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In this work we revisit two classic high-dimensional online learning problems, namely linear regression and contextual bandits, from the perspective of adversarial robustness.
A survey of sampling from contaminated distributions
John W Tukey · 1960
Earlier work this paper cites.
Robust estimation of a location parameter
Peter J Huber · 1964
Earlier work this paper cites.
Probability inequalities for sums of independent random variables
D Kh Fuk and Sergey V Nagaev · 1971
Earlier work this paper cites.
Robust regression: Asymptotics, conjectures and monte carlo
Peter J Huber · 1973
Earlier work this paper cites.
Mathematics and the picturing of data
John W Tukey · 1975
Earlier work this paper cites.
Asymptotic theory of least absolute error regression
Gilbert Bassett Jr and Roger Koenker · 1978
Earlier work this paper cites.
The ellipsoid method and its consequences in combinatorial optimization
M. Grötschel, L. Lovász, and A. Schrijver · 1981
Earlier work this paper cites.
Quadratic optimization problems
N.Z. Shor · 1987
Earlier work this paper cites.
A new algorithm for minimizing convex functions over convex sets
Pravin M Vaidya · 1989
Earlier work this paper cites.
Some dimension-free features of vector-valued martingales
Olav Kallenberg and Rafal Sztencel · 1991
Earlier work this paper cites.
Asymptotics for least absolute deviation regression estimators
David Pollard · 1991
Earlier work this paper cites.
Optimum bounds for the distributions of martingales in banach spaces
Iosif Pinelis · 1994
Earlier work this paper cites.
Estimation of moments of sums of independent real random variables
Rafał Latała et al · 1997
Earlier work this paper cites.
Associative reinforcement learning using linear probabilistic concepts
N. Abe and Philip M. Long · 1999
Earlier work this paper cites.
Squared Functional Systems and Optimization Problems
Yurii Nesterov · 2000
Earlier work this paper cites.
Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization
Pablo A. Parrilo · 2000
Earlier work this paper cites.
Relative loss bounds for on-line density estimation with the exponential family of distributions
Katy S Azoury and Manfred K Warmuth · 2001
Earlier work this paper cites.
New Positive Semidefinite Relaxations for Nonconvex Quadratic Programs
Jean B. Lasserre · 2001
Earlier work this paper cites.
Competitive on-line statistics
Volodya Vovk · 2001
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
Earlier work this paper cites.
Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, Shahar Mendelson, et al · 2005
Earlier work this paper cites.
Robust estimators are hard to compute
Thorsten Bernholt · 2006
Earlier work this paper cites.
Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
Earlier work this paper cites.
Lectures notes for cmsc 35900: Learning theory, 2008
Ambuj Tewari and Sham Kakade · 2008
Earlier work this paper cites.
Introduction to nonparametric estimation
Alexandre B Tsybakov · 2008
Earlier work this paper cites.
Online learning by ellipsoid method
Liu Yang, Rong Jin, and Jieping Ye · 2009
Earlier work this paper cites.
Theoretical statistics: Topics for a core course
Robert W Keener · 2010
Earlier work this paper cites.
Optimistic rates for learning with a smooth loss
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
Earlier work this paper cites.
Online learning and online convex optimization
Shai Shalev-Shwartz et al · 2011
Earlier work this paper cites.
User-friendly tail bounds for matrix martingales
Joel A Tropp · 2011
Earlier work this paper cites.
The best of both worlds: Stochastic and adversarial bandits
Sébastien Bubeck and Aleksandrs Slivkins · 2012
Earlier work this paper cites.
Tail inequalities for sums of random matrices that depend on the intrinsic dimension
Daniel Hsu, Sham Kakade, Tong Zhang, et al · 2012
Earlier work this paper cites.
User-friendly tail bounds for sums of random matrices
Joel A Tropp · 2012
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Bandits with heavy tail
Sébastien Bubeck, Nicolo Cesa-Bianchi, and Gábor Lugosi · 2013
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Algorithms and hardness for robust subspace recovery
Moritz Hardt and Ankur Moitra · 2013
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Rounding sum-of-squares relaxations
Boaz Barak, Jonathan A Kelner, and David Steurer · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2014
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One practical algorithm for both stochastic and adversarial bandits
Yevgeny Seldin and Aleksandrs Slivkins · 2014
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Distribution-independent pac learning of halfspaces with massart noise
Ilias Diakonikolas, Themis Gouleakis, and Christos Tzamos · 2019
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Recent advances in algorithmic high-dimensional robust statistics
Ilias Diakonikolas and Daniel M Kane · 2019
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Robust estimators in high-dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2019
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2019
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Ilias Diakonikolas, Weihao Kong, and Alistair Stewart · 2019
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Stanislav Minsker et al · 2015
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