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@TechReport{xu2022conformal,
author = {Xu, Chen and Xie, Yao},
date = {2022-06},
title = {Conformal prediction set for time-series},
doi = {10.48550/arXiv.2206.07851},
note = {arXiv:2206.07851 [cs, stat] type: article},
url = {http://arxiv.org/abs/2206.07851},
urldate = {2023-07-22},
abstract = {When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to construct prediction sets for time-series (with categorical responses), based on the prior work of [Xu and Xie, 2021]. In particular, we allow unknown dependencies to exist within features and responses that arrive in sequence. Method-wise, ERAPS is a distribution-free and ensemble-based framework that is applicable for arbitrary classifiers. Theoretically, we bound the coverage gap without assuming data exchangeability and show asymptotic set convergence. Empirically, we demonstrate valid marginal and conditional coverage by ERAPS, which also tends to yield smaller prediction sets than competing methods.},
annotation = {Comment: Strongly accepted by the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022},
file = {arXiv Fulltext PDF:https\://arxiv.org/pdf/2206.07851.pdf:application/pdf},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology},
}
@Article{kingma2014adam,
author = {Kingma, Diederik P and Ba, Jimmy},
title = {Adam: A method for stochastic optimization},
journal = {arXiv preprint arXiv:1412.6980},
year = {2014},
@TechReport{xiao2017fashion,
author = {Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
date = {2017-09},
institution = {arXiv},
title = {Fashion-{MNIST}: a {Novel} {Image} {Dataset} for {Benchmarking} {Machine} {Learning} {Algorithms}},
doi = {10.48550/arXiv.1708.07747},
note = {arXiv:1708.07747 [cs, stat] type: article},
url = {http://arxiv.org/abs/1708.07747},
urldate = {2023-05-10},
abstract = {We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist},
annotation = {Comment: Dataset is freely available at https://github.com/zalandoresearch/fashion-mnist Benchmark is available at http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/},
file = {:xiao2017fashion - Fashion MNIST_ a Novel Image Dataset for Benchmarking Machine Learning Algorithms.pdf:PDF},
keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning},
shorttitle = {Fashion-{MNIST}},
}
@Online{mw2023fidelity,
author = {Merriam-Webster},
title = {"Fidelity"},
url = {https://www.merriam-webster.com/dictionary/fidelity},
language = {en},
organization = {Merriam-Webster},
urldate = {2023-03-23},
abstract = {the quality or state of being faithful; accuracy in details : exactness; the degree to which an electronic device (such as a record player, radio, or television) accurately reproduces its effect (such as sound or picture)… See the full definition},
}
author = {Altmeyer, Patrick and Angela, Giovan and Buszydlik, Aleksander and Dobiczek, Karol and van Deursen, Arie and Liem, Cynthia CS},
booktitle = {2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},
title = {Endogenous Macrodynamics in Algorithmic Recourse},
organization = {IEEE},
pages = {418--431},
year = {2023},
}
%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Saved with string encoding Unicode (UTF-8)
@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{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},
}
@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},
}
@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},
}
@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},
}
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@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},
}
@Article{angelopoulos2021gentle,
author = {Angelopoulos, Anastasios N and Bates, Stephen},
title = {A gentle introduction to conformal prediction and distribution-free uncertainty quantification},
journal = {arXiv preprint arXiv:2107.07511},
year = {2021},
@Misc{angelopoulos2022uncertainty,
author = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
title = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
eprint = {2009.14193},
eprinttype = {arxiv},
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:},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning},
number = {arXiv:2009.14193},
primaryclass = {cs, math, stat},
publisher = {{arXiv}},
@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{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},
}
@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},
}
@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{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},
}
@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{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},
}
@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{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},
}
@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},
}
@Article{bechara1997deciding,
author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
title = {Deciding Advantageously before Knowing the Advantageous Strategy},
number = {5304},
pages = {1293--1295},
volume = {275},
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 = {1997},
}
@Book{berlinet2011reproducing,
author = {Berlinet, Alain and Thomas-Agnan, Christine},
title = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics},
publisher = {{Springer Science \& Business Media}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2011},
}
@Misc{bernanke1990federal,
author = {Bernanke, Ben S},
title = {The Federal Funds Rate and the Channels of Monetary Transnission},
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}},
year = {1990},
}
@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},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Advances in neural information processing systems},
year = {2014},
}
@Article{bholat2020impact,
author = {Bholat, D and Gharbawi, M and Thew, O},
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},
}
@Book{bishop2006pattern,
author = {Bishop, Christopher M},
title = {Pattern Recognition and Machine Learning},
publisher = {{springer}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2006},
}
@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},
}
@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},
}
@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},
}
@Article{borisov2022deep,
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},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
publisher = {IEEE},
year = {2022},
@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},
}
@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},
}
@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},
}
@Book{brock1991nonlinear,
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}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {1991},
}
@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},
@Unpublished{bussmann2020neural,
author = {Bussmann, Bart and Nys, Jannes and Latr{\'e}, Steven},
title = {Neural {{Additive Vector Autoregression Models}} for {{Causal Discovery}} in {{Time Series Data}}},
archiveprefix = {arXiv},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
eprint = {2010.09429},
eprinttype = {arxiv},
year = {2020},
}
@Report{card1993minimum,
author = {Card, David and Krueger, Alan B},
title = {Minimum Wages and Employment: {{A}} Case Study of the Fast Food Industry in {{New Jersey}} and {{Pennsylvania}}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
school = {{National Bureau of Economic Research}},
year = {1993},
}
@InProceedings{carlini2017evaluating,
author = {Carlini, Nicholas and Wagner, David},
booktitle = {2017 Ieee Symposium on Security and Privacy (Sp)},
title = {Towards Evaluating the Robustness of Neural Networks},
pages = {39--57},
publisher = {{IEEE}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2017},
}
@Article{carlisle2019racist,
author = {Carlisle, M.},
title = {Racist Data Destruction? - a {{Boston}} Housing Dataset Controversy},
url = {https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8},
bdsk-url-1 = {https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2019},
}
@Article{carrell2009does,
author = {Carrell, Scott E and Fullerton, Richard L and West, James E},
title = {Does Your Cohort Matter? {{Measuring}} Peer Effects in College Achievement},
number = {3},
pages = {439--464},
volume = {27},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Journal of Labor Economics},
year = {2009},
}
@Article{carrell2013natural,
author = {Carrell, Scott E and Sacerdote, Bruce I and West, James E},
title = {From Natural Variation to Optimal Policy? {{The}} Importance of Endogenous Peer Group Formation},
number = {3},
pages = {855--882},
volume = {81},
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 = {2013},
}
@Article{carrizosa2021generating,
author = {Carrizosa, Emilio and Ramırez-Ayerbe, Jasone and Romero, Dolores},
title = {Generating {{Collective Counterfactual Explanations}} in {{Score-Based Classification}} via {{Mathematical Optimization}}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2021},
}
@Article{cascarino2022explainable,
author = {Cascarino, Giuseppe and Moscatelli, Mirko and Parlapiano, Fabio},
title = {Explainable {{Artificial Intelligence}}: Interpreting Default Forecasting Models Based on {{Machine Learning}}},
number = {674},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Bank of Italy Occasional Paper},
year = {2022},
}
@Article{chandola2009anomaly,
author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
title = {Anomaly Detection: {{A}} Survey},
number = {3},
pages = {1--58},
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