title={{Defence against Adversarial Attacks Using Classical and Quantum-Enhanced Boltzmann Machines}},
author={Kehoe, Aidan and Wittek, Peter and Xue, Yanbo and Pozas-Kerstjens, Alejandro}
}
@misc{GroupDetailedOutbreak,
title={{Detailed Epidemiological Data from the COVID-19 Outbreak}},
author={Group, Open COVID-19 Data Working}
}
@inproceedings{DombrowskiDiffeomorphicFlows,
title={{Diffeomorphic Explanations with Normalizing Flows}},
booktitle={ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models},
author={Dombrowski, Ann-Kathrin and Gerken, Jan E and Kessel, Pan}
}
@unpublished{JeanneretDiffusionExplanations,
title={{Diffusion Models for Counterfactual Explanations}},
author={Jeanneret, Guillaume and Simon, Lo\"\ic and Jurie, Frédéric},
arxivId={2203.15636}
}
@article{RomerDoesSchwartz,
title={{Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz}},
author={Romer, Christina D and Romer, David H},
pages={121--170},
volume={4}
}
@article{CarrellDoesAchievement,
title={{Does Your Cohort Matter? Measuring Peer Effects in College Achievement}},
author={Carrell, Scott E and Fullerton, Richard L and West, James E},
number={3},
pages={439--464},
volume={27}
}
@inproceedings{GalDropoutLearning,
title={{Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning}},
booktitle={International Conference on Machine Learning},
author={Gal, Yarin and Ghahramani, Zoubin},
pages={1050--1059},
publisher={PMLR}
}
@article{SrivastavaDropout:Overfitting,
title={{Dropout: A Simple Way to Prevent Neural Networks from Overfitting}},
author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
number={1},
pages={1929--1958},
volume={15}
}
@article{FangDynamicEvidence,
title={{Dynamic Inefficiencies in an Employment-Based Health Insurance System: Theory and Evidence}},
author={Fang, Hanming and Gavazza, Alessandro},
number={7},
pages={3047--3077},
volume={101}
}
@article{GreeneEconometric71e,
title={{Econometric Analysis, 71e}},
author={Greene, William H}
}
@article{LucasEconometricHolland,
title={{Econometric Policy Evaluation: A Critique `, in K. Brunner and A Meltzer, The Phillips Curve and Labor Markets, North Holland}},
author={Lucas, J R}
}
@book{PerryEconomicAfter,
title={{Economic Events, Ideas, and Policies: The 1960s and After}},
author={Perry, George L and Tobin, James},
publisher={Brookings Institution Press}
}
@article{GretherEconomicPhenomenon,
title={{Economic Theory of Choice and the Preference Reversal Phenomenon}},
author={Grether, David M and Plott, Charles R},
number={4},
pages={623--638},
volume={69}
}
@article{VanBovenEgocentricEffect.,
title={{Egocentric Empathy Gaps between Owners and Buyers: Misperceptions of the Endowment Effect.}},
author={Van Boven, Leaf and Dunning, David and Loewenstein, George},
number={1},
pages={66},
volume={79}
}
@inproceedings{AltmeyerEndogenousRecourse,
title={{Endogenous Macrodynamics in Algorithmic Recourse}},
booktitle={First IEEE Conference on Secure and Trustworthy Machine Learning},
author={Altmeyer, Patrick and Angela, Giovan and Buszydlik, Aleksander and Dobiczek, Karol and van Deursen, Arie and Liem, Cynthia}
}
@article{XuEpidemiologicalInformation,
title={{Epidemiological Data from the COVID-19 Outbreak, Real-Time Case Information}},
author={Xu, Bo and Gutierrez, Bernardo and Mekaru, Sumiko and Sewalk, Kara and Goodwin, Lauren and Loskill, Alyssa and Cohn, Emily and Hswen, Yulin and Hill, Sarah C and Cobo, Maria M and Zarebski, Alexander and Li, Sabrina and Wu, Chieh-Hsi and Hulland, Erin and Morgan, Julia and Wang, Lin and O'Brien, Katelynn and Scarpino, Samuel V and Brownstein, John S and Pybus, Oliver G and Pigott, David M and Kraemer, Moritz U G},
