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@TechReport{xu2022conformal,
author = {Xu, Chen and Xie, Yao},
date = {2022-06},
institution = {arXiv},
title = {Conformal prediction set for time-series},
doi = {10.48550/arXiv.2206.07851},
note = {arXiv:2206.07851 [cs, stat] type: article},
url = {http://arxiv.org/abs/2206.07851},
urldate = {2023-07-22},
abstract = {When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to construct prediction sets for time-series (with categorical responses), based on the prior work of [Xu and Xie, 2021]. In particular, we allow unknown dependencies to exist within features and responses that arrive in sequence. Method-wise, ERAPS is a distribution-free and ensemble-based framework that is applicable for arbitrary classifiers. Theoretically, we bound the coverage gap without assuming data exchangeability and show asymptotic set convergence. Empirically, we demonstrate valid marginal and conditional coverage by ERAPS, which also tends to yield smaller prediction sets than competing methods.},
annotation = {Comment: Strongly accepted by the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022},
file = {arXiv Fulltext PDF:https\://arxiv.org/pdf/2206.07851.pdf:application/pdf},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology},
}
@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},
journal = {The Quarterly journal of economics},
year = {2003},
}
@Article{ariely2006tom,
author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
title = {Tom {{Sawyer}} and the Construction of Value},
doi = {10.1017/cbo9780511618031.015},
number = {1},
pages = {1--10},
volume = {60},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Journal of Economic Behavior \& Organization},
year = {2006},
}
@Article{arrieta2020explainable,
author = {Arrieta, Alejandro Barredo and Diaz-Rodriguez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garcia, Salvador and Gil-Lopez, Sergio and Molina, Daniel and Benjamins, Richard and others},
title = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
doi = {10.1016/j.inffus.2019.12.012},
pages = {82--115},
volume = {58},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Information Fusion},
year = {2020},
}
@Article{auer2002finitetime,
author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
number = {2},
pages = {235--256},
volume = {47},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Machine learning},
year = {2002},
}
@Article{barabasi2016network,
author = {Barab{\'a}si, Albert-L{\'a}szl{\'o}},
title = {Network {{Science}}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Network Science},
year = {2016},
}
@Unpublished{bastounis2021mathematics,
author = {Bastounis, Alexander and Hansen, Anders C and Vla{\v c}i{\'c}, Verner},
title = {The Mathematics of Adversarial Attacks in {{AI}}--{{Why}} Deep Learning Is Unstable despite the Existence of Stable Neural Networks},
archiveprefix = {arXiv},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
eprint = {2109.06098},
eprinttype = {arxiv},
year = {2021},
}
@Article{bechara1997deciding,
author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
title = {Deciding Advantageously before Knowing the Advantageous Strategy},
doi = {10.7551/mitpress/3077.003.0044},
number = {5304},
pages = {1293--1295},
volume = {275},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
journal = {Science (New York, N.Y.)},
shortjournal = {Science},
year = {1997},
}
@Book{berlinet2011reproducing,
author = {Berlinet, Alain and Thomas-Agnan, Christine},
title = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics},
doi = {10.1007/978-1-4419-9096-9},
publisher = {{Springer Science \& Business Media}},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
year = {2011},
}
@Misc{bernanke1990federal,
author = {Bernanke, Ben S},
title = {The Federal Funds Rate and the Channels of Monetary Transnission},
doi = {10.3386/w3487},
date-added = {2022-12-13 12:58:01 +0100},
date-modified = {2022-12-13 12:58:01 +0100},
publisher = {{National Bureau of Economic Research Cambridge, Mass., USA}},
year = {1990},
}
@Article{besbes2014stochastic,
author = {Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
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