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We study the task of online learning in the presence of Massart noise.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
Asymptotically subminimax solutions of compound statistical decision problems
H. Robbins · 1951
Earlier work this paper cites.
Controlled random walks
D. Blackwell et al · 1954
Earlier work this paper cites.
Approximation to bayes risk in repeated play
J. Hannan · 1957
Earlier work this paper cites.
The Perceptron: a probabilistic model for information storage and organization in the brain
F. Rosenblatt · 1958
Earlier work this paper cites.
On convergence proofs on perceptrons
A. Novikoff · 1962
Earlier work this paper cites.
Bandit problems: sequential allocation of experiments (monographs on statistics and applied probability)
Donald A Berry and Bert Fristedt · 1985
Earlier work this paper cites.
Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm
N. Littlestone · 1988
Earlier work this paper cites.
Mistake bounds and logarithmic linear-threshold learning algorithms
N. Littlestone · 1989
Earlier work this paper cites.
Learning Boolean functions in an infinite attribute space
A. Blum · 1990
Earlier work this paper cites.
Decision theoretic generalizations of the PAC model for neural net and other learning applications
D. Haussler · 1992
Earlier work this paper cites.
The weighted majority algorithm
N. Littlestone and M. Warmuth · 1994
Earlier work this paper cites.
How fast can a threshold gate learn?
W. Maass and G. Turan · 1994
Earlier work this paper cites.
Worst-case analysis of the Perceptron and exponentiated update algorithms
T. Bylander · 1998
Earlier work this paper cites.
Optimal mistake bound learning is hard
Moti Frances and Ami Litman · 1998
Cited alongside, same era.
Prediction, learning, and games
N. Cesa-Bianchi and G. Lugosi · 2006
Cited alongside, same era.
Hardness of learning halfspaces with noise
V. Guruswami and P. Raghavendra · 2006
Cited alongside, same era.
Risk bounds for statistical learning
P. Massart and E. Nedelec · 2006
Cited alongside, same era.
The epoch-greedy algorithm for multi-armed bandits with side information
John Langford and Tong Zhang · 2007
Cited alongside, same era.
Agnostic online learning
Shai Ben-David, Dávid Pál, and Shai Shalev-Shwartz · 2009
Cited alongside, same era.
Parametric bandits: The generalized linear case
Provably optimal algorithms for generalized linear contextual bandits
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Later among the works it cites.
Practical contextual bandits with regression oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert Schapire · 2018
Later among the works it cites.
Distribution-independent pac learning of halfspaces with massart noise
I. Diakonikolas, T. Gouleakis, and C. Tzamos · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
Later among the works it cites.
Classification under misspecification: Halfspaces, generalized linear models, and connections to evolvability
S. Chen, F. Koehler, A. Moitra, and M. Yau · 2020
Later among the works it cites.
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Contextual bandits with linear payoff functions
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Efficient optimal learning for contextual bandits
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Contextual bandit learning with predictable rewards
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Learning halfspaces with tsybakov noise
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Agnostic proper learning of halfspaces under gaussian marginals
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Efficiently learning halfspaces with tsybakov noise
I. Diakonikolas, D. M. Kane, V. Kontonis, C. Tzamos, and N. Zarifis · 2021
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Sq lower bounds for learning single neurons with massart noise
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Linear label ranking with bounded noise
Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, and Christos Tzamos · 2022
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Optimal SQ lower bounds for learning halfspaces with massart noise
R. Nasser and S. Tiegel · 2022
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