10 citations · 33 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
Residual Bootstrap Exploration for Bandit Algorithms
Chi-Hua Wang, Yang Yu, Botao Hao +1
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoo…
Bootstrapping Upper Confidence Bound
Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen +1
Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confi…