bib.bib

@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}},
  date      = {2023},
  title     = {Endogenous {Macrodynamics} in {Algorithmic} {Recourse}},
  file      = {:altmeyerendogenous - Endogenous Macrodynamics in Algorithmic Recourse.pdf:PDF},
}

%% 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},
	date = {2002},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Econometrica : journal of the Econometric Society},
	number = {1},
	pages = {91--117},
	shortjournal = {Econometrica},
	title = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
	volume = {70}}

@article{abadie2003economic,
	author = {Abadie, Alberto and Gardeazabal, Javier},
	date = {2003},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {American economic review},
	number = {1},
	pages = {113--132},
	title = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
	volume = {93}}

@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}})},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {1--8},
	publisher = {{IEEE}},
	title = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}}}

@article{allen2017referencedependent,
	author = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Management Science},
	number = {6},
	pages = {1657--1672},
	title = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
	volume = {63}}

@article{altmeyer2018option,
	author = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
	date = {2018},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation}}

@article{altmeyer2021deep,
	author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	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}}

@book{altmeyer2021deepvars,
	author = {Altmeyer, Patrick},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Deepvars: {{Deep Vector Autoregession}}}}

@misc{altmeyer2022counterfactualexplanations,
	author = {Altmeyer, Patrick},
	date = {2022},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	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}}

@software{altmeyerCounterfactualExplanationsJlJulia2022,
	author = {Altmeyer, Patrick},
	date = {2022},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	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}}

@unpublished{angelopoulos2021gentle,
	archiveprefix = {arXiv},
	author = {Angelopoulos, Anastasios N. and Bates, Stephen},
	date = {2021},
	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},
	title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification}}

@Misc{angelopoulos2022uncertainty,
  author        = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
  date          = {2022-09-03},
  title         = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
  eprint        = {2009.14193},
  eprinttype    = {arxiv},
  url           = {http://arxiv.org/abs/2009.14193},
  urldate       = {2022-12-07},
  abstract      = {Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90\%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.},
  archiveprefix = {arXiv},
  bdsk-url-1    = {http://arxiv.org/abs/2009.14193},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  file          = {/Users/FA31DU/Zotero/storage/5BYIRBR2/Angelopoulos et al. - 2022 - Uncertainty Sets for Image Classifiers using Confo.pdf;/Users/FA31DU/Zotero/storage/2QJAKFKV/2009.html},
  keywords      = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning},
  number        = {arXiv:2009.14193},
  primaryclass  = {cs, math, stat},
  publisher     = {{arXiv}},
}

@article{angelucci2009indirect,
	author = {Angelucci, Manuela and De Giorgi, Giacomo},
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	volume = {99}}

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	title = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records}}

@unpublished{antoran2020getting,
	archiveprefix = {arXiv},
	author = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
	date = {2020},
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	volume = {60}}

@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},
	date = {2020},
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	title = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
	volume = {58}}

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@unpublished{bastounis2021mathematics,
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	date = {2021},
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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 {{Impact}} of {{Covid}} on {{Machine Learning}} and {{Data Science}} in {{UK Banking}}}}

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@article{blaom2020mlj,
	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},
	author = {Blaom, Anthony D. and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J.},
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	doi = {10.21105/joss.02704},
	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},
	issn = {2475-9066},
	journaltitle = {Journal of Open Source Software},
	langid = {english},
	number = {55},
	pages = {2704},
	shorttitle = {{{MLJ}}},
	title = {{{MLJ}}: {{A Julia}} Package for Composable Machine Learning},
	url = {https://joss.theoj.org/papers/10.21105/joss.02704},
	urldate = {2022-10-27},
	volume = {5},
	bdsk-url-1 = {https://joss.theoj.org/papers/10.21105/joss.02704},
	bdsk-url-2 = {https://doi.org/10.21105/joss.02704}}

@inproceedings{blundell2015weight,
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	title = {Weight Uncertainty in Neural Network}}

