625},
- shortjournal = {Econometrica},
- title = {Neoclassical Theory versus Prospect Theory: {{Evidence}} from the Marketplace},
- volume = {72}}
-
-@article{lucas1976econometric,
- author = {Lucas, JR},
- date = {1976},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Econometric Policy Evaluation: A Critique `, in {{K}}. {{Brunner}} and {{A Meltzer}}, {{The Phillips}} Curve and Labor Markets, {{North Holland}}}}
+@Article{gilbert1998immune,
+ author = {Gilbert, Daniel T and Pinel, Elizabeth C and Wilson, Timothy D and Blumberg, Stephen J and Wheatley, Thalia P},
+ title = {Immune Neglect: A Source of Durability Bias in Affective Forecasting.},
+ number = {3},
+ pages = {617},
+ volume = {75},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of personality and social psychology},
+ year = {1998},
+}
-@inproceedings{lundberg2017unified,
- author = {Lundberg, Scott M and Lee, Su-In},
- booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
- date = {2017},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {4768--4777},
- title = {A Unified Approach to Interpreting Model Predictions}}
+@Article{gneezy2006uncertainty,
+ author = {Gneezy, Uri and List, John A and Wu, George},
+ title = {The Uncertainty Effect: {{When}} a Risky Prospect Is Valued Less than Its Worst Possible Outcome},
+ number = {4},
+ pages = {1283--1309},
+ volume = {121},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The Quarterly Journal of Economics},
+ year = {2006},
+}
-@book{lutkepohl2005new,
- author = {L{\"u}tkepohl, Helmut},
- date = {2005},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Springer Science \& Business Media}},
- title = {New Introduction to Multiple Time Series Analysis}}
+@InCollection{goan2020bayesian,
+ author = {Goan, Ethan and Fookes, Clinton},
+ booktitle = {Case {{Studies}} in {{Applied Bayesian Data Science}}},
+ title = {Bayesian {{Neural Networks}}: {{An Introduction}} and {{Survey}}},
+ pages = {45--87},
+ publisher = {{Springer}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
-@article{madrian2001power,
- author = {Madrian, Brigitte C and Shea, Dennis F},
- date = {2001},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The Quarterly journal of economics},
- number = {4},
- pages = {1149--1187},
- title = {The Power of Suggestion: {{Inertia}} in 401 (k) Participation and Savings Behavior},
- volume = {116}}
-
-@book{manning2008introduction,
- author = {Manning, Christopher D and Sch{\"u}tze, Hinrich and Raghavan, Prabhakar},
- date = {2008},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Cambridge university press}},
- title = {Introduction to Information Retrieval}}
+@Article{goldsmith-pinkham2013social,
+ author = {Goldsmith-Pinkham, Paul and Imbens, Guido W},
+ title = {Social Networks and the Identification of Peer Effects},
+ number = {3},
+ pages = {253--264},
+ volume = {31},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Business \& Economic Statistics},
+ year = {2013},
+}
-@misc{manokhin2022awesome,
- author = {Manokhin, Valery},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Awesome Conformal Prediction}}
+@Unpublished{goodfellow2014explaining,
+ author = {Goodfellow, Ian J and Shlens, Jonathon and Szegedy, Christian},
+ title = {Explaining and Harnessing Adversarial Examples},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1412.6572},
+ eprinttype = {arxiv},
+ year = {2014},
+}
-@article{manski1993identification,
- author = {Manski, Charles F},
- date = {1993},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The review of economic studies},
- number = {3},
- pages = {531--542},
- title = {Identification of Endogenous Social Effects: {{The}} Reflection Problem},
- volume = {60}}
-
-@article{markle2018goals,
- author = {Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
- date = {2018},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of Risk and Uncertainty},
- number = {1},
- pages = {19--50},
- title = {Goals as Reference Points in Marathon Running: {{A}} Novel Test of Reference Dependence},
- volume = {56}}
-
-@article{masini2021machine,
- author = {Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of Economic Surveys},
- title = {Machine Learning Advances for Time Series Forecasting}}
+@Book{goodfellow2016deep,
+ author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
+ title = {Deep {{Learning}}},
+ publisher = {{MIT Press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2016},
+}
-@article{mccracken2016fredmd,
- author = {McCracken, Michael W and Ng, Serena},
- date = {2016},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of Business \& Economic Statistics},
- number = {4},
- pages = {574--589},
- title = {{{FRED-MD}}: {{A}} Monthly Database for Macroeconomic Research},
- volume = {34}}
-
-@article{mcculloch1990logical,
- author = {McCulloch, Warren S and Pitts, Walter},
- date = {1990},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Bulletin of mathematical biology},
- number = {1},
- pages = {99--115},
- title = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
- volume = {52}}
-
-@article{migut2015visualizing,
- author = {Migut, MA and Worring, Marcel and Veenman, Cor J},
- date = {2015},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Data Mining and Knowledge Discovery},
- number = {1},
- pages = {273--295},
- title = {Visualizing Multi-Dimensional Decision Boundaries in {{2D}}},
- volume = {29}}
-
-@article{miller2019explanation,
- author = {Miller, Tim},
- date = {2019},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Artificial intelligence},
- pages = {1--38},
- title = {Explanation in Artificial Intelligence: {{Insights}} from the Social Sciences},
- volume = {267}}
-
-@inproceedings{miller2020strategic,
- abstract = {Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise.},
- author = {Miller, John and Milli, Smitha and Hardt, Moritz},
- booktitle = {Proceedings of the 37th {{International Conference}} on {{Machine Learning}}},
- date = {2020-11-21},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eventtitle = {International {{Conference}} on {{Machine Learning}}},
- file = {/Users/FA31DU/Zotero/storage/46I2QMPI/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf;/Users/FA31DU/Zotero/storage/NWREET6B/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf},
- issn = {2640-3498},
- langid = {english},
- pages = {6917--6926},
- publisher = {{PMLR}},
- title = {Strategic {{Classification}} Is {{Causal Modeling}} in {{Disguise}}},
- url = {https://proceedings.mlr.press/v119/miller20b.html},
- urldate = {2022-11-03},
- bdsk-url-1 = {https://proceedings.mlr.press/v119/miller20b.html}}
-
-@article{mischel1988nature,
- author = {Mischel, Walter and Shoda, Yuichi and Peake, Philip K},
- date = {1988},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of personality and social psychology},
- number = {4},
- pages = {687},
- title = {The Nature of Adolescent Competencies Predicted by Preschool Delay of Gratification.},
- volume = {54}}
-
-@inproceedings{mittelstadt2019explaining,
- author = {Mittelstadt, Brent and Russell, Chris and Wachter, Sandra},
- booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
