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trying to sort out reference

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......@@ -459,7 +459,7 @@ Note that the Reference section does not count towards the page limit.
\medskip
% \bibliography{references}
\bibliography{bib}
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\section*{Checklist}
......
@inproceedings{LakkarajuExplanations,
title = {{" How Do I Fool You?" Manipulating User Trust via Misleading Black Box Explanations}},
booktitle = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
author = {Lakkaraju, Himabindu and Bastani, Osbert},
pages = {79--85}
}
@inproceedings{RibeiroWhyClassifier,
title = {{"Why Should i Trust You?" Explaining the Predictions of Any Classifier}},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
pages = {1135--1144}
}
@inproceedings{HannekeALearning,
title = {{A Bound on the Label Complexity of Agnostic Active Learning}},
booktitle = {Proceedings of the 24th International Conference on Machine Learning},
author = {Hanneke, Steve},
pages = {353--360}
}
@article{deOliveiraAData,
title = {{A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data}},
author = {de Oliveira, Raphael Mazzine Barbosa and Martens, David},
number = {16},
pages = {7274},
volume = {11}
}
@unpublished{AngelopoulosAQuantification,
title = {{A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification}},
author = {Angelopoulos, Anastasios N and Bates, Stephen},
arxivId = {2107.07511}
}
@article{GrettonATest,
title = {{A Kernel Two-Sample Test}},
author = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"{o}}lkopf, Bernhard and Smola, Alexander},
number = {1},
pages = {723--773},
volume = {13}
}
@article{McCullochAActivity,
title = {{A Logical Calculus of the Ideas Immanent in Nervous Activity}},
author = {McCulloch, Warren S and Pitts, Walter},
number = {1},
pages = {99--115},
volume = {52}
}
@article{JolliffeALASSO,
title = {{A Modified Principal Component Technique Based on the LASSO}},
author = {Jolliffe, Ian T and Trendafilov, Nickolay T and Uddin, Mudassir},
number = {3},
pages = {531--547},
volume = {12}
}
@book{FriedmanA1867-1960,
title = {{A Monetary History of the United States, 1867-1960}},
author = {Friedman, Milton and Schwartz, Anna Jacobson},
volume = {14},
publisher = {Princeton University Press}
}
@article{WittenAAnalysis,
title = {{A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis}},
author = {Witten, Daniela M and Tibshirani, Robert and Hastie, Trevor},
number = {3},
pages = {515--534},
volume = {10}
}
@article{SturmAHorse,
title = {{A Simple Method to Determine If a Music Information Retrieval System Is a ``Horse''}},
author = {Sturm, Bob L},
number = {6},
pages = {1636--1644},
volume = {16}
}
@article{HsiehASelectivity,
title = {{A Social Interactions Model with Endogenous Friendship Formation and Selectivity}},
author = {Hsieh, Chih-Sheng and Lee, Lung Fei},
number = {2},
pages = {301--319},
volume = {31}
}
@unpublished{KarimiAProspects,
title = {{A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects}},
author = {Karimi, Amir-Hossein and Barthe, Gilles and Sch{\"{o}}lkopf, Bernhard and Valera, Isabel},
arxivId = {2010.04050}
}
@unpublished{BrancoADistributions,
title = {{A Survey of Predictive Modelling under Imbalanced Distributions}},
author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita},
arxivId = {1505.01658}
}
@article{GamaAAdaptation,
title = {{A Survey on Concept Drift Adaptation}},
author = {Gama, João and {\v{Z}}liobait{\.{e}}, Indrė and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},
number = {4},
pages = {1--37},
volume = {46}
}
@inproceedings{LundbergAPredictions,
title = {{A Unified Approach to Interpreting Model Predictions}},
booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
author = {Lundberg, Scott M and Lee, Su-In},
pages = {4768--4777}
}
@inproceedings{UstunActionableClassification,
title = {{Actionable Recourse in Linear Classification}},
booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
author = {Ustun, Berk and Spangher, Alexander and Liu, Yang},
pages = {10--19}
}
@unpublished{KingmaAdam:Optimization,
title = {{Adam: A Method for Stochastic Optimization}},
author = {Kingma, Diederik P and Ba, Jimmy},
arxivId = {1412.6980}
}
@article{ChettyAdjustmentRecords,
title = {{Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records}},
author = {Chetty, Raj and Friedman, John N and Olsen, Tore and Pistaferri, Luigi},
number = {2},
pages = {749--804},
volume = {126}
}
@unpublished{RaghunathanAdversarialGeneralization,
title = {{Adversarial Training Can Hurt Generalization}},
author = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John C and Liang, Percy},