@article{borch2022machine,
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	journaltitle = {Technology in Society},
	pages = {101852},
	title = {Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading}}

@unpublished{borisov2021deep,
	archiveprefix = {arXiv},
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	date = {2021},
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	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2110.01889},
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	title = {Deep Neural Networks and Tabular Data: {{A}} Survey}}

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	pages = {41--55},
	title = {Identification of Peer Effects through Social Networks},
	volume = {150}}

@article{bramoulle2020peer,
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	date = {2020},
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	title = {Peer Effects in Networks: {{A}} Survey},
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@unpublished{branco2015survey,
	archiveprefix = {arXiv},
	author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita},
	date = {2015},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
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	title = {A Survey of Predictive Modelling under Imbalanced Distributions}}

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	title = {Gender Shades: {{Intersectional}} Accuracy Disparities in Commercial Gender Classification}}

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	title = {Generating {{Collective Counterfactual Explanations}} in {{Score-Based Classification}} via {{Mathematical Optimization}}}}

@article{cascarino2022explainable,
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	date = {2022},
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	title = {Explainable {{Artificial Intelligence}}: Interpreting Default Forecasting Models Based on {{Machine Learning}}}}

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	title = {Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: {{Evidence}} from {{Danish}} Tax Records},
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	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of Applied Econometrics},
	number = {2},
	pages = {301--319},
	title = {A Social Interactions Model with Endogenous Friendship Formation and Selectivity},
	volume = {31}}

@unpublished{immer2020improving,
	archiveprefix = {arXiv},
	author = {Immer, Alexander and Korzepa, Maciej and Bauer, Matthias},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2008.08400},
	eprinttype = {arxiv},
	title = {Improving Predictions of Bayesian Neural Networks via Local Linearization}}

@unpublished{innes2018fashionable,
	archiveprefix = {arXiv},
	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},
	date = {2018},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1811.01457},
	eprinttype = {arxiv},
	title = {Fashionable Modelling with Flux}}

@article{innes2018flux,
	author = {Innes, Mike},
	date = {2018},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of Open Source Software},
	number = {25},
	pages = {602},
	title = {Flux: {{Elegant}} Machine Learning with {{Julia}}},
	volume = {3}}

@unpublished{ish-horowicz2019interpreting,
	archiveprefix = {arXiv},
	author = {Ish-Horowicz, Jonathan and Udwin, Dana and Flaxman, Seth and Filippi, Sarah and Crawford, Lorin},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1901.09839},
	eprinttype = {arxiv},
	title = {Interpreting Deep Neural Networks through Variable Importance}}

@inproceedings{jabbari2017fairness,
	author = {Jabbari, Shahin and Joseph, Matthew and Kearns, Michael and Morgenstern, Jamie and Roth, Aaron},
	booktitle = {International {{Conference}} on {{Machine Learning}}},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {1617--1626},
	publisher = {{PMLR}},
	title = {Fairness in Reinforcement Learning}}

@article{jackson2007meeting,
	author = {Jackson, Matthew O and Rogers, Brian W},
	date = {2007},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {American Economic Review},
	number = {3},
	pages = {890--915},
	title = {Meeting Strangers and Friends of Friends: {{How}} Random Are Social Networks?},
	volume = {97}}

@unpublished{jeanneret2022diffusion,
	archiveprefix = {arXiv},
	author = {Jeanneret, Guillaume and Simon, Lo{\"\i}c and Jurie, Fr{\'e}d{\'e}ric},
	date = {2022},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2203.15636},
	eprinttype = {arxiv},
	title = {Diffusion {{Models}} for {{Counterfactual Explanations}}}}

@article{johansson2005failure,
	author = {Johansson, Petter and Hall, Lars and Sikstr{\"o}m, Sverker and Olsson, Andreas},
	date = {2005},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Science (New York, N.Y.)},
	number = {5745},
	pages = {116--119},
	shortjournal = {Science},
	title = {Failure to Detect Mismatches between Intention and Outcome in a Simple Decision Task},
	volume = {310}}

