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Having similar behavior at training time and test time $-$ what we call a "What You See Is What You Get" (WYSIWYG) property $-$ is desirable in machine learning.
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 1911
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Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid
Ron Kohavi et al · 1996
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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When random sampling preserves privacy
Kamalika Chaudhuri and Nina Mishra · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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What can we learn privately
Sofya Raskhodnikova, Adam Smith, Homin K Lee, Kobbi Nissim, and Shiva Prasad Kasiviswanathan · 2008
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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
Ninghui Li, Wahbeh Qardaji, and Dong Su · 2012
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Privacy and statistical risk: Formalisms and minimax bounds
Rina Foygel Barber and John C Duchi · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Lecture notes on information theory
Yury Polyanskiy and Yihong Wu · 2014
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Adding gradient noise improves learning for very deep networks
Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan R. Ullman · 2016
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Adaptive learning with robust generalization guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu · 2016
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C. Duchi · 2016
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Learning with differential privacy: Stability, learnability and the sufficiency and necessity of erm principle
Yu-Xiang Wang, Jing Lei, and Stephen E Fienberg · 2016
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Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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The devil is in the tails: Fine-grained classification in the wild
Grant Van Horn and Pietro Perona · 2017
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Per-instance differential privacy
Yu-Xiang Wang · 2017
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Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu and Maxim Raginsky · 2017
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Age progression/regression by conditional adversarial autoencoder
Model patching: Closing the subgroup performance gap with data augmentation
Karan Goel, Albert Gu, Yixuan Li, and Christopher Re · 2020
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Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms
Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M Roy, and Gintare Karolina Dziugaite · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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Racial disparities in automated speech recognition
Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel · 2020
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Co-designing checklists to understand organizational challenges and opportunities around fairness in ai
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Zhifei Zhang, Yang Song, and Hairong Qi · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach · 2020
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Distributional generalization: A new kind of generalization
Preetum Nakkiran and Yamini Bansal · 2020
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SoK: Chasing accuracy and privacy, and catching both in differentially private histogram publication
Boel Nelson and Jenni Reuben · 2020
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pandas-dev/pandas: Pandas, 2020
The pandas development team · 2020
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Fair decision making using privacy-protected data
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
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Reasoning about generalization via conditional mutual information
Thomas Steinke and Lydia Zakynthinou · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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A closer look at accuracy vs. robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, and Kamalika Chaudhuri · 2020
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Coping with label shift via distributionally robust optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, and Suvrit Sra · 2020
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Statistics of robust optimization: A generalized empirical likelihood approach
John C Duchi, Peter W Glynn, and Hongseok Namkoong · 2021
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Individual privacy accounting via a Renyi filter
Vitaly Feldman and Tijana Zrnic · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Removing disparate impact on model accuracy in differentially private stochastic gradient descent
Depeng Xu, Wei Du, and Xintao Wu · 2021
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Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
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Francesco Croce and Matthias Hein · 2022
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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Disparate vulnerability to membership inference attacks
Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, and Carmela Troncoso · 2022
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How unfair is private learning?
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Understanding robust overfitting of adversarial training and beyond
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