10 citations · 31 across the 7 of their papers we have counts for
12 papers
Interacting Contour Stochastic Gradient Langevin Dynamics
Wei Deng, Siqi Liang, Botao Hao +2
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (C…
Bandit Phase Retrieval
Tor Lattimore, Botao Hao
We study a bandit version of phase retrieval where the learner chooses actions in the -dimensional unit ball and the expected reward is $\langle A_t, θ_\star\ran…
Information Directed Sampling for Sparse Linear Bandits
Botao Hao, Tor Lattimore, Wei Deng
Stochastic sparse linear bandits offer a practical model for high-dimensional online decision-making problems and have a rich information-regret structure. In this work we explore…
Optimization Issues in KL-Constrained Approximate Policy Iteration
Nevena Lazić, Botao Hao, Yasin Abbasi-Yadkori +2
Many reinforcement learning algorithms can be seen as versions of approximate policy iteration (API). While standard API often performs poorly, it has been shown that learning can…
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore +2
This paper provides a statistical analysis of high-dimensional batch Reinforcement Learning (RL) using sparse linear function approximation. When there is a large number of candida…
High-Dimensional Sparse Linear Bandits
Botao Hao, Tor Lattimore, Mengdi Wang
Stochastic linear bandits with high-dimensional sparse features are a practical model for a variety of domains, including personalized medicine and online advertising. We derive a…