5 citations · 26 across the 23 of their papers we have counts for
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Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
Osama A. Hanna, Lin F. Yang, Christina Fragouli
In this paper, we address the stochastic contextual linear bandit problem, where a decision maker is provided a context (a random set of actions drawn from a distribution). The exp…
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wirel…
Near-Optimal Sample Complexity Bounds for Constrained MDPs
Sharan Vaswani, Lin F. Yang, Csaba Szepesvári
In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (C…
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
Sanae Amani, Lin F. Yang, Ching-An Cheng
We study lifelong reinforcement learning (RL) in a regret minimization setting of linear contextual Markov decision process (MDP), where the agent needs to learn a multi-task polic…
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Sanae Amani, Tor Lattimore, András György +1
We study distributed contextual linear bandits with stochastic contexts, where agents act cooperatively to solve a linear bandit-optimization problem with -dimensional featu…