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Multi-distribution learning generalizes the classic PAC learning to handle data coming from multiple distributions.
On the uniform convergence of relative frequencies of events to their probabilities
VN Vapnik and A Ya Chervonenkis · 1971
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On the density of families of sets
Norbert Sauer · 1972
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A combinatorial problem; stability and order for models and theories in infinitary languages
Saharon Shelah · 1972
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A theory of the learnable
Leslie G Valiant · 1984
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Learnability and the vapnik-chervonenkis dimension
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth · 1989
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The strength of weak learnability
Robert E Schapire · 1990
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The weighted majority algorithm
Nick Littlestone and Manfred K Warmuth · 1994
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Multitask learning
Rich Caruana · 1997
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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A short introduction to boosting
Yoav Freund, Robert Schapire, and Naoki Abe · 1999
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Agnostic boosting
Shai Ben-David, Philip M Long, and Yishay Mansour · 2001
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Boosting using branching programs
Yishay Mansour and David McAllester · 2002
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Boosting in the presence of noise
Adam Kalai and Rocco A Servedio · 2003
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A new pac bound for intersection-closed concept classes
Peter Auer and Ronald Ortner · 2004
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Faster and simpler algorithms for multicommodity flow and other fractional packing problems
Naveen Garg and Jochen Könemann · 2007
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On agnostic boosting and parity learning
Adam Tauman Kalai, Yishay Mansour, and Elad Verbin · 2008
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Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs
Paul Christiano, Jonathan A Kelner, Aleksander Madry, Daniel A Spielman, and Shang-Hua Teng · 2011
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The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 2012
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Distributed learning, communication complexity and privacy
Maria Florina Balcan, Avrim Blum, Shai Fine, and Yishay Mansour · 2012
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Explaining adaboost
Robert E Schapire · 2013
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Optimal and adaptive algorithms for online boosting
Alina Beygelzimer, Satyen Kale, and Haipeng Luo · 2015
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The optimal sample complexity of pac learning
Steve Hanneke · 2016
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Collaborative pac learning
Avrim Blum, Nika Haghtalab, Ariel D Procaccia, and Mingda Qiao · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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Boosting simple learners
Noga Alon, Alon Gonen, Elad Hazan, and Shay Moran · 2021
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Multiclass boosting and the cost of weak learning
Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, and Robert E Schapire · 2021
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One for one, or all for all: Equilibria and optimality of collaboration in federated learning
Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, and Han Shao · 2021
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Minimax group fairness: Algorithms and experiments
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2021
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Learning models with uniform performance via distributionally robust optimization
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Tight bounds for collaborative pac learning via multiplicative weights
Jiecao Chen, Qin Zhang, and Yuan Zhou · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
Cited alongside, same era.
Improved algorithms for collaborative pac learning
Huy Nguyen and Lydia Zakynthinou · 2018
Cited alongside, same era.
Do outliers ruin collaboration?
Mingda Qiao · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Cited alongside, same era.
John C Duchi and Hongseok Namkoong · 2021
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Multi-group agnostic pac learnability
Guy N Rothblum and Gal Yona · 2021
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Adaptive sampling for minimax fair classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, and Tara Javidi · 2021
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Memory bounds for continual learning
Xi Chen, Christos Papadimitriou, and Binghui Peng · 2022
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On-demand sampling: Learning optimally from multiple distributions
Nika Haghtalab, Michael Jordan, and Eric Zhao · 2022
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Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher J Tosh and Daniel Hsu · 2022
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Optimal pac bounds without uniform convergence
Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, and Nikita Zhivotovskiy · 2023
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Open problem: The sample complexity of multi-distribution learning for vc classes
Pranjal Awasthi, Nika Haghtalab, and Eric Zhao · 2023
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Improper multiclass boosting
Nataly Brukhim, Steve Hanneke, and Shay Moran · 2023
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Bagging is an optimal pac learner
Kasper Green Larsen · 2023
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Near optimal memory-regret tradeoff for online learning
Binghui Peng and Aviad Rubinstein · 2023
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Online prediction in sub-linear space
Binghui Peng and Fred Zhang · 2023
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Optimal multi-distribution learning
Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S Du, and Jason D Lee · 2023
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