- date = {2019},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {279--288},
- title = {Explaining Explanations in {{AI}}}}
+@Article{goodfriend2005incredible,
+ author = {Goodfriend, Marvin and King, Robert G},
+ title = {The Incredible {{Volcker}} Disinflation},
+ number = {5},
+ pages = {981--1015},
+ volume = {52},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Monetary Economics},
+ year = {2005},
+}
-@book{molnar2020interpretable,
- author = {Molnar, Christoph},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Lulu. com}},
- title = {Interpretable Machine Learning}}
+@Article{graham2017econometric,
+ author = {Graham, Bryan S},
+ title = {An Econometric Model of Network Formation with Degree Heterogeneity},
+ number = {4},
+ pages = {1033--1063},
+ volume = {85},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometrica : journal of the Econometric Society},
+ shortjournal = {Econometrica},
+ year = {2017},
+}
-@book{morgan2015counterfactuals,
- author = {Morgan, Stephen L and Winship, Christopher},
- date = {2015},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Cambridge University Press}},
- title = {Counterfactuals and Causal Inference}}
+@Article{greene2012econometric,
+ author = {Greene, William H},
+ title = {Econometric Analysis, 71e},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Stern School of Business, New York University},
+ year = {2012},
+}
-@article{mosteller1951experimental,
- author = {Mosteller, Frederick and Nogee, Philip},
- date = {1951},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Journal of Political Economy},
- number = {5},
- pages = {371--404},
- title = {An Experimental Measurement of Utility},
- volume = {59}}
-
-@inproceedings{mothilal2020explaining,
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- booktitle = {Proceedings of the 2020 {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
- date = {2020},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {607--617},
- title = {Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations}}
+@Article{grether1979economic,
+ author = {Grether, David M and Plott, Charles R},
+ title = {Economic Theory of Choice and the Preference Reversal Phenomenon},
+ number = {4},
+ pages = {623--638},
+ volume = {69},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The American Economic Review},
+ year = {1979},
+}
-@book{murphy2012machine,
- author = {Murphy, Kevin P},
- date = {2012},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{MIT press}},
- title = {Machine Learning: A Probabilistic Perspective}}
+@Article{gretton2012kernel,
+ author = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
+ title = {A Kernel Two-Sample Test},
+ number = {1},
+ pages = {723--773},
+ volume = {13},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The Journal of Machine Learning Research},
+ year = {2012},
+}
-@book{murphy2012machine,
- author = {Murphy, Kevin P},
- date = {2012},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{MIT press}},
- title = {Machine Learning: {{A}} Probabilistic Perspective}}
+@Unpublished{griffith2020name,
+ author = {Griffith, Alan},
+ title = {Name {{Your Friends}}, but {{Only Five}}? {{The Importance}} of {{Censoring}} in {{Peer Effects Estimates}} Using {{Social Network Data}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
-@book{murphy2022probabilistic,
- author = {Murphy, Kevin P},
- date = {2022},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{MIT Press}},
- title = {Probabilistic {{Machine Learning}}: {{An}} Introduction}}
+@Unpublished{grinsztajn2022why,
+ author = {Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l},
+ title = {Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2207.08815},
+ eprinttype = {arxiv},
+ year = {2022},
+}
-@article{nagel1995unraveling,
- author = {Nagel, Rosemarie},
- date = {1995},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {The American Economic Review},
- number = {5},
- pages = {1313--1326},
- title = {Unraveling in Guessing Games: {{An}} Experimental Study},
- volume = {85}}
-
-@unpublished{navarro-martinez2021bridging,
- author = {Navarro-Martinez, Daniel and Wang, Xinghua},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Bridging the Gap between the Lab and the Field: {{Dictator}} Games and Donations}}
+@Misc{group2020detailed,
+ author = {Group, Open COVID-19 Data Working},
+ title = {Detailed {{Epidemiological Data}} from the {{COVID-19 Outbreak}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
-@inproceedings{nelson2015evaluating,
- author = {Nelson, Kevin and Corbin, George and Anania, Mark and Kovacs, Matthew and Tobias, Jeremy and Blowers, Misty},
- booktitle = {2015 {{IEEE Symposium}} on {{Computational Intelligence}} for {{Security}} and {{Defense Applications}} ({{CISDA}})},
- date = {2015},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- pages = {1--8},
- publisher = {{IEEE}},
- title = {Evaluating Model Drift in Machine Learning Algorithms}}
+@InProceedings{gupta2011thompson,
+ author = {Gupta, Neha and Granmo, Ole-Christoffer and Agrawala, Ashok},
+ booktitle = {2011 10th {{International Conference}} on {{Machine Learning}} and {{Applications}} and {{Workshops}}},
+ title = {Thompson Sampling for Dynamic Multi-Armed Bandits},
+ pages = {484--489},
+ publisher = {{IEEE}},
+ volume = {1},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2011},
+}
-@book{nocedal2006numerical,
- author = {Nocedal, Jorge and Wright, Stephen},
- date = {2006},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Springer Science \& Business Media}},
- title = {Numerical Optimization}}
+@Book{hamilton2020time,
+ author = {Hamilton, James Douglas},
+ title = {Time Series Analysis},
+ publisher = {{Princeton university press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
-@misc{oecd2021artificial,
- author = {{OECD}},
- date = {2021},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- title = {Artificial {{Intelligence}}, {{Machine Learning}} and {{Big Data}} in {{Finance}}: {{Opportunities}}, {{Challenges}} and {{Implications}} for {{Policy Makers}}},
- url = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
- bdsk-url-1 = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf}}
+@Article{hamon2020robustness,
+ author = {Hamon, Ronan and Junklewitz, Henrik and Sanchez, Ignacio},
+ title = {Robustness and Explainability of Artificial Intelligence},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Publications Office of the European Union},
+ year = {2020},
+}
-@online{oecdArtificialIntelligenceMachine2021,
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- title = {Actionable Recourse in Linear Classification}}
+@Article{hsieh2016social,
+ author = {Hsieh, Chih-Sheng and Lee, Lung Fei},
+ title = {A Social Interactions Model with Endogenous Friendship Formation and Selectivity},
+ number = {2},
+ pages = {301--319},
+ volume = {31},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Applied Econometrics},
+ year = {2016},
+}
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- title = {Trustworthy {{Machine Learning}}}}
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+@Unpublished{immer2020improving,