number={106},
volume={7},
doi={doi.org/10.1038/s41597-020-0448-0}
}
@article{QuEstimatingMatrix,
title={{Estimating a Spatial Autoregressive Model with an Endogenous Spatial Weight Matrix}},
author={Qu, Xi and Lee, Lung-fei},
number={2},
pages={209--232},
volume={184}
}
@article{JohnssonEstimationApproach,
title={{Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach}},
author={Johnsson, Ida and Moon, Hyungsik Roger},
number={2},
pages={328--345},
volume={103}
}
@inproceedings{NelsonEvaluatingAlgorithms,
title={{Evaluating Model Drift in Machine Learning Algorithms}},
booktitle={2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)},
author={Nelson, Kevin and Corbin, George and Anania, Mark and Kovacs, Matthew and Tobias, Jeremy and Blowers, Misty},
pages={1--8},
publisher={IEEE}
}
@article{KahnemanExperimentalTheorem,
title={{Experimental Tests of the Endowment Effect and the Coase Theorem}},
author={Kahneman, Daniel and Knetsch, Jack L and Thaler, Richard H},
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}},
pages={82--115},
volume={58}
}
@article{CascarinoExplainableLearning,
title={{Explainable Artificial Intelligence: Interpreting Default Forecasting Models Based on Machine Learning}},
author={Cascarino, Giuseppe and Moscatelli, Mirko and Parlapiano, Fabio},
number={674}
}
@unpublished{GoodfellowExplainingExamples,
title={{Explaining and Harnessing Adversarial Examples}},
author={Goodfellow, Ian J and Shlens, Jonathon and Szegedy, Christian},
arxivId={1412.6572}
}
@inproceedings{MittelstadtExplainingAI,
title={{Explaining Explanations in AI}},
booktitle={Proceedings of the Conference on Fairness, Accountability, and Transparency},
author={Mittelstadt, Brent and Russell, Chris and Wachter, Sandra},
pages={279--288}
}
@inproceedings{MothilalExplainingExplanations,
title={{Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations}},
booktitle={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
author={Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
pages={607--617}
}
@article{MillerExplanationSciences,
title={{Explanation in Artificial Intelligence: Insights from the Social Sciences}},
author={Miller, Tim},
pages={1--38},
volume={267}
}
@article{DhurandharExplanationsNegatives,
title={{Explanations Based on the Missing: Towards Contrastive Explanations with Pertinent Negatives}},
author={Dhurandhar, Amit and Chen, Pin-Yu and Luss, Ronny and Tu, Chun-Chen and Ting, Paishun and Shanmugam, Karthikeyan and Das, Payel},
volume={31}
}
@unpublished{KuiperExploringAuthorities,
title={{Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities}},
author={Kuiper, Ouren and van den Berg, Martin and van den Burgt, Joost and Leijnen, Stefan},
arxivId={2111.02244}
}
@inproceedings{PoyiadziFACE:Explanations,
title={{FACE: Feasible and Actionable Counterfactual Explanations}},
booktitle={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
author={Poyiadzi, Rafael and Sokol, Kacper and Santos-Rodriguez, Raul and De Bie, Tijl and Flach, Peter},
pages={344--350}
}
@article{RabanserFailingShift,
title={{Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift}},
author={Rabanser, Stephan and G{\"{u}}nnemann, Stephan and Lipton, Zachary},
volume={32}
}
@article{JohanssonFailureTask,
title={{Failure to Detect Mismatches between Intention and Outcome in a Simple Decision Task}},
author={Johansson, Petter and Hall, Lars and Sikstr{\"{o}}m, Sverker and Olsson, Andreas},
number={5745},
pages={116--119},
volume={310}
}
@misc{BarocasFairnessLearning,
title={{Fairness and Machine Learning}},
author={Barocas, Solon and Hardt, Moritz and Narayanan, Arvind},
url={https://fairmlbook.org/index.html}