arxivId = {1906.06032}
}
@unpublished{KarimiAlgorithmicApproach,
title = {{Algorithmic Recourse under Imperfect Causal Knowledge: A Probabilistic Approach}},
author = {Karimi, Amir-Hossein and Von K{\"{u}}gelgen, Julius and Sch{\"{o}}lkopf, Bernhard and Valera, Isabel},
arxivId = {2006.06831}
}
@inproceedings{KarimiAlgorithmicInterventions,
title = {{Algorithmic Recourse: From Counterfactual Explanations to Interventions}},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
author = {Karimi, Amir-Hossein and Sch{\"{o}}lkopf, Bernhard and Valera, Isabel},
pages = {353--362}
}
@book{WassermanAllStatistics,
title = {{All of Nonparametric Statistics}},
author = {Wasserman, Larry},
publisher = {Springer Science {\&} Business Media}
}
@book{WassermanAllInference,
title = {{All of Statistics: A Concise Course in Statistical Inference}},
author = {Wasserman, Larry},
publisher = {Springer Science {\&} Business Media}
}
@article{HeckmanAlternativeOverview,
title = {{Alternative Methods for Evaluating the Impact of Interventions: An Overview}},
author = {Heckman, James J and Robb Jr, Richard},
number = {1-2},
pages = {239--267},
volume = {30}
}
@article{GrahamAnHeterogeneity,
title = {{An Econometric Model of Network Formation with Degree Heterogeneity}},
author = {Graham, Bryan S},
number = {4},
pages = {1033--1063},
volume = {85}
}
@article{ChapelleAnSampling,
title = {{An Empirical Evaluation of Thompson Sampling}},
author = {Chapelle, Olivier and Li, Lihong},
pages = {2249--2257},
volume = {24}
}
@article{MostellerAnUtility,
title = {{An Experimental Measurement of Utility}},
author = {Mosteller, Frederick and Nogee, Philip},
number = {5},
pages = {371--404},
volume = {59}
}
@article{FixAnEstimation,
title = {{An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation}},
author = {Fix, E and Hodges, J},
number = {57},
pages = {233--238},
volume = {3}
}
@article{ChandolaAnomalySurvey,
title = {{Anomaly Detection: A Survey}},
author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
number = {3},
pages = {1--58},
volume = {41}
}
@article{SimsAreAnalysis,
title = {{Are Forecasting Models Usable for Policy Analysis?}},
author = {Sims, Christopher A and {others}},
number = {Win},
pages = {2--16},
volume = {10}
}
@article{DanielssonArtificialRisk,
title = {{Artificial Intelligence and Systemic Risk}},
author = {Danielsson, Jon and Macrae, Robert and Uthemann, Andreas},
pages = {106290}
}
@misc{OECDArtificialMakers,
title = {{Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges and Implications for Policy Makers}},
author = {{OECD}},
url = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf}
}
@misc{OECDArtificialMakersb,
title = {{Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges and Implications for Policy Makers}},
author = {{OECD}},
publisher = {OECD},
url = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf}
}
@misc{ManokhinAwesomePrediction,
title = {{Awesome Conformal Prediction}},
author = {Manokhin, Valery}
}
@article{KirschBatchbald:Learning,
title = {{Batchbald: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning}},
author = {Kirsch, Andreas and Van Amersfoort, Joost and Gal, Yarin},
pages = {7026--7037},
volume = {32}
}
@unpublished{HoffBayes-OptimalControl,
title = {{Bayes-Optimal Prediction with Frequentist Coverage Control}},
author = {Hoff, Peter},
arxivId = {2105.14045}
}
@unpublished{HoulsbyBayesianLearning,
title = {{Bayesian Active Learning for Classification and Preference Learning}},
author = {Houlsby, Neil and Husz{\'{a}}r, Ferenc and Ghahramani, Zoubin and Lengyel, Máté},
arxivId = {1112.5745}
}
@book{GelmanBayesianAnalysis,
title = {{Bayesian Data Analysis}},
author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
publisher = {CRC press}
}
@incollection{GoanBayesianSurvey,
title = {{Bayesian Neural Networks: An Introduction and Survey}},
booktitle = {Case Studies in Applied Bayesian Data Science},
author = {Goan, Ethan and Fookes, Clinton},
pages = {45--87},
publisher = {Springer}
}
@unpublished{StantonBayesianGuarantees,
title = {{Bayesian Optimization with Conformal Coverage Guarantees}},
author = {Stanton, Samuel and Maddox, Wesley and Wilson, Andrew Gordon},
arxivId = {2210.12496}
}
@article{LeeBestDisturbances,
title = {{Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances}},
author = {Lee, Lung-fei},
number = {4},
pages = {307--335},
volume = {22}
}
@unpublished{Navarro-MartinezBridgingDonations,
title = {{Bridging the Gap between the Lab and the Field: Dictator Games and Donations}},
author = {Navarro-Martinez, Daniel and Wang, Xinghua}
}
@unpublished{PawelczykCarla:Algorithms,
