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We study the problem of transfer-learning in the setting of stochastic linear bandit tasks.
A one-armed bandit problem with a concomitant variable
Michael Woodroofe · 1979
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Improved algorithms for linear stochastic bandits
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Statistics for high-dimensional data: methods, theory and applications
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École D’Été de Probabilités de Saint-Flour XXXVIII-2008
V. Koltchinskii · 2011
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Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
Vladimir Koltchinskii, Karim Lounici, and Alexandre B Tsybakov · 2011
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Oracle inequalities and optimal inference under group sparsity
Karim Lounici, Massimiliano Pontil, Sara Van De Geer, and Alexandre B Tsybakov · 2011
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Estimation of (near) low-rank matrices with noise and high-dimensional scaling
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Freedman’s inequality for matrix martingales
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Online-to-confidence-set conversions and application to sparse stochastic bandits
Yasin Abbasi-Yadkori, David Pal, and Csaba Szepesvari · 2012
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Excess risk bounds for multitask learning with trace norm regularization
Andreas Maurer and Massimiliano Pontil · 2013
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Sparse multi-task reinforcement learning
Daniele Calandriello, Alessandro Lazaric, and Marcello Restelli · 2014
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Portfolio choices with orthogonal bandit learning
Weiwei Shen, Jun Wang, Yu-Gang Jiang, and Hongyuan Zha · 2015
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Online decision making with high-dimensional covariates
Hamsa Bastani and Mohsen Bayati · 2020
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Meta-learning with stochastic linear bandits
Leonardo Cella, Alessandro Lazaric, and Massimiliano Pontil · 2020
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Beyond ucb: Optimal and efficient contextual bandits with regression oracles
Dylan Foster and Alexander Rakhlin · 2020
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High-dimensional sparse linear bandits
Botao Hao, Tor Lattimore, and Mengdi Wang · 2020
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Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
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The benefit of multitask representation learning
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Branislav Kveton, Csaba Szepesvári, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, and S Muthukrishnan · 2017
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Provably optimal algorithms for generalized linear contextual bandits
Lihong Li, Yu Lu, and Dengyong Zhou · 2017
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Incremental learning-to-learn with statistical guarantees
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, and Massimiliano Pontil · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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Minimax concave penalized multi-armed bandit model with high-dimensional covariates
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Impact of representation learning in linear bandits
Jiaqi Yang, Wei Hu, Jason D Lee, and Simon Shaolei Du · 2020
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Mostly exploration-free algorithms for contextual bandits
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No regrets for learning the prior in bandits
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Multi-task and meta-learning with sparse linear bandits
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Near-optimal representation learning for linear bandits and linear rl
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Low-rank generalized linear bandit problems
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Sparsity-agnostic lasso bandit
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Bayesian decision-making under misspecified priors with applications to meta-learning
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