+ author = {Immer, Alexander and Korzepa, Maciej and Bauer, Matthias},
+ title = {Improving Predictions of Bayesian Neural Networks via Local Linearization},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2008.08400},
+ eprinttype = {arxiv},
+ year = {2020},
+}
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+@Unpublished{innes2018fashionable,
+ author = {Innes, Michael and Saba, Elliot and Fischer, Keno and Gandhi, Dhairya and Rudilosso, Marco Concetto and Joy, Neethu Mariya and Karmali, Tejan and Pal, Avik and Shah, Viral},
+ title = {Fashionable Modelling with Flux},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1811.01457},
+ eprinttype = {arxiv},
+ year = {2018},
+}
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- publisher = {{Springer Science \& Business Media}},
- title = {All of Nonparametric Statistics}}
+@Article{innes2018flux,
+ author = {Innes, Mike},
+ title = {Flux: {{Elegant}} Machine Learning with {{Julia}}},
+ number = {25},
+ pages = {602},
+ volume = {3},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Open Source Software},
+ year = {2018},
+}
-@book{wasserman2013all,
- author = {Wasserman, Larry},
- date = {2013},
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- date-modified = {2022-12-13 12:58:01 +0100},
- publisher = {{Springer Science \& Business Media}},
- title = {All of Statistics: A Concise Course in Statistical Inference}}
+@Unpublished{ish-horowicz2019interpreting,
+ author = {Ish-Horowicz, Jonathan and Udwin, Dana and Flaxman, Seth and Filippi, Sarah and Crawford, Lorin},
+ title = {Interpreting Deep Neural Networks through Variable Importance},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1901.09839},
+ eprinttype = {arxiv},
+ year = {2019},
+}
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- eprint = {2001.10995},
- eprinttype = {arxiv},
- title = {The Case for {{Bayesian}} Deep Learning}}
+@InProceedings{jabbari2017fairness,
+ author = {Jabbari, Shahin and Joseph, Matthew and Kearns, Michael and Morgenstern, Jamie and Roth, Aaron},
+ booktitle = {International {{Conference}} on {{Machine Learning}}},
+ title = {Fairness in Reinforcement Learning},
+ pages = {1617--1626},
+ publisher = {{PMLR}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2017},
+}
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+@Article{jackson2007meeting,
+ author = {Jackson, Matthew O and Rogers, Brian W},
+ title = {Meeting Strangers and Friends of Friends: {{How}} Random Are Social Networks?},
+ number = {3},
+ pages = {890--915},
+ volume = {97},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {American Economic Review},
+ year = {2007},
+}
+
+@Unpublished{jeanneret2022diffusion,
+ author = {Jeanneret, Guillaume and Simon, Lo{\"\i}c and Jurie, Fr{\'e}d{\'e}ric},
+ title = {Diffusion {{Models}} for {{Counterfactual Explanations}}},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2203.15636},
+ eprinttype = {arxiv},
+ year = {2022},
+}
+
+@Article{johansson2005failure,
+ author = {Johansson, Petter and Hall, Lars and Sikstr{\"o}m, Sverker and Olsson, Andreas},
+ title = {Failure to Detect Mismatches between Intention and Outcome in a Simple Decision Task},
+ number = {5745},
+ pages = {116--119},
+ volume = {310},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Science (New York, N.Y.)},
+ shortjournal = {Science},
+ year = {2005},
+}
+
+@Article{johnsson2021estimation,
+ author = {Johnsson, Ida and Moon, Hyungsik Roger},
+ title = {Estimation of Peer Effects in Endogenous Social Networks: {{Control}} Function Approach},
+ number = {2},
+ pages = {328--345},
+ volume = {103},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Review of Economics and Statistics},
+ year = {2021},
+}
+
+@Article{jolliffe2003modified,
+ author = {Jolliffe, Ian T and Trendafilov, Nickolay T and Uddin, Mudassir},
+ title = {A Modified Principal Component Technique Based on the {{LASSO}}},
+ number = {3},
+ pages = {531--547},
+ volume = {12},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of computational and Graphical Statistics},
+ year = {2003},
+}
+
+@Article{joseph2021forecasting,
+ author = {Joseph, Andreas and Kalamara, Eleni and Kapetanios, George and Potjagailo, Galina},
+ title = {Forecasting Uk Inflation Bottom Up},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@Unpublished{joshi2019realistic,
+ author = {Joshi, Shalmali and Koyejo, Oluwasanmi and Vijitbenjaronk, Warut and Kim, Been and Ghosh, Joydeep},
+ title = {Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1907.09615},
+ eprinttype = {arxiv},
+ year = {2019},
+}
+
+@Unpublished{jospin2020handson,
+ author = {Jospin, Laurent Valentin and Buntine, Wray and Boussaid, Farid and Laga, Hamid and Bennamoun, Mohammed},
+ title = {Hands-on {{Bayesian Neural Networks}}--a {{Tutorial}} for {{Deep Learning Users}}},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2007.06823},
+ eprinttype = {arxiv},
+ year = {2020},
+}
+
+@Misc{kaggle2011give,
+ author = {Kaggle},
+ title = {Give Me Some Credit, {{Improve}} on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the next Two Years.},
+ url = {https://www.kaggle.com/c/GiveMeSomeCredit},
+ bdsk-url-1 = {https://www.kaggle.com/c/GiveMeSomeCredit},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Kaggle}},
+ year = {2011},
+}
+
+@online{kagglecompetitionGiveMeCredit,
+ author = {Kaggle Competition},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
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- volume = {36}}
-
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+ title = {Give Me Some Credit, {{Improve}} on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the next Two Years.},
+ url = {https://www.kaggle.com/c/GiveMeSomeCredit},
+ bdsk-url-1 = {https://www.kaggle.com/c/GiveMeSomeCredit}}
+
+@Article{kahneman1979prospect,
+ author = {Kahneman, Daniel and Tversky, Amos},
+ title = {Prospect {{Theory}}: {{An Analysis}} of {{Decision}} under {{Risk}}},
+ pages = {263--291},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometrica: Journal of the Econometric Society},
+ year = {1979},
+}
+
+@Article{kahneman1990experimental,
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+ title = {Experimental Tests of the Endowment Effect and the {{Coase}} Theorem},
+ number = {6},
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+ volume = {98},
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+ date-modified = {2022-12-13 12:58:01 +0100},
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+ year = {1990},
+}
+
+@Article{kahneman1992reference,
+ author = {Kahneman, Daniel},
+ title = {Reference Points, Anchors, Norms, and Mixed Feelings},
+ number = {2},
+ pages = {296--312},
+ volume = {51},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Organizational behavior and human decision processes},
+ year = {1992},
+}
+
+@Unpublished{karimi2020algorithmic,
+ author = {Karimi, Amir-Hossein and Von K{\"u}gelgen, Julius and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+ title = {Algorithmic Recourse under Imperfect Causal Knowledge: A Probabilistic Approach},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2006.06831},
+ eprinttype = {arxiv},
+ year = {2020},
+}
+