@article{johnsson2021estimation,
	author = {Johnsson, Ida and Moon, Hyungsik Roger},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Review of Economics and Statistics},
	number = {2},
	pages = {328--345},
	title = {Estimation of Peer Effects in Endogenous Social Networks: {{Control}} Function Approach},
	volume = {103}}

@article{jolliffe2003modified,
	author = {Jolliffe, Ian T and Trendafilov, Nickolay T and Uddin, Mudassir},
	date = {2003},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of computational and Graphical Statistics},
	number = {3},
	pages = {531--547},
	title = {A Modified Principal Component Technique Based on the {{LASSO}}},
	volume = {12}}

@article{joseph2021forecasting,
	author = {Joseph, Andreas and Kalamara, Eleni and Kapetanios, George and Potjagailo, Galina},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Forecasting Uk Inflation Bottom Up}}

@unpublished{joshi2019realistic,
	archiveprefix = {arXiv},
	author = {Joshi, Shalmali and Koyejo, Oluwasanmi and Vijitbenjaronk, Warut and Kim, Been and Ghosh, Joydeep},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1907.09615},
	eprinttype = {arxiv},
	title = {Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems}}

@unpublished{jospin2020handson,
	archiveprefix = {arXiv},
	author = {Jospin, Laurent Valentin and Buntine, Wray and Boussaid, Farid and Laga, Hamid and Bennamoun, Mohammed},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2007.06823},
	eprinttype = {arxiv},
	title = {Hands-on {{Bayesian Neural Networks}}--a {{Tutorial}} for {{Deep Learning Users}}}}

@misc{kaggle2011give,
	author = {Kaggle},
	date = {2011},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	publisher = {{Kaggle}},
	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.},
	url = {https://www.kaggle.com/c/GiveMeSomeCredit},
	bdsk-url-1 = {https://www.kaggle.com/c/GiveMeSomeCredit}}

@online{kagglecompetitionGiveMeCredit,
	author = {Kaggle Competition},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	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.},
	url = {https://www.kaggle.com/c/GiveMeSomeCredit},
	bdsk-url-1 = {https://www.kaggle.com/c/GiveMeSomeCredit}}

@article{kahneman1979prospect,
	author = {Kahneman, Daniel and Tversky, Amos},
	date = {1979},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Econometrica: Journal of the Econometric Society},
	pages = {263--291},
	title = {Prospect {{Theory}}: {{An Analysis}} of {{Decision}} under {{Risk}}}}

@article{kahneman1990experimental,
	author = {Kahneman, Daniel and Knetsch, Jack L and Thaler, Richard H},
	date = {1990},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of political Economy},
	number = {6},
	pages = {1325--1348},
	title = {Experimental Tests of the Endowment Effect and the {{Coase}} Theorem},
	volume = {98}}

@article{kahneman1992reference,
	author = {Kahneman, Daniel},
	date = {1992},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Organizational behavior and human decision processes},
	number = {2},
	pages = {296--312},
	title = {Reference Points, Anchors, Norms, and Mixed Feelings},
	volume = {51}}

@unpublished{karimi2020algorithmic,
	archiveprefix = {arXiv},
	author = {Karimi, Amir-Hossein and Von K{\"u}gelgen, Julius and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2006.06831},
	eprinttype = {arxiv},
	title = {Algorithmic Recourse under Imperfect Causal Knowledge: A Probabilistic Approach}}

@unpublished{karimi2020survey,
	archiveprefix = {arXiv},
	author = {Karimi, Amir-Hossein and Barthe, Gilles and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2010.04050},
	eprinttype = {arxiv},
	title = {A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects}}

@inproceedings{karimi2021algorithmic,
	author = {Karimi, Amir-Hossein and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
	booktitle = {Proceedings of the 2021 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {353--362},
	title = {Algorithmic Recourse: From Counterfactual Explanations to Interventions}}

@inproceedings{kaur2020interpreting,
	author = {Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer},
	booktitle = {Proceedings of the 2020 {{CHI}} Conference on Human Factors in Computing Systems},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {1--14},
	title = {Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning}}