}
@inproceedings{JabbariFairnessLearning,
title={{Fairness in Reinforcement Learning}},
booktitle={International Conference on Machine Learning},
author={Jabbari, Shahin and Joseph, Matthew and Kearns, Michael and Morgenstern, Jamie and Roth, Aaron},
pages={1617--1626},
publisher={PMLR}
}
@unpublished{InnesFashionableFlux,
title={{Fashionable Modelling with Flux}},
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},
arxivId={1811.01457}
}
@inproceedings{SatopaaFindingBehavior,
title={{Finding a" Kneedle" in a Haystack: Detecting Knee Points in System Behavior}},
booktitle={2011 31st International Conference on Distributed Computing Systems Workshops},
author={Satopaa, Ville and Albrecht, Jeannie and Irwin, David and Raghavan, Barath},
pages={166--171},
publisher={IEEE}
}
@article{AuerFinite-TimeProblem,
title={{Finite-Time Analysis of the Multiarmed Bandit Problem}},
author={Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
number={2},
pages={235--256},
volume={47}
}
@article{InnesFlux:Julia,
title={{Flux: Elegant Machine Learning with Julia}},
author={Innes, Mike},
number={25},
pages={602},
volume={3}
}
@inproceedings{SlackFoolingMethods,
title={{Fooling Lime and Shap: Adversarial Attacks on Post Hoc Explanation Methods}},
booktitle={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
author={Slack, Dylan and Hilgard, Sophie and Jia, Emily and Singh, Sameer and Lakkaraju, Himabindu},
pages={180--186}
}
@article{JosephForecastingUp,
title={{Forecasting Uk Inflation Bottom Up}},
author={Joseph, Andreas and Kalamara, Eleni and Kapetanios, George and Potjagailo, Galina}
}
@article{ZhangForecastingArt,
title={{Forecasting with Artificial Neural Networks:: The State of the Art}},
author={Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y},
number={1},
pages={35--62},
volume={14}
}
@article{McCrackenFRED-MD:Research,
title={{FRED-MD: A Monthly Database for Macroeconomic Research}},
author={McCracken, Michael W and Ng, Serena},
number={4},
pages={574--589},
volume={34}
}
@article{CarrellFromFormation,
title={{From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation}},
author={Carrell, Scott E and Sacerdote, Bruce I and West, James E},
number={3},
pages={855--882},
volume={81}
}
@inproceedings{RasmussenGaussianLearning,
title={{Gaussian Processes in Machine Learning}},
booktitle={Summer School on Machine Learning},
author={Rasmussen, Carl Edward},
pages={63--71},
publisher={Springer}
}
@inproceedings{BuolamwiniGenderClassification,
title={{Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification}},
booktitle={Conference on Fairness, Accountability and Transparency},
author={Buolamwini, Joy and Gebru, Timnit},
pages={77--91},
publisher={PMLR}
}
@article{CarrizosaGeneratingOptimization,
title={{Generating Collective Counterfactual Explanations in Score-Based Classification via Mathematical Optimization}},
author={Carrizosa, Emilio and Ramırez-Ayerbe, Jasone and Romero, Dolores}
}
@inproceedings{SchutGeneratingUncertainties,
title={{Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties}},
booktitle={International Conference on Artificial Intelligence and Statistics},
author={Schut, Lisa and Key, Oscar and Mc Grath, Rory and Costabello, Luca and Sacaleanu, Bogdan and Gal, Yarin and {others}},
title={{Getting a Clue: A Method for Explaining Uncertainty Estimates}},
author={Antor{\'{a}}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'{a}}ndez-Lobato, José Miguel},
arxivId={2006.06848}
}
@misc{KaggleGiveYears.,
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.}},
author={{Kaggle}},
publisher={Kaggle},
url={https://www.kaggle.com/c/GiveMeSomeCredit}
}