title = {{Carla: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms}},
author = {Pawelczyk, Martin and Bielawski, Sascha and van den Heuvel, Johannes and Richter, Tobias and Kasneci, Gjergji},
arxivId = {2108.00783}
}
@article{DehejiaCausalPrograms,
title = {{Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs}},
author = {Dehejia, Rajeev H and Wahba, Sadek},
number = {448},
pages = {1053--1062},
volume = {94}
}
@article{SimonsonChoiceEffects,
title = {{Choice Based on Reasons: The Case of Attraction and Compromise Effects}},
author = {Simonson, Itamar},
number = {2},
pages = {158--174},
volume = {16}
}
@article{FalkCleanEffects,
title = {{Clean Evidence on Peer Effects}},
author = {Falk, Armin and Ichino, Andrea},
number = {1},
pages = {39--57},
volume = {24}
}
@article{FehrCooperationExperiments,
title = {{Cooperation and Punishment in Public Goods Experiments}},
author = {Fehr, Ernst and Gachter, Simon},
number = {4},
pages = {980--994},
volume = {90}
}
@article{SlackCounterfactualManipulated,
title = {{Counterfactual Explanations Can Be Manipulated}},
author = {Slack, Dylan and Hilgard, Anna and Lakkaraju, Himabindu and Singh, Sameer},
volume = {34}
}
@unpublished{SpoonerCounterfactualModels,
title = {{Counterfactual Explanations for Arbitrary Regression Models}},
author = {Spooner, Thomas and Dervovic, Danial and Long, Jason and Shepard, Jon and Chen, Jiahao and Magazzeni, Daniele},
arxivId = {2106.15212}
}
@unpublished{VermaCounterfactualReview,
title = {{Counterfactual Explanations for Machine Learning: A Review}},
author = {Verma, Sahil and Dickerson, John and Hines, Keegan},
arxivId = {2010.10596}
}
@inproceedings{DaiCounterfactualXai,
title = {{Counterfactual Explanations for Prediction and Diagnosis in Xai}},
author = {Dai, Xinyue and Keane, Mark T and Shalloo, Laurence and Ruelle, Elodie and Byrne, Ruth M J},
pages = {215--226}
}
@article{WachterCounterfactualGDPR,
title = {{Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR}},
author = {Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
pages = {841},
volume = {31}
}
@misc{AltmeyerCounterfactualExplanations.JlRecourse,
title = {{CounterfactualExplanations.Jl - a Julia Package for Counterfactual Explanations and Algorithmic Recourse}},
author = {Altmeyer, Patrick},
url = {https://github.com/pat-alt/CounterfactualExplanations.jl}
}
@misc{AltmeyerCounterfactualExplanations.JlRecourseb,
title = {{CounterfactualExplanations.Jl - a Julia Package for Counterfactual Explanations and Algorithmic Recourse}},
author = {Altmeyer, Patrick},
url = {https://github.com/pat-alt/CounterfactualExplanations.jl}
}
@book{MorganCounterfactualsInference,
title = {{Counterfactuals and Causal Inference}},
author = {Morgan, Stephen L and Winship, Christopher},
publisher = {Cambridge University Press}
}
@unpublished{ZhengDagsLearning,
title = {{Dags with No Tears: Continuous Optimization for Structure Learning}},
author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
arxivId = {1803.01422}
}
@article{BecharaDecidingStrategy,
title = {{Deciding Advantageously before Knowing the Advantageous Strategy}},
author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
number = {5304},
pages = {1293--1295},
volume = {275}
}
@inproceedings{GalDeepData,
title = {{Deep Bayesian Active Learning with Image Data}},
booktitle = {International Conference on Machine Learning},
author = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
pages = {1183--1192},
publisher = {PMLR}
}
@book{GoodfellowDeepLearning,
title = {{Deep Learning}},
author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
publisher = {MIT Press}
}
@unpublished{BorisovDeepSurvey,
title = {{Deep Neural Networks and Tabular Data: A Survey}},
author = {Borisov, Vadim and Leemann, Tobias and Se{\ss}ler, Kathrin and Haug, Johannes and Pawelczyk, Martin and Kasneci, Gjergji},
arxivId = {2110.01889}
}
@article{AltmeyerDeepData,
title = {{Deep Vector Autoregression for Macroeconomic Data}},
author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
url = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf}
}
@book{AltmeyerDeepvars:Autoregession,
title = {{Deepvars: Deep Vector Autoregession}},
author = {Altmeyer, Patrick}
}
@article{KehoeDefenceMachines,
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}
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@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},
number = {6},
pages = {1325--1348},
volume = {98}
}
@article{ArrietaExplainableAI,
title = {{Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI}},
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}
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@article{AuerFinite-TimeProblem,