@article{kehoe2021defence,
	author = {Kehoe, Aidan and Wittek, Peter and Xue, Yanbo and Pozas-Kerstjens, Alejandro},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Machine Learning: Science and Technology},
	title = {Defence against Adversarial Attacks Using Classical and Quantum-Enhanced {{Boltzmann}} Machines}}

@unpublished{kendall2017what,
	archiveprefix = {arXiv},
	author = {Kendall, Alex and Gal, Yarin},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1703.04977},
	eprinttype = {arxiv},
	title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?}}

@article{kihoro2004seasonal,
	author = {Kihoro, J and Otieno, RO and Wafula, C},
	date = {2004},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Seasonal Time Series Forecasting: {{A}} Comparative Study of {{ARIMA}} and {{ANN}} Models}}

@book{kilian2017structural,
	author = {Kilian, Lutz and L{\"u}tkepohl, Helmut},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	publisher = {{Cambridge University Press}},
	title = {Structural Vector Autoregressive Analysis}}

@unpublished{kingma2014adam,
	archiveprefix = {arXiv},
	author = {Kingma, Diederik P and Ba, Jimmy},
	date = {2014},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1412.6980},
	eprinttype = {arxiv},
	title = {Adam: {{A}} Method for Stochastic Optimization}}

@article{kirsch2019batchbald,
	author = {Kirsch, Andreas and Van Amersfoort, Joost and Gal, Yarin},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Advances in neural information processing systems},
	pages = {7026--7037},
	title = {Batchbald: {{Efficient}} and Diverse Batch Acquisition for Deep Bayesian Active Learning},
	volume = {32}}

@unpublished{kuiper2021exploring,
	archiveprefix = {arXiv},
	author = {Kuiper, Ouren and van den Berg, Martin and van den Burgt, Joost and Leijnen, Stefan},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {2111.02244},
	eprinttype = {arxiv},
	title = {Exploring {{Explainable AI}} in the {{Financial Sector}}: {{Perspectives}} of {{Banks}} and {{Supervisory Authorities}}}}

@article{kydland1982time,
	author = {Kydland, Finn E and Prescott, Edward C},
	date = {1982},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Econometrica: Journal of the Econometric Society},
	pages = {1345--1370},
	title = {Time to Build and Aggregate Fluctuations}}

@unpublished{lachapelle2019gradientbased,
	archiveprefix = {arXiv},
	author = {Lachapelle, S{\'e}bastien and Brouillard, Philippe and Deleu, Tristan and Lacoste-Julien, Simon},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1906.02226},
	eprinttype = {arxiv},
	title = {Gradient-Based Neural Dag Learning}}

@inproceedings{lakkaraju2020how,
	author = {Lakkaraju, Himabindu and Bastani, Osbert},
	booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {79--85},
	title = {" {{How}} Do {{I}} Fool You?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}}}

@inproceedings{lakkaraju2020how,
	author = {Lakkaraju, Himabindu and Bastani, Osbert},
	booktitle = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
	date = {2020},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {79--85},
	title = {" {{How Do I Fool You}}?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}}}

@unpublished{lakshminarayanan2016simple,
	archiveprefix = {arXiv},
	author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
	date = {2016},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1612.01474},
	eprinttype = {arxiv},
	title = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles}}

@unpublished{laugel2017inverse,
	archiveprefix = {arXiv},
	author = {Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1712.08443},
	eprinttype = {arxiv},
	shortjournal = {arXiv preprint arXiv:1712.08443},
	title = {Inverse Classification for Comparison-Based Interpretability in Machine Learning}}

@thesis{lawrence2001variational,
	author = {Lawrence, Neil David},
	date = {2001},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	institution = {{University of Cambridge}},
	title = {Variational Inference in Probabilistic Models},
	type = {phdthesis}}