+@Unpublished{karimi2020survey,
+ author = {Karimi, Amir-Hossein and Barthe, Gilles and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+ title = {A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2010.04050},
+ eprinttype = {arxiv},
+ year = {2020},
+}
+
+@InProceedings{karimi2021algorithmic,
+ author = {Karimi, Amir-Hossein and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+ booktitle = {Proceedings of the 2021 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+ title = {Algorithmic Recourse: From Counterfactual Explanations to Interventions},
+ pages = {353--362},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@InProceedings{kaur2020interpreting,
+ author = {Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer},
+ booktitle = {Proceedings of the 2020 {{CHI}} Conference on Human Factors in Computing Systems},
+ title = {Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning},
+ pages = {1--14},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@Article{kehoe2021defence,
+ author = {Kehoe, Aidan and Wittek, Peter and Xue, Yanbo and Pozas-Kerstjens, Alejandro},
+ title = {Defence against Adversarial Attacks Using Classical and Quantum-Enhanced {{Boltzmann}} Machines},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Machine Learning: Science and Technology},
+ year = {2021},
+}
+
+@Unpublished{kendall2017what,
+ author = {Kendall, Alex and Gal, Yarin},
+ title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1703.04977},
+ eprinttype = {arxiv},
+ year = {2017},
+}
+
+@Article{kihoro2004seasonal,
+ author = {Kihoro, J and Otieno, RO and Wafula, C},
+ title = {Seasonal Time Series Forecasting: {{A}} Comparative Study of {{ARIMA}} and {{ANN}} Models},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2004},
+}
+
+@Book{kilian2017structural,
+ author = {Kilian, Lutz and L{\"u}tkepohl, Helmut},
+ title = {Structural Vector Autoregressive Analysis},
+ publisher = {{Cambridge University Press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2017},
+}
+
+@Unpublished{kingma2014adam,
+ author = {Kingma, Diederik P and Ba, Jimmy},
+ title = {Adam: {{A}} Method for Stochastic Optimization},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1412.6980},
+ eprinttype = {arxiv},
+ year = {2014},
+}
+
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+ title = {Batchbald: {{Efficient}} and Diverse Batch Acquisition for Deep Bayesian Active Learning},
+ pages = {7026--7037},
+ volume = {32},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Advances in neural information processing systems},
+ year = {2019},
+}
+
+@Unpublished{kuiper2021exploring,
+ author = {Kuiper, Ouren and van den Berg, Martin and van den Burgt, Joost and Leijnen, Stefan},
+ title = {Exploring {{Explainable AI}} in the {{Financial Sector}}: {{Perspectives}} of {{Banks}} and {{Supervisory Authorities}}},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2111.02244},
+ eprinttype = {arxiv},
+ year = {2021},
+}
+
+@Article{kydland1982time,
+ author = {Kydland, Finn E and Prescott, Edward C},
+ title = {Time to Build and Aggregate Fluctuations},
+ pages = {1345--1370},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometrica: Journal of the Econometric Society},
+ year = {1982},
+}
+
+@Unpublished{lachapelle2019gradientbased,
+ author = {Lachapelle, S{\'e}bastien and Brouillard, Philippe and Deleu, Tristan and Lacoste-Julien, Simon},
+ title = {Gradient-Based Neural Dag Learning},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1906.02226},
+ eprinttype = {arxiv},
+ year = {2019},
+}
+
+@InProceedings{lakkaraju2020how,
+ author = {Lakkaraju, Himabindu and Bastani, Osbert},
+ booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+ title = {" {{How}} Do {{I}} Fool You?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}},
+ pages = {79--85},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@InProceedings{lakkaraju2020how,
+ author = {Lakkaraju, Himabindu and Bastani, Osbert},
+ booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+ title = {" {{How Do I Fool You}}?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}},
+ pages = {79--85},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@Unpublished{lakshminarayanan2016simple,
+ author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
+ title = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1612.01474},
+ eprinttype = {arxiv},
+ year = {2016},
+}
+
+@Unpublished{laugel2017inverse,
+ author = {Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
+ title = {Inverse Classification for Comparison-Based Interpretability in Machine Learning},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1712.08443},
+ eprinttype = {arxiv},
+ shortjournal = {arXiv preprint arXiv:1712.08443},
+ year = {2017},
+}
+
+@Thesis{lawrence2001variational,
+ author = {Lawrence, Neil David},
+ title = {Variational Inference in Probabilistic Models},
+ type = {phdthesis},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ school = {{University of Cambridge}},
+ year = {2001},
+}
+
+@Article{lecun1998mnist,
+ author = {LeCun, Yann},
+ title = {The {{MNIST}} Database of Handwritten Digits},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ shortjournal = {http://yann. lecun. com/exdb/mnist/},
+ year = {1998},
+}
+
+@Article{lee2003best,
+ author = {Lee, Lung-fei},
+ title = {Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances},
+ number = {4},
+ pages = {307--335},
+ volume = {22},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometric Reviews},
+ year = {2003},
+}
+
+@Article{lerner2013financial,
+ author = {Lerner, Jennifer S and Li, Ye and Weber, Elke U},
+ title = {The Financial Costs of Sadness},
+ number = {1},
+ pages = {72--79},
+ volume = {24},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Psychological science},
+ year = {2013},
+}
+
+@Article{list2004neoclassical,
+ author = {List, John A},
+ title = {Neoclassical Theory versus Prospect Theory: {{Evidence}} from the Marketplace},
+ number = {2},
+ pages = {615--625},
+ volume = {72},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Econometrica : journal of the Econometric Society},
+ shortjournal = {Econometrica},
+ year = {2004},
+}
+
+@Article{lucas1976econometric,
+ author = {Lucas, JR},
+ title = {Econometric Policy Evaluation: A Critique `, in {{K}}. {{Brunner}} and {{A Meltzer}}, {{The Phillips}} Curve and Labor Markets, {{North Holland}}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {1976},
+}
+
+@InProceedings{lundberg2017unified,
+ author = {Lundberg, Scott M and Lee, Su-In},
+ booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
+ title = {A Unified Approach to Interpreting Model Predictions},
+ pages = {4768--4777},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2017},
+}
+
+@Book{lutkepohl2005new,
+ author = {L{\"u}tkepohl, Helmut},
+ title = {New Introduction to Multiple Time Series Analysis},
+ publisher = {{Springer Science \& Business Media}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2005},
+}
+
+@Article{madrian2001power,
+ author = {Madrian, Brigitte C and Shea, Dennis F},
+ title = {The Power of Suggestion: {{Inertia}} in 401 (k) Participation and Savings Behavior},
+ number = {4},