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title = {{German Credit Data}},
author = {Hoffman, Hans},
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title = {{German Credit Data}},
author = {Hoffman, Hans},
url = {https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)}
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title = {{Give Me Some Credit, Improve on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the next Two Years.}},
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url = {https://www.kaggle.com/c/GiveMeSomeCredit}
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@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},
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title = {{Hands-on Bayesian Neural Networks–a Tutorial for Deep Learning Users}},
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title = {{Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning}},
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title = {{Intriguing Properties of Neural Networks}},
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title = {{Inverse Classification for Comparison-Based Interpretability in Machine Learning}},
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language = {en}
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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)},
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publisher = {IEEE}
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title = {{Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading}},
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@misc{CardMinimumPennsylvania,
title = {{Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania}},
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title = {{Modeling Multivariate Time Series in Economics: From Auto-Regressions to Recurrent Neural Networks}},
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title = {{Multi-Armed Bandit, Dynamic Environments and Meta-Bandits}},
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doi = {10.1214/20-AOS1965},
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@book{BerlinetReproducingStatistics,
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title = {{Robustness and Explainability of Artificial Intelligence}},
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title = {{Round Numbers as Goals: Evidence from Baseball, SAT Takers, and the Lab}},
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title = {{Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles}},
author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
arxivId = {1612.01474}
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title = {{Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards}},
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title = {{Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead}},
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title = {{Strategic Classification Is Causal Modeling in Disguise}},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
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publisher = {PMLR},
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issn = {2640-3498}
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title = {{Structural Vector Autoregressive Analysis}},
author = {Kilian, Lutz and L{\"{u}}tkepohl, Helmut},
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title = {{Support-Vector Networks}},
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title = {{Taming Non-Stationary Bandits: A Bayesian Approach}},
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arxivId = {1707.09727}
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title = {{The Book of Why: The New Science of Cause and Effect}},
author = {Pearl, Judea and Mackenzie, Dana},
publisher = {Basic books}
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title = {{The Case for Bayesian Deep Learning}},
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@article{YehTheClients,
title = {{The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients}},
author = {Yeh, I-Cheng and Lien, Che-hui},
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@article{AbadieTheCountry,
title = {{The Economic Costs of Conflict: A Case Study of the Basque Country}},
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title = {{The Evaluability Hypothesis: An Explanation for Preference Reversals between Joint and Separate Evaluations of Alternatives}},
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@misc{BernankeTheTransnission,
title = {{The Federal Funds Rate and the Channels of Monetary Transnission}},
author = {Bernanke, Ben S},
