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Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
We study the problem of dynamic batch learning in high-dimensional spars...
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Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual band...
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Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination
In this work we revisit two classic high-dimensional online learning pro...
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Distributional Robust Batch Contextual Bandits
Policy learning using historical observational data is an important prob...
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Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
A fundamental challenge in contextual bandits is to develop flexible, ge...
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Neural Contextual Bandits with Deep Representation and Shallow Exploration
We study a general class of contextual bandits, where each context-actio...
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Efficient Linear Bandits through Matrix Sketching
We prove that two popular linear contextual bandit algorithms, OFUL and ...
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Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning. We consider two popular limited adaptivity models in literature: batch learning and rare policy switches. We show that, when the context vectors are adversarially chosen in d-dimensional linear contextual bandits, the learner needs O(d log d log T) policy switches to achieve the minimax-optimal regret, and this is optimal up to poly(log d, loglog T) factors; for stochastic context vectors, even in the more restricted batch learning model, only O(loglog T) batches are needed to achieve the optimal regret. Together with the known results in literature, our results present a complete picture about the adaptivity constraints in linear contextual bandits. Along the way, we propose the distributional optimal design, a natural extension of the optimal experiment design, and provide a both statistically and computationally efficient learning algorithm for the problem, which may be of independent interest.
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