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@Article{chickering2003optimal,
author = {Chickering, David Maxwell},
title = {Optimal structure identification with greedy search},
doi = {10.1162/153244303321897717},
issn = {1532-4435},
number = {null},
pages = {507–554},
url = {https://doi.org/10.1162/153244303321897717},
volume = {3},
abstract = {In this paper we prove the so-called "Meek Conjecture". In particular, we show that if a DAG H is an independence map of another DAG G, then there exists a finite sequence of edge additions and covered edge reversals in G such that (1) after each edge modification H remains an independence map of G and (2) after all modifications G =H. As shown by Meek (1997), this result has an important consequence for Bayesian approaches to learning Bayesian networks from data: in the limit of large sample size, there exists a two-phase greedy search algorithm that---when applied to a particular sparsely-connected search space---provably identifies a perfect map of the generative distribution if that perfect map is a DAG. We provide a new implementation of the search space, using equivalence classes as states, for which all operators used in the greedy search can be scored efficiently using local functions of the nodes in the domain. Finally, using both synthetic and real-world datasets, we demonstrate that the two-phase greedy approach leads to good solutions when learning with finite sample sizes.},
issue_date = {3/1/2003},
journal = {J. Mach. Learn. Res.},
month = {mar},
numpages = {48},
publisher = {JMLR.org},
year = {2003},
}
@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},
title = {Algorithmic Recourse: from Counterfactual Explanations to Interventions},
doi = {10.1145/3442188.3445899},
isbn = {9781450383097},
location = {Virtual Event, Canada},
pages = {353–362},
publisher = {Association for Computing Machinery},
series = {FAccT '21},
url = {https://doi.org/10.1145/3442188.3445899},
abstract = {As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision. Counterfactual explanations -"how the world would have (had) to be different for a desirable outcome to occur"- aim to satisfy these criteria. Existing works have primarily focused on designing algorithms to obtain counterfactual explanations for a wide range of settings. However, it has largely been overlooked that ultimately, one of the main objectives is to allow people to act rather than just understand. In layman's terms, counterfactual explanations inform an individual where they need to get to, but not how to get there. In this work, we rely on causal reasoning to caution against the use of counterfactual explanations as a recommendable set of actions for recourse. Instead, we propose a shift of paradigm from recourse via nearest counterfactual explanations to recourse through minimal interventions, shifting the focus from explanations to interventions.},
address = {New York, NY, USA},
keywords = {algorithmic recourse, causal inference, consequential recommendations, contrastive explanations, counterfactual explanations, explainable artificial intelligence, minimal interventions},
numpages = {10},
year = {2021},
}
@Book{pearl2009book,
author = {Pearl, Judea},
title = {Causality: Models, Reasoning and Inference},
year = {2009},
isbn = {052189560X},
publisher = {Cambridge University Press},
address = {USA},
edition = {2nd},
abstract = {Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 3,000 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.}
}
@TechReport{xu2022conformal,
author = {Xu, Chen and Xie, Yao},
date = {2022-06},
institution = {arXiv},
title = {Conformal prediction set for time-series},
doi = {10.48550/arXiv.2206.07851},
note = {arXiv:2206.07851 [cs, stat] type: article},
url = {http://arxiv.org/abs/2206.07851},
urldate = {2023-07-22},
abstract = {When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to construct prediction sets for time-series (with categorical responses), based on the prior work of [Xu and Xie, 2021]. In particular, we allow unknown dependencies to exist within features and responses that arrive in sequence. Method-wise, ERAPS is a distribution-free and ensemble-based framework that is applicable for arbitrary classifiers. Theoretically, we bound the coverage gap without assuming data exchangeability and show asymptotic set convergence. Empirically, we demonstrate valid marginal and conditional coverage by ERAPS, which also tends to yield smaller prediction sets than competing methods.},
annotation = {Comment: Strongly accepted by the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022},
file = {arXiv Fulltext PDF:https\://arxiv.org/pdf/2206.07851.pdf:application/pdf},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology},
}
@Article{kingma2014adam,
author = {Kingma, Diederik P and Ba, Jimmy},
title = {Adam: A method for stochastic optimization},
journal = {arXiv preprint arXiv:1412.6980},
year = {2014},
}
@Misc{xiao2017fashion,
author = {Han Xiao and Kashif Rasul and Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
eprint = {1708.07747},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
year = {2017},
}
@Online{mw2023fidelity,
author = {Merriam-Webster},
title = {"Fidelity"},
url = {https://www.merriam-webster.com/dictionary/fidelity},
language = {en},
organization = {Merriam-Webster},
urldate = {2023-03-23},
abstract = {the quality or state of being faithful; accuracy in details : exactness; the degree to which an electronic device (such as a record player, radio, or television) accurately reproduces its effect (such as sound or picture)… See the full definition},
}
@InProceedings{altmeyer2023endogenous,