@misc{CompetitionGiveYears.,
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.}},
author={Competition, Kaggle},
url={https://www.kaggle.com/c/GiveMeSomeCredit}
}
@article{MarkleGoalsDependence,
title={{Goals as Reference Points in Marathon Running: A Novel Test of Reference Dependence}},
author={Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
number={1},
pages={19--50},
volume={56}
}
@unpublished{LachapelleGradient-BasedLearning,
title={{Gradient-Based Neural Dag Learning}},
author={Lachapelle, Sébastien and Brouillard, Philippe and Deleu, Tristan and Lacoste-Julien, Simon},
arxivId={1906.02226}
}
@unpublished{JospinHands-onUsers,
title={{Hands-on Bayesian Neural Networks–a Tutorial for Deep Learning Users}},
author={Jospin, Laurent Valentin and Buntine, Wray and Boussaid, Farid and Laga, Hamid and Bennamoun, Mohammed},
arxivId={2007.06823}
}
@article{UngemachHowDelay,
title={{How Incidental Values from the Environment Affect Decisions about Money, Risk, and Delay}},
author={Ungemach, Christoph and Stewart, Neil and Reimers, Stian},
number={2},
pages={253--260},
volume={22}
}
@article{ManskiIdentificationProblem,
title={{Identification of Endogenous Social Effects: The Reflection Problem}},
author={Manski, Charles F},
number={3},
pages={531--542},
volume={60}
}
@article{BramoulleIdentificationNetworks,
title={{Identification of Peer Effects through Social Networks}},
author={Bramoull{\'{e}}, Yann and Djebbari, Habiba and Fortin, Bernard},
number={1},
pages={41--55},
volume={150}
}
@article{GilbertImmuneForecasting.,
title={{Immune Neglect: A Source of Durability Bias in Affective Forecasting.}},
author={Gilbert, Daniel T and Pinel, Elizabeth C and Wilson, Timothy D and Blumberg, Stephen J and Wheatley, Thalia P},
number={3},
pages={617},
volume={75}
}
@article{HamzacebiImprovingForecasting,
title={{Improving Artificial Neural Networks' Performance in Seasonal Time Series Forecasting}},
author={Hamza{\c{c}}ebi, Coşkun},
number={23},
pages={4550--4559},
volume={178}
}
@unpublished{ImmerImprovingLinearization,
title={{Improving Predictions of Bayesian Neural Networks via Local Linearization}},
author={Immer, Alexander and Korzepa, Maciej and Bauer, Matthias},
arxivId={2008.08400}
}
@article{HershfieldIncreasingSelf,
title={{Increasing Saving Behavior through Age-Progressed Renderings of the Future Self}},
author={Hershfield, Hal E and Goldstein, Daniel G and Sharpe, William F and Fox, Jesse and Yeykelis, Leo and Carstensen, Laura L and Bailenson, Jeremy N},
number={SPL},
pages={S23–S37},
volume={48}
}
@article{AngelucciIndirectConsumption,
title={{Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?}},
author={Angelucci, Manuela and De Giorgi, Giacomo},
number={1},
pages={486--508},
volume={99}
}
@article{AbadieInstrumentalEarnings,
title={{Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings}},
author={Abadie, Alberto and Angrist, Joshua and Imbens, Guido},
number={1},
pages={91--117},
volume={70}
}
@book{MolnarInterpretableLearning,
title={{Interpretable Machine Learning}},
author={Molnar, Christoph},
publisher={Lulu. com}
}
@unpublished{Ish-HorowiczInterpretingImportance,
title={{Interpreting Deep Neural Networks through Variable Importance}},
author={Ish-Horowicz, Jonathan and Udwin, Dana and Flaxman, Seth and Filippi, Sarah and Crawford, Lorin},
arxivId={1901.09839}
}
@inproceedings{KaurInterpretingLearning,
title={{Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning}},
booktitle={Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
author={Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer},
pages={1--14}
}
@unpublished{SzegedyIntriguingNetworks,
