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Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules.
Maurice Sion · 1958
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Thoughts on hypothesis boosting
M. Kearns · 1988
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Boosting a weak learning algorithm by majority
Yoav Freund · 1990
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The strength of weak learnability
Robert E. Schapire · 1990
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Game theory, on-line prediction and boosting
Yoav Freund and Robert E Schapire · 1996
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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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Adaptive game playing using multiplicative weights
Yoav Freund and Robert E Schapire · 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 A. McAllester · 2002
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Optimally-smooth adaptive boosting and application to agnostic learning
Dmitry Gavinsky · 2003
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Boosting in the presence of noise
Adam Tauman Kalai and Rocco A. Servedio · 2005
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
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On agnostic boosting and parity learning
Adam Tauman Kalai, Yishay Mansour, and Elad Verbin · 2008
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Adaptive martingale boosting
Philip M. Long and Rocco A. Servedio · 2008
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Distribution-specific agnostic boosting
Vitaly Feldman · 2009
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Potential-based agnostic boosting
Varun Kanade and Adam Kalai · 2009
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An online boosting algorithm with theoretical justifications, 2012
Shang-Tse Chen, Hsuan-Tien Lin, and Chi-Jen Lu · 2012
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Boosting: Foundations and Algorithms
Robert E. Schapire and Yoav Freund · 2012
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Online learning via differential privacy
Jacob D. Abernethy, Chansoo Lee, Audra McMillan, and Ambuj Tewari · 2017
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The price of differential privacy for online learning
Naman Agarwal and Karan Singh · 2017
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Boosting for dynamical systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, and Zhou Lu · 2019
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Online control with adversarial disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, and Karan Singh · 2019
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Logarithmic regret for online control
Naman Agarwal, Elad Hazan, and Karan Singh · 2019
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Private PAC learning implies finite Littlestone dimension
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Online gradient boosting
Alina Beygelzimer, Elad Hazan, Satyen Kale, and Haipeng Luo · 2015
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Optimal and adaptive algorithms for online boosting
Alina Beygelzimer, Satyen Kale, and Haipeng Luo · 2015
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Communication efficient distributed agnostic boosting
Shang-Tse Chen, Maria-Florina Balcan, and Duen Horng Chau · 2016
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Supervised learning through the lens of compression
Ofir David, Shay Moran, and Amir Yehudayoff · 2016
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Passing tests without memorizing: Two models for fooling discriminators, 2019
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The nonstochastic control problem
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The role of interactivity in local differential privacy
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How to use heuristics for differential privacy
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