+ pages = {1149--1187},
+ volume = {116},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The Quarterly journal of economics},
+ year = {2001},
+}
+
+@Book{manning2008introduction,
+ author = {Manning, Christopher D and Sch{\"u}tze, Hinrich and Raghavan, Prabhakar},
+ title = {Introduction to Information Retrieval},
+ publisher = {{Cambridge university press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2008},
+}
+
+@misc{manokhin2022awesome,
+ author = {Manokhin, Valery},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {International journal of forecasting},
- number = {1},
- pages = {35--62},
- title = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art},
- volume = {14}}
-
-@article{zhang2003time,
- author = {Zhang, G Peter},
- date = {2003},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {Neurocomputing},
- pages = {159--175},
- title = {Time Series Forecasting Using a Hybrid {{ARIMA}} and Neural Network Model},
- volume = {50}}
-
-@unpublished{zheng2018dags,
- archiveprefix = {arXiv},
- author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
- date = {2018},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- eprint = {1803.01422},
- eprinttype = {arxiv},
- title = {Dags with No Tears: {{Continuous}} Optimization for Structure Learning}}
+ title = {Awesome Conformal Prediction}}
-@article{zhu2015optimal,
- author = {Zhu, Rong and Ma, Ping and Mahoney, Michael W and Yu, Bin},
- date = {2015},
- date-added = {2022-12-13 12:58:01 +0100},
- date-modified = {2022-12-13 12:58:01 +0100},
- journaltitle = {arXiv},
- pages = {arXiv--1509},
- title = {Optimal Subsampling Approaches for Large Sample Linear Regression}}
+@Article{manski1993identification,
+ author = {Manski, Charles F},
+ title = {Identification of Endogenous Social Effects: {{The}} Reflection Problem},
+ number = {3},
+ pages = {531--542},
+ volume = {60},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The review of economic studies},
+ year = {1993},
+}
+
+@Article{markle2018goals,
+ author = {Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
+ title = {Goals as Reference Points in Marathon Running: {{A}} Novel Test of Reference Dependence},
+ number = {1},
+ pages = {19--50},
+ volume = {56},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Risk and Uncertainty},
+ year = {2018},
+}
+
+@Article{masini2021machine,
+ author = {Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F},
+ title = {Machine Learning Advances for Time Series Forecasting},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Economic Surveys},
+ year = {2021},
+}
+
+@Article{mccracken2016fredmd,
+ author = {McCracken, Michael W and Ng, Serena},
+ title = {{{FRED-MD}}: {{A}} Monthly Database for Macroeconomic Research},
+ number = {4},
+ pages = {574--589},
+ volume = {34},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Business \& Economic Statistics},
+ year = {2016},
+}
+
+@Article{mcculloch1990logical,
+ author = {McCulloch, Warren S and Pitts, Walter},
+ title = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
+ number = {1},
+ pages = {99--115},
+ volume = {52},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Bulletin of mathematical biology},
+ year = {1990},
+}
+
+@Article{migut2015visualizing,
+ author = {Migut, MA and Worring, Marcel and Veenman, Cor J},
+ title = {Visualizing Multi-Dimensional Decision Boundaries in {{2D}}},
+ number = {1},
+ pages = {273--295},
+ volume = {29},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Data Mining and Knowledge Discovery},
+ year = {2015},
+}
+
+@Article{miller2019explanation,
+ author = {Miller, Tim},
+ title = {Explanation in Artificial Intelligence: {{Insights}} from the Social Sciences},
+ pages = {1--38},
+ volume = {267},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Artificial intelligence},
+ year = {2019},
+}
+
+@InProceedings{miller2020strategic,
+ author = {Miller, John and Milli, Smitha and Hardt, Moritz},
+ booktitle = {Proceedings of the 37th {{International Conference}} on {{Machine Learning}}},
+ title = {Strategic {{Classification}} Is {{Causal Modeling}} in {{Disguise}}},
+ eventtitle = {International {{Conference}} on {{Machine Learning}}},
+ pages = {6917--6926},
+ publisher = {{PMLR}},
+ url = {https://proceedings.mlr.press/v119/miller20b.html},
+ urldate = {2022-11-03},
+ abstract = {Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise.},
+ bdsk-url-1 = {https://proceedings.mlr.press/v119/miller20b.html},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ file = {:/Users/FA31DU/Zotero/storage/46I2QMPI/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf:;:/Users/FA31DU/Zotero/storage/NWREET6B/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf:},
+ issn = {2640-3498},
+ langid = {english},
+ month = nov,
+ year = {2020},
+}
+
+@Article{mischel1988nature,
+ author = {Mischel, Walter and Shoda, Yuichi and Peake, Philip K},
+ title = {The Nature of Adolescent Competencies Predicted by Preschool Delay of Gratification.},
+ number = {4},
+ pages = {687},
+ volume = {54},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of personality and social psychology},
+ year = {1988},
+}
+
+@InProceedings{mittelstadt2019explaining,
+ author = {Mittelstadt, Brent and Russell, Chris and Wachter, Sandra},
+ booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
+ title = {Explaining Explanations in {{AI}}},
+ pages = {279--288},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2019},
+}
+
+@Book{molnar2020interpretable,
+ author = {Molnar, Christoph},
+ title = {Interpretable Machine Learning},
+ publisher = {{Lulu. com}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@Book{morgan2015counterfactuals,
+ author = {Morgan, Stephen L and Winship, Christopher},
+ title = {Counterfactuals and Causal Inference},
+ publisher = {{Cambridge University Press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2015},
+}
+
+@Article{mosteller1951experimental,
+ author = {Mosteller, Frederick and Nogee, Philip},
+ title = {An Experimental Measurement of Utility},
+ number = {5},
+ pages = {371--404},
+ volume = {59},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Political Economy},
+ year = {1951},
+}
+
+@InProceedings{mothilal2020explaining,
+ author = {Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
+ booktitle = {Proceedings of the 2020 {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+ title = {Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations},
+ pages = {607--617},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@Book{murphy2012machine,
+ author = {Murphy, Kevin P},
+ title = {Machine Learning: A Probabilistic Perspective},
+ publisher = {{MIT press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2012},
+}
+
+@Book{murphy2012machine,
+ author = {Murphy, Kevin P},
+ title = {Machine Learning: {{A}} Probabilistic Perspective},
+ publisher = {{MIT press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2012},
+}
+
+@Book{murphy2022probabilistic,
+ author = {Murphy, Kevin P},
+ title = {Probabilistic {{Machine Learning}}: {{An}} Introduction},
+ publisher = {{MIT Press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2022},
+}
+