@article{lecun1998mnist,
	author = {LeCun, Yann},
	date = {1998},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	shortjournal = {http://yann. lecun. com/exdb/mnist/},
	title = {The {{MNIST}} Database of Handwritten Digits}}

@article{lee2003best,
	author = {Lee, Lung-fei},
	date = {2003},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Econometric Reviews},
	number = {4},
	pages = {307--335},
	title = {Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances},
	volume = {22}}

@article{lerner2013financial,
	author = {Lerner, Jennifer S and Li, Ye and Weber, Elke U},
	date = {2013},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Psychological science},
	number = {1},
	pages = {72--79},
	title = {The Financial Costs of Sadness},
	volume = {24}}

@article{list2004neoclassical,
	author = {List, John A},
	date = {2004},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Econometrica : journal of the Econometric Society},
	number = {2},
	pages = {615--625},
	shortjournal = {Econometrica},
	title = {Neoclassical Theory versus Prospect Theory: {{Evidence}} from the Marketplace},
	volume = {72}}

@article{lucas1976econometric,
	author = {Lucas, JR},
	date = {1976},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Econometric Policy Evaluation: A Critique `, in {{K}}. {{Brunner}} and {{A Meltzer}}, {{The Phillips}} Curve and Labor Markets, {{North Holland}}}}

@inproceedings{lundberg2017unified,
	author = {Lundberg, Scott M and Lee, Su-In},
	booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
	date = {2017},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	pages = {4768--4777},
	title = {A Unified Approach to Interpreting Model Predictions}}

@book{lutkepohl2005new,
	author = {L{\"u}tkepohl, Helmut},
	date = {2005},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	publisher = {{Springer Science \& Business Media}},
	title = {New Introduction to Multiple Time Series Analysis}}

@article{madrian2001power,
	author = {Madrian, Brigitte C and Shea, Dennis F},
	date = {2001},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {The Quarterly journal of economics},
	number = {4},
	pages = {1149--1187},
	title = {The Power of Suggestion: {{Inertia}} in 401 (k) Participation and Savings Behavior},
	volume = {116}}

@book{manning2008introduction,
	author = {Manning, Christopher D and Sch{\"u}tze, Hinrich and Raghavan, Prabhakar},
	date = {2008},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	publisher = {{Cambridge university press}},
	title = {Introduction to Information Retrieval}}

@misc{manokhin2022awesome,
	author = {Manokhin, Valery},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	title = {Awesome Conformal Prediction}}

@article{manski1993identification,
	author = {Manski, Charles F},
	date = {1993},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {The review of economic studies},
	number = {3},
	pages = {531--542},
	title = {Identification of Endogenous Social Effects: {{The}} Reflection Problem},
	volume = {60}}

@article{markle2018goals,
	author = {Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
	date = {2018},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of Risk and Uncertainty},
	number = {1},
	pages = {19--50},
	title = {Goals as Reference Points in Marathon Running: {{A}} Novel Test of Reference Dependence},
	volume = {56}}

@article{masini2021machine,
	author = {Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of Economic Surveys},
	title = {Machine Learning Advances for Time Series Forecasting}}

@article{mccracken2016fredmd,
	author = {McCracken, Michael W and Ng, Serena},
	date = {2016},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Journal of Business \& Economic Statistics},
	number = {4},
	pages = {574--589},
	title = {{{FRED-MD}}: {{A}} Monthly Database for Macroeconomic Research},
	volume = {34}}

@article{mcculloch1990logical,
	author = {McCulloch, Warren S and Pitts, Walter},
	date = {1990},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Bulletin of mathematical biology},
	number = {1},
	pages = {99--115},
	title = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
	volume = {52}}

@article{migut2015visualizing,
	author = {Migut, MA and Worring, Marcel and Veenman, Cor J},
	date = {2015},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Data Mining and Knowledge Discovery},
	number = {1},
	pages = {273--295},
	title = {Visualizing Multi-Dimensional Decision Boundaries in {{2D}}},
	volume = {29}}

