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@TechReport{xu2022conformal,
author = {Xu, Chen and Xie, Yao},
date = {2022-06},
institution = {arXiv},
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},
}
@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 Patrick Altmeyer 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},
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},
}
@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},
}
@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},
year = {2018},
}
@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},
volume = {41},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {ACM computing surveys (CSUR)},
year = {2009},
}
@Article{chapelle2011empirical,
author = {Chapelle, Olivier and Li, Lihong},
title = {An Empirical Evaluation of Thompson Sampling},
pages = {2249--2257},
volume = {24},
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 = {2011},
}
@Article{chetty2011adjustment,
author = {Chetty, Raj and Friedman, John N and Olsen, Tore and Pistaferri, Luigi},
title = {Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: {{Evidence}} from {{Danish}} Tax Records},
number = {2},
pages = {749--804},
volume = {126},
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 = {2011},
}
@Article{cortes1995supportvector,
author = {Cortes, Corinna and Vapnik, Vladimir},
title = {Support-Vector Networks},
number = {3},
pages = {273--297},
volume = {20},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Machine learning},
year = {1995},
}
@Article{crawford2019variable,
author = {Crawford, Lorin and Flaxman, Seth R and Runcie, Daniel E and West, Mike},
title = {Variable Prioritization in Nonlinear Black Box Methods: {{A}} Genetic Association Case Study},
number = {2},
pages = {958},
volume = {13},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {The annals of applied statistics},
year = {2019},
}
@InProceedings{dai2022counterfactual,
author = {Dai, Xinyue and Keane, Mark T and Shalloo, Laurence and Ruelle, Elodie and Byrne, Ruth MJ},
title = {Counterfactual Explanations for Prediction and Diagnosis in Xai},
eventtitle = {Proceedings of the 2022 {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
pages = {215--226},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2022},
}
@Article{danielsson2021artificial,
author = {Danielsson, Jon and Macrae, Robert and Uthemann, Andreas},
title = {Artificial Intelligence and Systemic Risk},
pages = {106290},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Journal of Banking \& Finance},
year = {2021},
}
@Article{daxberger2021laplace,
author = {Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp},
title = {Laplace {{Redux-Effortless Bayesian Deep Learning}}},
volume = {34},
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 = {2021},
}
@Article{dehejia1999causal,
author = {Dehejia, Rajeev H and Wahba, Sadek},
title = {Causal Effects in Nonexperimental Studies: {{Reevaluating}} the Evaluation of Training Programs},
number = {448},
pages = {1053--1062},
volume = {94},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Journal of the American statistical Association},
year = {1999},
}
@Article{dell2010persistent,
author = {Dell, Melissa},
title = {The Persistent Effects of {{Peru}}'s Mining Mita},
number = {6},
pages = {1863--1903},
volume = {78},
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 = {2010},
}
@Article{denhengst2020reinforcement,
author = {den Hengst, Floris and Grua, Eoin Martino and el Hassouni, Ali and Hoogendoorn, Mark},
title = {Reinforcement Learning for Personalization: {{A}} Systematic Literature Review},
issue = {Preprint},
pages = {1--41},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Data Science},
options = {useprefix=true},
year = {2020},
}
@Article{deoliveira2021framework,
author = {de Oliveira, Raphael Mazzine Barbosa and Martens, David},
title = {A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data},
number = {16},
pages = {7274},
volume = {11},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Applied Sciences},
options = {useprefix=true},
year = {2021},
}
@Article{dhurandhar2018explanations,
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},
}
@InProceedings{dombrowski2021diffeomorphic,
author = {Dombrowski, Ann-Kathrin and Gerken, Jan E and Kessel, Pan},
booktitle = {{{ICML Workshop}} on {{Invertible Neural Networks}}, {{Normalizing Flows}}, and {{Explicit Likelihood Models}}},
title = {Diffeomorphic Explanations with Normalizing Flows},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2021},
}
@InProceedings{dorffner1996neural,
author = {Dorffner, Georg},
booktitle = {Neural Network World},
title = {Neural Networks for Time Series Processing},
publisher = {{Citeseer}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {1996},
}
@Article{epstein1979stability,
author = {Epstein, Seymour},
title = {The Stability of Behavior: {{I}}. {{On}} Predicting Most of the People Much of the Time.},
number = {7},
pages = {1097},
volume = {37},
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 = {1979},
}
@Online{barocas2022fairness,
author = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
title = {Fairness and Machine Learning},
url = {https://fairmlbook.org/index.html},
urldate = {2022-11-08},
bdsk-url-1 = {https://fairmlbook.org/index.html},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
month = dec,
year = {2022},
}
@Article{falk2006clean,
author = {Falk, Armin and Ichino, Andrea},
title = {Clean Evidence on Peer Effects},
number = {1},
pages = {39--57},
volume = {24},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Journal of labor economics},
year = {2006},
}
@Unpublished{fan2020interpretability,
author = {Fan, Fenglei and Xiong, Jinjun and Wang, Ge},
title = {On Interpretability of Artificial Neural Networks},
archiveprefix = {arXiv},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
eprint = {2001.02522},
eprinttype = {arxiv},
year = {2020},
}
@Article{fang2011dynamic,
author = {Fang, Hanming and Gavazza, Alessandro},
title = {Dynamic Inefficiencies in an Employment-Based Health Insurance System: {{Theory}} and Evidence},
number = {7},
pages = {3047--77},
volume = {101},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {American Economic Review},
year = {2011},
}
@Article{fehr2000cooperation,
author = {Fehr, Ernst and Gachter, Simon},
title = {Cooperation and Punishment in Public Goods Experiments},
number = {4},
pages = {980--994},
volume = {90},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {American Economic Review},
year = {2000},
}
@Article{fix1951important,
author = {Fix, E and Hodges, J},
title = {An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation},
number = {57},
pages = {233--238},
volume = {3},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {International Statistical Review},
year = {1951},
}
@Book{friedman2008monetary,
author = {Friedman, Milton and Schwartz, Anna Jacobson},
title = {A Monetary History of the {{United States}}, 1867-1960},
publisher = {{Princeton University Press}},
volume = {14},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2008},
}
@InProceedings{gal2016dropout,
author = {Gal, Yarin and Ghahramani, Zoubin},
booktitle = {International Conference on Machine Learning},
title = {Dropout as a Bayesian Approximation: {{Representing}} Model Uncertainty in Deep Learning},
pages = {1050--1059},
publisher = {{PMLR}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2016},
}
@InProceedings{gal2017deep,
author = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
booktitle = {International {{Conference}} on {{Machine Learning}}},
title = {Deep Bayesian Active Learning with Image Data},
pages = {1183--1192},
publisher = {{PMLR}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2017},
}
@Article{galizzi2019external,
author = {Galizzi, Matteo M and Navarro-Martinez, Daniel},
title = {On the External Validity of Social Preference Games: A Systematic Lab-Field Study},
number = {3},
pages = {976--1002},
volume = {65},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Management Science},
year = {2019},
}
@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{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},
}
@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{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},
}
@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},
}
@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{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},
}
@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},
}
@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{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},
}
@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},
}
@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{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},