+@Article{nagel1995unraveling,
+ author = {Nagel, Rosemarie},
+ title = {Unraveling in Guessing Games: {{An}} Experimental Study},
+ number = {5},
+ pages = {1313--1326},
+ volume = {85},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {The American Economic Review},
+ year = {1995},
+}
+
+@Unpublished{navarro-martinez2021bridging,
+ author = {Navarro-Martinez, Daniel and Wang, Xinghua},
+ title = {Bridging the Gap between the Lab and the Field: {{Dictator}} Games and Donations},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@InProceedings{nelson2015evaluating,
+ author = {Nelson, Kevin and Corbin, George and Anania, Mark and Kovacs, Matthew and Tobias, Jeremy and Blowers, Misty},
+ booktitle = {2015 {{IEEE Symposium}} on {{Computational Intelligence}} for {{Security}} and {{Defense Applications}} ({{CISDA}})},
+ title = {Evaluating Model Drift in Machine Learning Algorithms},
+ pages = {1--8},
+ publisher = {{IEEE}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2015},
+}
+
+@Book{nocedal2006numerical,
+ author = {Nocedal, Jorge and Wright, Stephen},
+ title = {Numerical Optimization},
+ publisher = {{Springer Science \& Business Media}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2006},
+}
+
+@Misc{oecd2021artificial,
+ author = {{OECD}},
+ title = {Artificial {{Intelligence}}, {{Machine Learning}} and {{Big Data}} in {{Finance}}: {{Opportunities}}, {{Challenges}} and {{Implications}} for {{Policy Makers}}},
+ url = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+ bdsk-url-1 = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@Online{oecdArtificialIntelligenceMachine2021,
+ author = {{OECD}},
+ title = {Artificial {{Intelligence}}, {{Machine Learning}} and {{Big Data}} in {{Finance}}: {{Opportunities}}, {{Challenges}} and {{Implications}} for {{Policy Makers}}},
+ url = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+ bdsk-url-1 = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{OECD}},
+ year = {2021},
+}
+
+@Book{oneil2016weapons,
+ author = {O'Neil, Cathy},
+ title = {Weapons of Math Destruction: {{How}} Big Data Increases Inequality and Threatens Democracy},
+ publisher = {{Crown}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2016},
+}
+
+@Article{pace1997sparse,
+ author = {Pace, R Kelley and Barry, Ronald},
+ title = {Sparse Spatial Autoregressions},
+ number = {3},
+ pages = {291--297},
+ volume = {33},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Statistics \& Probability Letters},
+ year = {1997},
+}
+
+@Unpublished{parr2018matrix,
+ author = {Parr, Terence and Howard, Jeremy},
+ title = {The Matrix Calculus You Need for Deep Learning},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1802.01528},
+ eprinttype = {arxiv},
+ year = {2018},
+}
+
+@Unpublished{pawelczyk2021carla,
+ author = {Pawelczyk, Martin and Bielawski, Sascha and van den Heuvel, Johannes and Richter, Tobias and Kasneci, Gjergji},
+ title = {Carla: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2108.00783},
+ eprinttype = {arxiv},
+ year = {2021},
+}
+
+@Book{pearl2018book,
+ author = {Pearl, Judea and Mackenzie, Dana},
+ title = {The Book of Why: The New Science of Cause and Effect},
+ publisher = {{Basic books}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2018},
+}
+
+@Article{pearl2019seven,
+ author = {Pearl, Judea},
+ title = {The Seven Tools of Causal Inference, with Reflections on Machine Learning},
+ number = {3},
+ pages = {54--60},
+ volume = {62},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Communications of the ACM},
+ year = {2019},
+}
+
+@Article{pedregosa2011scikitlearn,
+ author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
+ title = {Scikit-Learn: {{Machine}} Learning in {{Python}}},
+ pages = {2825--2830},
+ volume = {12},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {the Journal of machine Learning research},
+ year = {2011},
+}
+
+@Book{perry2010economic,
+ author = {Perry, George L and Tobin, James},
+ title = {Economic {{Events}}, {{Ideas}}, and {{Policies}}: The 1960s and After},
+ publisher = {{Brookings Institution Press}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2010},
+}
+
+@Article{pfaff2008var,
+ author = {Pfaff, Bernhard and others},
+ title = {{{VAR}}, {{SVAR}} and {{SVEC}} Models: {{Implementation}} within {{R}} Package Vars},
+ number = {4},
+ pages = {1--32},
+ volume = {27},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Statistical Software},
+ year = {2008},
+}
+
+@Book{pindyck2014microeconomics,
+ author = {Pindyck, Robert S and Rubinfeld, Daniel L},
+ title = {Microeconomics},
+ publisher = {{Pearson Education}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2014},
+}
+
+@Article{pope2011numbers,
+ author = {Pope, Devin and Simonsohn, Uri},
+ title = {Round Numbers as Goals: {{Evidence}} from Baseball, {{SAT}} Takers, and the Lab},
+ number = {1},
+ pages = {71--79},
+ volume = {22},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Psychological science},
+ year = {2011},
+}
+
+@InProceedings{poyiadzi2020face,
+ author = {Poyiadzi, Rafael and Sokol, Kacper and Santos-Rodriguez, Raul and De Bie, Tijl and Flach, Peter},
+ booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+ title = {{{FACE}}: {{Feasible}} and Actionable Counterfactual Explanations},
+ pages = {344--350},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2020},
+}
+
+@Article{qu2015estimating,
+ author = {Qu, Xi and Lee, Lung-fei},
+ title = {Estimating a Spatial Autoregressive Model with an Endogenous Spatial Weight Matrix},
+ number = {2},
+ pages = {209--232},
+ volume = {184},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Journal of Econometrics},
+ year = {2015},
+}
+
+@Article{rabanser2019failing,
+ author = {Rabanser, Stephan and G{\"u}nnemann, Stephan and Lipton, Zachary},
+ title = {Failing Loudly: {{An}} Empirical Study of Methods for Detecting Dataset Shift},
+ volume = {32},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {Advances in Neural Information Processing Systems},
+ year = {2019},
+}
+
+@Unpublished{raghunathan2019adversarial,
+ author = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John C and Liang, Percy},
+ title = {Adversarial Training Can Hurt Generalization},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1906.06032},
+ eprinttype = {arxiv},
+ year = {2019},
+}
+
+@Unpublished{raj2017taming,
+ author = {Raj, Vishnu and Kalyani, Sheetal},
+ title = {Taming Non-Stationary Bandits: {{A Bayesian}} Approach},
+ archiveprefix = {arXiv},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1707.09727},
+ eprinttype = {arxiv},
+ year = {2017},
+}
+
+@InProceedings{rasmussen2003gaussian,
+ author = {Rasmussen, Carl Edward},
+ booktitle = {Summer School on Machine Learning},
+ title = {Gaussian Processes in Machine Learning},
+ pages = {63--71},
+ publisher = {{Springer}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2003},
+}
+
+@InProceedings{ribeiro2016why,
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2016},
+}
+
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+ journal = {Nature Machine Intelligence},
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+
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+ year = {2001},
