58 citations · 169 across the 25 of their papers we have counts for
26 papers
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach
Dengwang Tang, Rahul Jain, Botao Hao +1
In this paper, we study the problem of efficient online reinforcement learning in the infinite horizon setting when there is an offline dataset to start with. We assume that the of…
Sequential Best-Arm Identification with Application to Brain-Computer Interface
Xin Zhou, Botao Hao, Jian Kang +2
A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. It allows individuals to interact wi…
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
Botao Hao, Rahul Jain, Dengwang Tang +1
In this paper, we address the following problem: Given an offline demonstration dataset from an imperfect expert, what is the best way to leverage it to bootstrap online learning p…
Leveraging Demonstrations to Improve Online Learning: Quality Matters
Botao Hao, Rahul Jain, Tor Lattimore +2
We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We sh…
Sample Efficient Deep Reinforcement Learning via Local Planning
Dong Yin, Sridhar Thiagarajan, Nevena Lazic +3
The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environmen…
Contextual Information-Directed Sampling
Botao Hao, Tor Lattimore, Chao Qin
Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm. However, it is still unclear what is the right for…
Regret Bounds for Information-Directed Reinforcement Learning
Botao Hao, Tor Lattimore
Information-directed sampling (IDS) has revealed its potential as a data-efficient algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for Markov D…
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…
The Neural Testbed: Evaluating Joint Predictions
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +7
Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluat…
Efficient Local Planning with Linear Function Approximation
Dong Yin, Botao Hao, Yasin Abbasi-Yadkori +2
We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We assume that the agent only has local access to the simulator…
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…
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Botao Hao, Xiang Ji, Yaqi Duan +3
Bootstrapping provides a flexible and effective approach for assessing the quality of batch reinforcement learning, yet its theoretical property is less understood. In this paper,…
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…
Online Sparse Reinforcement Learning
Botao Hao, Tor Lattimore, Csaba Szepesvári +1
We investigate the hardness of online reinforcement learning in fixed horizon, sparse linear Markov decision process (MDP), with a special focus on the high-dimensional regime wher…
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
Jie Zhou, Botao Hao, Zheng Wen +2
Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. In these problems, a decision at each t…
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…
Adaptive Approximate Policy Iteration
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori +2
Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains. However,…
Adaptive Exploration in Linear Contextual Bandit
Botao Hao, Tor Lattimore, Csaba Szepesvari
Contextual bandits serve as a fundamental model for many sequential decision making tasks. The most popular theoretically justified approaches are based on the optimism principle.…
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…
Sparse Tensor Additive Regression
Botao Hao, Boxiang Wang, Pengyuan Wang +3
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that model…
Sparse and Low-rank Tensor Estimation via Cubic Sketchings
Botao Hao, Anru Zhang, Guang Cheng
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse te…