publisher = {National Bureau of Economic Research Cambridge, Mass., USA}
}
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title = {{The Financial Costs of Sadness}},
author = {Lerner, Jennifer S and Li, Ye and Weber, Elke U},
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volume = {24}
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title = {{The Framing of Decisions and the Psychology of Choice}},
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title = {{The Frontiers of Fairness in Machine Learning}},
author = {Chouldechova, Alexandra and Roth, Aaron},
url = {http://arxiv.org/abs/1810.08810},
institution = {arXiv},
doi = {10.48550/arXiv.1810.08810},
keywords = {Computer Science - Computer Science and Game Theory, Computer Science - Data Structures and Algorithms, Computer Science - Machine Learning, Statistics - Machine Learning}
}
@article{BholatTheBanking,
title = {{The Impact of Covid on Machine Learning and Data Science in UK Banking}},
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title = {{The Incredible Volcker Disinflation}},
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title = {{The Mathematics of Adversarial Attacks in AI–Why Deep Learning Is Unstable despite the Existence of Stable Neural Networks}},
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title = {{The Matrix Calculus You Need for Deep Learning}},
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title = {{Thompson Sampling for Dynamic Multi-Armed Bandits}},
booktitle = {2011 10th International Conference on Machine Learning and Applications and Workshops},
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@book{HamiltonTimeAnalysis,
title = {{Time Series Analysis}},
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publisher = {Princeton university press}
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title = {{Time to Build and Aggregate Fluctuations}},
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title = {{Towards Evaluating the Robustness of Neural Networks}},
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title = {{Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems}},
author = {Joshi, Shalmali and Koyejo, Oluwasanmi and Vijitbenjaronk, Warut and Kim, Been and Ghosh, Joydeep},
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title = {{Towards Robust and Reliable Algorithmic Recourse}},
author = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
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@misc{AngelopoulosUncertaintyPrediction,
title = {{Uncertainty Sets for Image Classifiers Using Conformal Prediction}},
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publisher = {arXiv},
url = {http://arxiv.org/abs/2009.14193},
arxivId = {2009.14193},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning}
}
@article{NagelUnravelingStudy,
title = {{Unraveling in Guessing Games: An Experimental Study}},
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title = {{Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study}},
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@misc{LawrenceVariationalModels,
title = {{Variational Inference in Probabilistic Models}},
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institution = {University of Cambridge}
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title = {{Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy}},
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@inproceedings{BlundellWeightNetwork,
title = {{Weight Uncertainty in Neural Network}},
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@unpublished{KendallWhatVision,
title = {{What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?}},
author = {Kendall, Alex and Gal, Yarin},
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title = {{Who Accepts Savage's Axiom?}},
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@unpublished{GrinsztajnWhyData,
title = {{Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?}},
author = {Grinsztajn, Léo and Oyallon, Edouard and Varoquaux, Gaël},
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@inproceedings{GrathwohlYourOne,
title = {{Your classifier is secretly an energy based model and you should treat it like one}},
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url = {https://openreview.net/forum?id=Hkxzx0NtDB},
language = {en}
}
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title = {{``Coherent Arbitrariness'': Stable Demand Curves without Stable Preferences}},
author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
number = {1},
pages = {73--106},
volume = {118}
}
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