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+
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+ title = {Least Ambiguous Set-Valued Classifiers with Bounded Error Levels},
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+ pages = {223--234},
+ volume = {114},
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ file = {:/Users/FA31DU/Zotero/storage/YXQ8N76A/Sadinle et al. - 2019 - Least ambiguous set-valued classifiers with bounde.pdf:;:/Users/FA31DU/Zotero/storage/ZHB56F3V/01621459.2017.html:},
+ journal = {Journal of the American Statistical Association},
+ publisher = {{Taylor \& Francis}},
+ year = {2019},
+}
+
+@InProceedings{satopaa2011finding,
+ author = {Satopaa, Ville and Albrecht, Jeannie and Irwin, David and Raghavan, Barath},
+ booktitle = {2011 31st International Conference on Distributed Computing Systems Workshops},
+ title = {Finding a" Kneedle" in a Haystack: {{Detecting}} Knee Points in System Behavior},
+ pages = {166--171},
+ publisher = {{IEEE}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2011},
+}
+
+@InProceedings{schut2021generating,
+ author = {Schut, Lisa and Key, Oscar and Mc Grath, Rory and Costabello, Luca and Sacaleanu, Bogdan and Gal, Yarin and others},
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+ pages = {1756--1764},
+ publisher = {{PMLR}},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ year = {2021},
+}
+
+@Book{schutze2008introduction,
+ author = {Sch{\"u}tze, Hinrich and Manning, Christopher D and Raghavan, Prabhakar},
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+ year = {2008},
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+@InProceedings{slack2020fooling,
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+ year = {2020},
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+
+@Article{slack2021counterfactual,
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+ volume = {34},
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+@Unpublished{spooner2021counterfactual,
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+ title = {Counterfactual {{Explanations}} for {{Arbitrary Regression Models}}},
+ archiveprefix = {arXiv},
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+ eprint = {2106.15212},
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+ year = {2021},
+}
+
+@Article{srivastava2014dropout,
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+ year = {2014},
+}
+
+@Unpublished{stanton2022bayesian,
+ author = {Stanton, Samuel and Maddox, Wesley and Wilson, Andrew Gordon},
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+ archiveprefix = {arXiv},
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+ year = {2022},
+}
+
+@Article{sturm2014simple,
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+}
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+
+@Book{sutton2018reinforcement,
+ author = {Sutton, Richard S and Barto, Andrew G},
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+ publisher = {{MIT press}},
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+ year = {2018},
+}
+
+@Unpublished{szegedy2013intriguing,
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+ title = {Intriguing Properties of Neural Networks},
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+}
+
+@Unpublished{upadhyay2021robust,
+ author = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
+ title = {Towards {{Robust}} and {{Reliable Algorithmic Recourse}}},
+ archiveprefix = {arXiv},
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2102.13620},
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+
+@InProceedings{ustun2019actionable,
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+ year = {2019},
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+ year = {2022},
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+
+@Unpublished{verma2020counterfactual,
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+ year = {2020},
+}
+
+@Article{wachter2017counterfactual,
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+}
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+ journal = {Machine learning},
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+}
+
+@Unpublished{wilson2020case,
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+ eprinttype = {arxiv},
+ year = {2020},
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+}
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+ pages = {2473--2480},
+ volume = {36},
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+ journal = {Expert systems with applications},
+ year = {2009},
+}
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+
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+ journal = {Neurocomputing},
+ year = {2003},
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+
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+ archiveprefix = {arXiv},
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+ eprint = {1803.01422},
+ eprinttype = {arxiv},
+ year = {2018},
+}
+
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+ title = {Optimal Subsampling Approaches for Large Sample Linear Regression},
+ pages = {arXiv--1509},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journal = {arXiv},
+ year = {2015},
+}
@Article{barber2021predictive,
- author = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
- date = {2021-02},
- journaltitle = {The Annals of Statistics},
- title = {Predictive inference with the jackknife+},
- doi = {10.1214/20-AOS1965},
- issn = {0090-5364, 2168-8966},
- number = {1},
- pages = {486--507},
- url = {https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-1/Predictive-inference-with-the-jackknife/10.1214/20-AOS1965.full},
- urldate = {2022-12-13},
- volume = {49},
- abstract = {This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to \$K\$-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk (Ann. Math. Artif. Intell. 74 (2015) 9–28) and we discuss connections.},
- file = {:Barber2021 - Predictive Inference with the Jackknife+.pdf:PDF},
- keywords = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
- publisher = {Institute of Mathematical Statistics},
+ author = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
+ title = {Predictive inference with the jackknife+},
+ doi = {10.1214/20-AOS1965},
+ issn = {0090-5364, 2168-8966},
+ number = {1},
+ pages = {486--507},
+ urldate = {2022-12-13},
+ volume = {49},
+ abstract = {This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to \$K\$-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk (Ann. Math. Artif. Intell. 74 (2015) 9–28) and we discuss connections.},
+ file = {:Barber2021 - Predictive Inference with the Jackknife+.pdf:PDF},
+ journal = {The Annals of Statistics},
+ keywords = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
+ month = feb,
+ publisher = {Institute of Mathematical Statistics},
+ year = {2021},
}
@TechReport{chouldechova2018frontiers,
- author = {Chouldechova, Alexandra and Roth, Aaron},
- date = {2018-10},
- institution = {arXiv},
- title = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
- doi = {10.48550/arXiv.1810.08810},
- note = {arXiv:1810.08810 [cs, stat] type: article},
