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We present an extensive study of the key problem of online learning where algorithms are allowed to abstain from making predictions.
An optimum character recognition system using decision function
Chow, C · 1957
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On optimum recognition error and reject trade-off
Chow, C · 1970
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The weighted majority algorithm
Littlestone, N. and Warmuth, M. K · 1994
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The nonstochastic multi-armed bandit problem
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E · 2003
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Nearest-neighbor searching and metric space dimensions
Clarkson, K. L · 2006
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Online learning with prior knowledge
Hazan, E. and Megiddo, N · 2007
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Classification with a reject option using a hinge loss
Bartlett, P. and Wegkamp, M · 2008
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Knows What It Knows: A framework for self-aware learning
Li, L., Littman, M., and Thomas, W · 2008
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CIFAR-10 (Canadian Institute for Advanced Research), 2009
Krizhevsky, A., Nair, V., and Hinton, G · 2009
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On the foundations of noise-free selective classification
El-Yaniv, R. and Wiener, Y · 2010
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Trading off mistakes and don’t-know predictions
Sayedi, A., Zadimoghaddam, M., and Blum, A · 2010
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Agnostic selective classification
El-Yaniv, R. and Wiener, Y · 2011
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From bandits to experts: On the value of side-observations
Mannor, S. and Shamir, O · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Bubeck, S. and Cesa-Bianchi, N · 2012
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Leveraging side observations in stochastic bandits
Caron, S., Kveton, B., Lelarge, M., and Bhagat, S · 2012
Cited alongside, same era.
Efficient learning by implicit exploration in bandit problems with side observations
Kocák, T., Neu, G., Valko, M., and Munos, R · 2014
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Online learning with feedback graphs: Beyond bandits
Alon, N., Cesa-Bianchi, N., Dekel, O., and Koren, T · 2015
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Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Neu, G · 2015
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Online learning with feedback graphs without the graphs
Cohen, A., Hazan, T., and Koren, T · 2016
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Introduction to Online Convex Optimization
Hazan, E · 2016
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The extended Littlestone’s dimension for learning with mistakes and abstentions
Zhang, C. and Chaudhuri, K · 2016
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From bandits to experts: A tale of domination and independence
Alon, N., Cesa-Bianchi, N., Gentile, C., and Mansour, Y · 2013
Cited alongside, same era.
Nonstochastic multi-armed bandits with graph-structured feedback
Alon, N., Cesa-Bianchi, N., Gentile, C., Mannor, S., Mansour, Y., and Shamir, O · 2014
Cited alongside, same era.
Finite-time analysis of the multi-armed bandit problem
Auer, P., Cesa-Bianchi, N., and Fischer, P
Cited in the paper.
The nonstochastic multi-armed bandit problem
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E
Cited in the paper.
Learning with rejection
Cortes, C., DeSalvo, G., and Mohri, M
Cited in the paper.
Boosting with abstention
Cortes, C., DeSalvo, G., and Mohri, M
Cited in the paper.
Algorithmic chaining and the role of partial feedback in online nonparametric learning
Cesa-Bianchi, N., Gaillard, P., Gentile, C., and Gerchinovitz, S · 2017
Closest in time.
Feedback graph regret bounds for thompson sampling and ucb
Lykouris, T., Tardos, E., and Wali, D · 2019
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