@article{miller2019explanation,
	author = {Miller, Tim},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	journaltitle = {Artificial intelligence},
	pages = {1--38},
	title = {Explanation in Artificial Intelligence: {{Insights}} from the Social Sciences},
	volume = {267}}

@inproceedings{miller2020strategic,
	abstract = {Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise.},
	author = {Miller, John and Milli, Smitha and Hardt, Moritz},
	booktitle = {Proceedings of the 37th {{International Conference}} on {{Machine Learning}}},
	date = {2020-11-21},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eventtitle = {International {{Conference}} on {{Machine Learning}}},
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@unpublished{upadhyay2021robust,
	archiveprefix = {arXiv},
	author = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
	date = {2021},
	date-added = {2022-12-13 12:58:01 +0100},
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	eprinttype = {arxiv},
	title = {Towards {{Robust}} and {{Reliable Algorithmic Recourse}}}}

@inproceedings{ustun2019actionable,
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	booktitle = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
	date = {2019},
	date-added = {2022-12-13 12:58:01 +0100},
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	pages = {10--19},
	title = {Actionable Recourse in Linear Classification}}

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	publisher = {{Independently Published}},
	title = {Trustworthy {{Machine Learning}}}}

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@article{verstyuk2020modeling,
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	title = {Optimal Subsampling for Large Sample Logistic Regression},
	volume = {113}}

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@unpublished{zheng2018dags,
	archiveprefix = {arXiv},
	author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
	date = {2018},
	date-added = {2022-12-13 12:58:01 +0100},
	date-modified = {2022-12-13 12:58:01 +0100},
	eprint = {1803.01422},
	eprinttype = {arxiv},
	title = {Dags with No Tears: {{Continuous}} Optimization for Structure Learning}}

@article{zhu2015optimal,
	author = {Zhu, Rong and Ma, Ping and Mahoney, Michael W and Yu, Bin},
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	pages = {arXiv--1509},
	title = {Optimal Subsampling Approaches for Large Sample Linear Regression}}

@Article{barber2021predictive,
  author       = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
  date         = {2021-02},
  journaltitle = {The Annals of Statistics},
  title        = {Predictive inference with the jackknife+},
  doi          = {10.1214/20-AOS1965},
  issn         = {0090-5364, 2168-8966},
  number       = {1},
  pages        = {486--507},
  url          = {https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-1/Predictive-inference-with-the-jackknife/10.1214/20-AOS1965.full},
  urldate      = {2022-12-13},
  volume       = {49},
  abstract     = {This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to \$K\$-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk (Ann. Math. Artif. Intell. 74 (2015) 9–28) and we discuss connections.},
  file         = {:Barber2021 - Predictive Inference with the Jackknife+.pdf:PDF},
  keywords     = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
  publisher    = {Institute of Mathematical Statistics},
}

@TechReport{chouldechova2018frontiers,
  author      = {Chouldechova, Alexandra and Roth, Aaron},
  date        = {2018-10},
  institution = {arXiv},
  title       = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
  doi         = {10.48550/arXiv.1810.08810},
  note        = {arXiv:1810.08810 [cs, stat] type: article},
  url         = {http://arxiv.org/abs/1810.08810},
  urldate     = {2022-12-23},
  abstract    = {The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.},
  file        = {:chouldechova2018frontiers - The Frontiers of Fairness in Machine Learning.pdf:PDF},
  keywords    = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
}

@Article{pawelczyk2022probabilistically,
  author       = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
  date         = {2022},
  journaltitle = {arXiv preprint arXiv:2203.06768},
  title        = {Probabilistically {Robust} {Recourse}: {Navigating} the {Trade}-offs between {Costs} and {Robustness} in {Algorithmic} {Recourse}},
  file         = {:pawelczyk2022probabilistically - Probabilistically Robust Recourse_ Navigating the Trade Offs between Costs and Robustness in Algorithmic Recourse.pdf:PDF},
  shorttitle   = {Probabilistically {Robust} {Recourse}},
}

@Comment{jabref-meta: databaseType:biblatex;}