- url = {http://arxiv.org/abs/1810.08810},
- urldate = {2022-12-23},
- abstract = {The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.},
- file = {:chouldechova2018frontiers - The Frontiers of Fairness in Machine Learning.pdf:PDF},
- keywords = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
+ author = {Chouldechova, Alexandra and Roth, Aaron},
+ title = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
+ doi = {10.48550/arXiv.1810.08810},
+ eprint = {1810.08810},
+ note = {arXiv:1810.08810 [cs, stat] type: article},
+ abstract = {The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.},
+ archiveprefix = {arxiv},
+ file = {:chouldechova2018frontiers - The Frontiers of Fairness in Machine Learning.pdf:PDF},
+ keywords = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
+ month = oct,
+ school = {arXiv},
+ year = {2018},
}
@Article{pawelczyk2022probabilistically,
- author = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
- date = {2022},
- journaltitle = {arXiv preprint arXiv:2203.06768},
- title = {Probabilistically {Robust} {Recourse}: {Navigating} the {Trade}-offs between {Costs} and {Robustness} in {Algorithmic} {Recourse}},
- file = {:pawelczyk2022probabilistically - Probabilistically Robust Recourse_ Navigating the Trade Offs between Costs and Robustness in Algorithmic Recourse.pdf:PDF},
- shorttitle = {Probabilistically {Robust} {Recourse}},
+ author = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
+ title = {Probabilistically {Robust} {Recourse}: {Navigating} the {Trade}-offs between {Costs} and {Robustness} in {Algorithmic} {Recourse}},
+ file = {:pawelczyk2022probabilistically - Probabilistically Robust Recourse_ Navigating the Trade Offs between Costs and Robustness in Algorithmic Recourse.pdf:PDF},
+ journal = {arXiv preprint arXiv:2203.06768},
+ shorttitle = {Probabilistically {Robust} {Recourse}},
+ year = {2022},
}
@InProceedings{stutz2022learning,
author = {Stutz, David and Dvijotham, Krishnamurthy Dj and Cemgil, Ali Taylan and Doucet, Arnaud},
- date = {2022-05},
title = {Learning {Optimal} {Conformal} {Classifiers}},
language = {en},
url = {https://openreview.net/forum?id=t8O-4LKFVx},
urldate = {2023-02-13},
abstract = {Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal prediction (CP) addresses these issues by using the classifier's predictions, e.g., its probability estimates, to predict confidence sets containing the true class with a user-specified probability. However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets. Thus, this paper explores strategies to differentiate through CP during training with the goal of training model with the conformal wrapper end-to-end. In our approach, conformal training (ConfTr), we specifically "simulate" conformalization on mini-batches during training. Compared to standard training, ConfTr reduces the average confidence set size (inefficiency) of state-of-the-art CP methods applied after training. Moreover, it allows to "shape" the confidence sets predicted at test time, which is difficult for standard CP. On experiments with several datasets, we show ConfTr can influence how inefficiency is distributed across classes, or guide the composition of confidence sets in terms of the included classes, while retaining the guarantees offered by CP.},
file = {:stutz2022learning - Learning Optimal Conformal Classifiers.pdf:PDF},
+ month = may,
+ year = {2022},
}
@InProceedings{grathwohl2020your,
author = {Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen, Joern-Henrik and Duvenaud, David and Norouzi, Mohammad and Swersky, Kevin},
- date = {2020-03},
title = {Your classifier is secretly an energy based model and you should treat it like one},
language = {en},
url = {https://openreview.net/forum?id=Hkxzx0NtDB},
urldate = {2023-02-13},
abstract = {We propose to reinterpret a standard discriminative classifier of p(y{\textbar}x) as an energy based model for the joint distribution p(x, y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x{\textbar}y). Within this framework, standard discriminative architectures may be used and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, and out-of-distribution detection while also enabling our models to generate samples rivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and present an approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-art in both generative and discriminative learning within one hybrid model.},
file = {:grathwohl2020your - Your Classifier Is Secretly an Energy Based Model and You Should Treat It like One.pdf:PDF},
+ month = mar,
+ year = {2020},
}
@Comment{jabref-meta: databaseType:biblatex;}
diff --git a/paper/paper.pdf b/paper/paper.pdf
index ab2daf18f2f01715c4626ae2b86b5fa1ca05a614..d352852daabd98f0979366b453ea062989d3ac00 100644
Binary files a/paper/paper.pdf and b/paper/paper.pdf differ
diff --git a/paper/paper.tex b/paper/paper.tex
index 2211b3d1e4aff96d3b4a73c9dcdfad676fc0d548..b6b92a961f095b5b60473c49ab720b8249f9cebc 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -32,6 +32,10 @@
\usepackage{xcolor} % colors
\usepackage{amsmath}
+% Bibliography
+\bibliographystyle{plainnat}
+\setcitestyle{authoryear, round, comma}
+
\title{Conformal Counterfactual Explanations}
@@ -125,6 +129,19 @@ Solutions to Equation~\ref{eq:general} are considered valid as soon as the predi
To properly serve both AI practitioners and individuals affected by AI decision-making systems, Counterfactual Explanations should have certain desirable properties, sometimes referred to as \textit{desiderata}. Besides diversity, which we already introduced above, some of the most prominent desiderata include sparsity, proximity \citep{wachter2017counterfactual}, actionability~\citep{ustun2019actionable}, plausibility \citep{joshi2019realistic,poyiadzi2020face,schut2021generating}, robustness \citep{upadhyay2021robust,pawelczyk2022probabilistically,altmeyer2023endogenous} and causality~\citep{karimi2021algorithmic}. Researchers have come up with various ways to meet these desiderata, which have been surveyed in~\citep{verma2020counterfactual} and~\citep{karimi2020survey}.
+\section*{References}
+
+
+References follow the acknowledgments. Use unnumbered first-level heading for
+the references. Any choice of citation style is acceptable as long as you are
+consistent. It is permissible to reduce the font size to \verb+small+ (9 point)
+when listing the references.
+Note that the Reference section does not count towards the page limit.
+
+\medskip
+
+\bibliography{../bib}
+
\section{Submission of papers to NeurIPS 2022}
@@ -483,21 +500,6 @@ Do {\bf not} include this section in the anonymized submission, only in the fina
\end{ack}
-\section*{References}
-
-
-References follow the acknowledgments. Use unnumbered first-level heading for
-the references. Any choice of citation style is acceptable as long as you are
-consistent. It is permissible to reduce the font size to \verb+small+ (9 point)
-when listing the references.
-Note that the Reference section does not count towards the page limit.
-
-\medskip
-
-\bibliographystyle{plainnat}
-\bibliography{../bib}
-
-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Checklist}