title={{Intriguing Properties of Neural Networks}},
author={Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
arxivId={1312.6199}
}
@book{ManningIntroductionRetrieval,
title={{Introduction to Information Retrieval}},
author={Manning, Christopher D and Sch{\"{u}}tze, Hinrich and Raghavan, Prabhakar},
publisher={Cambridge university press}
}
@book{SchutzeIntroductionRetrieval,
title={{Introduction to Information Retrieval}},
author={Sch{\"{u}}tze, Hinrich and Manning, Christopher D and Raghavan, Prabhakar},
volume={39},
publisher={Cambridge University Press Cambridge}
}
@unpublished{LaugelInverseLearning,
title={{Inverse Classification for Comparison-Based Interpretability in Machine Learning}},
author={Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
arxivId={1712.08443}
}
@article{DaxbergerLaplaceLearning,
title={{Laplace Redux-Effortless Bayesian Deep Learning}},
author={Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp},
volume={34}
}
@article{WidmerLearningContexts,
title={{Learning in the Presence of Concept Drift and Hidden Contexts}},
author={Widmer, Gerhard and Kubat, Miroslav},
number={1},
pages={69--101},
volume={23}
}
@inproceedings{StutzLearningClassifiers,
title={{Learning Optimal Conformal Classifiers}},
author={Stutz, David and Dvijotham, Krishnamurthy Dj and Cemgil, Ali Taylan and Doucet, Arnaud},
url={https://openreview.net/forum?id=t8O-4LKFVx},
language={en}
}
@article{SadinleLeastLevels,
title={{Least Ambiguous Set-Valued Classifiers with Bounded Error Levels}},
author={Sadinle, Mauricio and Lei, Jing and Wasserman, Larry},
number={525},
pages={223--234},
volume={114},
publisher={Taylor {\&} Francis}
}
@article{SunsteinLibertarianOxymoron,
title={{Libertarian Paternalism Is Not an Oxymoron}},
author={Sunstein, Cass R and Thaler, Richard H},
pages={1159--1202}
}
@article{AngristLifetimeRecords,
title={{Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records}},
author={Angrist, Joshua D},
pages={313--336}
}
@article{HochreiterLongMemory,
title={{Long Short-Term Memory}},
author={Hochreiter, Sepp and Schmidhuber, Jürgen},
number={8},
pages={1735--1780},
volume={9}
}
@article{MasiniMachineForecasting,
title={{Machine Learning Advances for Time Series Forecasting}},
author={Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F}
}
@inproceedings{AckermanMachineSlices,
title={{Machine Learning Model Drift Detection Via Weak Data Slices}},
booktitle={2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest)},
author={Ackerman, Samuel and Dube, Parijat and Farchi, Eitan and Raz, Orna and Zalmanovici, Marcel},
pages={1--8},
publisher={IEEE}
}
@article{BorchMachineTrading,
title={{Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading}},
author={Borch, Christian},
pages={101852}
}
@book{MurphyMachinePerspective,
title={{Machine Learning: A Probabilistic Perspective}},
author={Murphy, Kevin P},
publisher={MIT press}
}
@article{JacksonMeetingNetworks,
title={{Meeting Strangers and Friends of Friends: How Random Are Social Networks?}},
author={Jackson, Matthew O and Rogers, Brian W},
number={3},
pages={890--915},
volume={97}
}
@book{PindyckMicroeconomics,
title={{Microeconomics}},
author={Pindyck, Robert S and Rubinfeld, Daniel L},
publisher={Pearson Education}
}
@misc{CardMinimumPennsylvania,
title={{Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania}},
author={Card, David and Krueger, Alan B},
institution={National Bureau of Economic Research}
}
@article{BlaomMLJ:Learning,
title={{MLJ: A Julia Package for Composable Machine Learning}},
shorttitle={MLJ},
author={Blaom, Anthony D and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J},