author = {Altmeyer, Patrick and Angela, Giovan and Buszydlik, Aleksander and Dobiczek, Karol and van Deursen, Arie and Liem, Cynthia CS},
booktitle = {2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},
title = {Endogenous Macrodynamics in Algorithmic Recourse},
organization = {IEEE},
pages = {418--431},
year = {2023},
}
%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for Patrick Altmeyer at 2022-12-13 12:58:22 +0100
%% Saved with string encoding Unicode (UTF-8)
@Article{abadie2002instrumental,
author = {Abadie, Alberto and Angrist, Joshua and Imbens, Guido},
title = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
doi = {10.2139/ssrn.195733},
number = {1},
pages = {91--117},
volume = {70},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Econometrica : journal of the Econometric Society},
shortjournal = {Econometrica},
year = {2002},
}
@Article{abadie2003economic,
author = {Abadie, Alberto and Gardeazabal, Javier},
title = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
number = {1},
pages = {113--132},
volume = {93},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {American economic review},
year = {2003},
}
@InProceedings{ackerman2021machine,
author = {Ackerman, Samuel and Dube, Parijat and Farchi, Eitan and Raz, Orna and Zalmanovici, Marcel},
booktitle = {2021 {{IEEE}}/{{ACM Third International Workshop}} on {{Deep Learning}} for {{Testing}} and {{Testing}} for {{Deep Learning}} ({{DeepTest}})},
title = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}},
doi = {10.1109/deeptest52559.2021.00007},
pages = {1--8},
publisher = {{IEEE}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2021},
}
@Article{allen2017referencedependent,
author = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
title = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
doi = {10.3386/w20343},
number = {6},
pages = {1657--1672},
volume = {63},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Management Science},
year = {2017},
}
@Article{altmeyer2018option,
author = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
title = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2018},
}
@Article{altmeyer2021deep,
author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
title = {Deep {{Vector Autoregression}} for {{Macroeconomic Data}}},
url = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
bdsk-url-1 = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2021},
}
@Book{altmeyer2021deepvars,
author = {Altmeyer, Patrick},
title = {Deepvars: {{Deep Vector Autoregession}}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2021},
}
@Misc{altmeyer2022counterfactualexplanations,
author = {Altmeyer, Patrick},
title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2022},
}
@Software{altmeyerCounterfactualExplanationsJlJulia2022,
author = {Altmeyer, Patrick},
title = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
url = {https://github.com/pat-alt/CounterfactualExplanations.jl},
version = {0.1.2},
bdsk-url-1 = {https://github.com/pat-alt/CounterfactualExplanations.jl},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2022},
}
@Misc{angelopoulos2021gentle,
author = {Anastasios N. Angelopoulos and Stephen Bates},
title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification},
eprint = {2107.07511},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
year = {2022},
}
@Misc{angelopoulos2022uncertainty,
author = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
title = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
eprint = {2009.14193},
eprinttype = {arxiv},
abstract = {Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90\%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.},
archiveprefix = {arXiv},
bdsk-url-1 = {http://arxiv.org/abs/2009.14193},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
file = {:/Users/FA31DU/Zotero/storage/5BYIRBR2/Angelopoulos et al. - 2022 - Uncertainty Sets for Image Classifiers using Confo.pdf:;:/Users/FA31DU/Zotero/storage/2QJAKFKV/2009.html:},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning},
month = sep,
number = {arXiv:2009.14193},
primaryclass = {cs, math, stat},
publisher = {{arXiv}},
year = {2022},
}
@Article{angelucci2009indirect,
author = {Angelucci, Manuela and De Giorgi, Giacomo},
title = {Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?},
doi = {10.1257/aer.99.1.486},
number = {1},
pages = {486--508},
volume = {99},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {American economic review},
year = {2009},
}
@Article{angrist1990lifetime,
author = {Angrist, Joshua D},
title = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records},
pages = {313--336},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {The American Economic Review},
year = {1990},
}
@Unpublished{antoran2020getting,
author = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
title = {Getting a Clue: {{A}} Method for Explaining Uncertainty Estimates},
archiveprefix = {arXiv},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
eprint = {2006.06848},
eprinttype = {arxiv},
year = {2020},
}
@Article{arcones1992bootstrap,
author = {Arcones, Miguel A and Gine, Evarist},
title = {On the Bootstrap of {{U}} and {{V}} Statistics},
pages = {655--674},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {The Annals of Statistics},
year = {1992},
}
@Article{ariely2003coherent,
author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
title = {``{{Coherent}} Arbitrariness'': {{Stable}} Demand Curves without Stable Preferences},
doi = {10.1017/cbo9780511618031.014},
number = {1},
pages = {73--106},
volume = {118},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
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