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We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions and thus captures temporal effects of learning problems.
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Neri Merhav, Erik Ordentlich, Gadiel Seroussi, and Marcelo J. Weinberger · 2002
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Elad Hazan and C. Seshadhri · 2009
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Sascha Geulen, Berthold Vöcking, and Melanie Winkler · 2010
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Regret bounds for the adaptive control of linear quadratic systems
Yasin Abbasi-Yadkori and Csaba Szepesvári · 2011
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On the universality of online mirror descent
Nati Srebro, Karthik Sridharan, and Ambuj Tewari · 2011
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Mirror descent meets fixed share (and feels no regret)
Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, and Gilles Stoltz · 2012
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Online bandit learning against an adaptive adversary: from regret to policy regret
Ofer Dekel, Ambuj Tewari, and Raman Arora · 2012
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Online Learning and Online Convex Optimization
Shai Shalev-Shwartz · 2012
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Machine Learning in Non-stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Motoaki Kawanabe · 2012
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Zhi-Hua Zhou · 2012
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Online learning with switching costs and other adaptive adversaries
Nicolò Cesa-Bianchi, Ofer Dekel, and Ohad Shamir · 2013
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Oren Anava, Elad Hazan, and Shie Mannor · 2015
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Omar Besbes, Yonatan Gur, and Assaf J. Zeevi · 2015
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Minimax regret of switching-constrained online convex optimization: No phase transition
Lin Chen, Qian Yu, Hannah Lawrence, and Amin Karbasi · 2020
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Parameter-free, dynamic, and strongly-adaptive online learning
Ashok Cutkosky · 2020
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On the sample complexity of the linear quadratic regulator
Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu · 2020
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Logarithmic regret for adversarial online control
Dylan J. Foster and Max Simchowitz · 2020
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Regret-optimal control in dynamic environments
Gautam Goel and Babak Hassibi · 2020
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Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, and Karthik Sridharan · 2015
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Shifting regret, mirror descent, and matrices
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Introduction to Online Convex Optimization
Elad Hazan · 2016
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Online optimization in dynamic environments: Improved regret rates for strongly convex problems
Aryan Mokhtari, Shahin Shahrampour, Ali Jadbabaie, and Alejandro Ribeiro · 2016
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The nonstochastic control problem
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Improper learning for non-stochastic control
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Online learning in changing environments
Lijun Zhang · 2020
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A simple online algorithm for competing with dynamic comparators
Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou · 2020
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Dynamic regret of convex and smooth functions
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