booktitle={Proceedings of 3rd International Conference on Document Analysis and Recognition},
author={Ho, Tin Kam},
pages={278--282},
volume={1},
publisher={IEEE}
}
@article{ShafirReason-BasedChoice,
title={{Reason-Based Choice}},
author={Shafir, Eldar and Simonson, Itamar and Tversky, Amos},
number={1-2},
pages={11--36},
volume={49}
}
@article{KahnemanReferenceFeelings,
title={{Reference Points, Anchors, Norms, and Mixed Feelings}},
author={Kahneman, Daniel},
number={2},
pages={296--312},
volume={51}
}
@article{AllenReference-DependentRunners,
title={{Reference-Dependent Preferences: Evidence from Marathon Runners}},
author={Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
number={6},
pages={1657--1672},
volume={63}
}
@article{denHengstReinforcementReview,
title={{Reinforcement Learning for Personalization: A Systematic Literature Review}},
author={den Hengst, Floris and Grua, Eoin Martino and el Hassouni, Ali and Hoogendoorn, Mark},
number={Preprint},
pages={1--41}
}
@book{SuttonReinforcementIntroduction,
title={{Reinforcement Learning: An Introduction}},
author={Sutton, Richard S and Barto, Andrew G},
publisher={MIT press}
}
@book{BerlinetReproducingStatistics,
title={{Reproducing Kernel Hilbert Spaces in Probability and Statistics}},
author={Berlinet, Alain and Thomas-Agnan, Christine},
publisher={Springer Science {\&} Business Media}
}
@article{HamonRobustnessIntelligence,
title={{Robustness and Explainability of Artificial Intelligence}},
author={Hamon, Ronan and Junklewitz, Henrik and Sanchez, Ignacio}
}
@article{PopeRoundLab,
title={{Round Numbers as Goals: Evidence from Baseball, SAT Takers, and the Lab}},
author={Pope, Devin and Simonsohn, Uri},
number={1},
pages={71--79},
volume={22}
}
@article{ThalerSaveSaving,
title={{Save More Tomorrow: Using Behavioral Economics to Increase Employee Saving}},
author={Thaler, Richard H and Benartzi, Shlomo},
number={S1},
pages={S164–S187},
volume={112}
}
@article{PedregosaScikit-Learn:Python,
title={{Scikit-Learn: Machine Learning in Python}},
author={Pedregosa, Fabian and Varoquaux, Gaël 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}},
pages={2825--2830},
volume={12}
}
@article{KihoroSeasonalModels,
title={{Seasonal Time Series Forecasting: A Comparative Study of ARIMA and ANN Models}},
author={Kihoro, J and Otieno, R O and Wafula, C}
}
@unpublished{LakshminarayananSimpleEnsembles,
title={{Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles}},
author={Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
arxivId={1612.01474}
}
@article{Goldsmith-PinkhamSocialEffects,
title={{Social Networks and the Identification of Peer Effects}},
author={Goldsmith-Pinkham, Paul and Imbens, Guido W},
number={3},
pages={253--264},
volume={31}
}
@article{ThalerSomeInconsistency,
title={{Some Empirical Evidence on Dynamic Inconsistency}},
author={Thaler, Richard},
number={3},
pages={201--207},
volume={8}
}
@article{PaceSparseAutoregressions,
title={{Sparse Spatial Autoregressions}},
author={Pace, R Kelley and Barry, Ronald},
number={3},
pages={291--297},
volume={33}
}
@article{BesbesStochasticRewards,
title={{Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards}},
author={Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
pages={199--207},
volume={27}
}
@article{RudinStopInstead,
title={{Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead}},
author={Rudin, Cynthia},
number={5},
pages={206--215},
volume={1}
}
@inproceedings{MillerStrategicDisguise,
title={{Strategic Classification Is Causal Modeling in Disguise}},
booktitle={Proceedings of the 37th International Conference on Machine Learning},
author={Miller, John and Milli, Smitha and Hardt, Moritz},