bib.bib

@TechReport{kingma2017adam,
  author      = {Kingma, Diederik P. and Ba, Jimmy},
  date        = {2017-01},
  institution = {arXiv},
  title       = {Adam: {A} {Method} for {Stochastic} {Optimization}},
  doi         = {10.48550/arXiv.1412.6980},
  note        = {arXiv:1412.6980 [cs] type: article},
  url         = {http://arxiv.org/abs/1412.6980},
  urldate     = {2023-05-17},
  abstract    = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
  annotation  = {Comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015},
  file        = {arXiv Fulltext PDF:https\://arxiv.org/pdf/1412.6980.pdf:application/pdf},
  keywords    = {Computer Science - Machine Learning},
  shorttitle  = {Adam},
}

@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},
}

@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}},
  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.
%% https://bibdesk.sourceforge.io/

%% Created for Anonymous Author at 2022-12-13 12:58:22 +0100 


%% 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},
}

@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.},
  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},
  month         = sep,
  number        = {arXiv:2009.14193},
  primaryclass  = {cs, math, stat},
  publisher     = {{arXiv}},
  year          = {2022},
}

@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},