About Me

I am a Research Scientist at Deepmind in London. Previously, I was a Postdoctoral Researcher in the Department of Electrical Engineering at Princeton University working with Prof. Mengdi Wang. I got my Ph.D. (2014-2019) in the Department of Statistics at Purdue University advised by Prof. Guang Cheng. I was a research intern at Deepmind in London mentored by Dr. Tor Lattimore and Prof. Csaba Szepesvári (Summer, 2019), and a research intern at Adobe Research in San Jose mentored by Dr. Zheng Wen (Summer, 2018).

My research interests focus on the statistical perspective of reinforcement learning and multi-armed bandits. Previously, I worked on statistical machine learning and tensor learning.

Publications

2022

• Interacting Contour Stochastic Gradient Langevin Dynamics
  Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
  ICLR 2022.

• Confident Least Square Value Iteration with Local Access to a Simulator
  Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári
  AISTATS 2022.

• Efficient Local Planning with Linear Function Approximation
  Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári
  ALT 2022. [arXiv]

2021

• Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?
  Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy
  Submitted. [arXiv][GitHub]

• Bandit Phase Retrieval
  Tor Lattimore, Botao Hao
  NeurIPS 2021. [arXiv]

• Information Directed Sampling for Sparse Linear Bandits
  Botao Hao, Tor Lattimore, Wei Deng
  NeurIPS 2021 (spotlight). [arXiv] [slides]

• Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
  Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang
  ICML 2021. [arXiv]

• Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
  Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
  ICML 2021. [arXiv]

• Online Sparse Reinforcement Learning
  Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
  AISTATS 2021. [arXiv] [poster]

• Adaptive Approximate Policy Iteration
  Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári
  AISTATS 2021. [arXiv] [poster]

• Sparse Tensor Additive Regression
  Botao Hao, Boxiang Wang, Pengyuan Wang, Jingfei Zhang, Jian Yang, Will Wei Sun
  Journal of Machine Learning Research. [arXiv]

2020

• Tensors in Modern Statistical Learning
  Will Wei Sun, Botao Hao, Lexin Li
  In Wiley StatsRef: Statistics Reference Online. [Wiley Online Library]

• High-Dimensional Sparse Linear Bandits
  Botao Hao, Tor Lattimore, Mengdi Wang
  NeurIPS 2020. [arXiv] [slides] [poster]

• Adaptive Exploration in Linear Contextual Bandit
  Botao Hao, Tor Lattimore, Csaba Szepesvári
  AISTATS 2020. [arXiv] [slides]

• Sparse and Low-rank Tensor Estimation via Cubic Sketchings
  Botao Hao, Anru Zhang, Guang Cheng
  IEEE Transactions on Information Theory [arXiv] [slides]
  Accepted in part to AISTATS 2020.

2019

• Nonparametric Bayesian Aggregation for Massive Data
  Zuofeng Shang, Botao Hao, Guang Cheng
  Journal of Machine Learning Research. [pdf]

• Bootstrapping Upper Confidence Bound
  Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng
  NeurIPS 2019. [arXiv] [poster]

2018

• Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
  Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng
  Journal of Machine Learning Research. [pdf] [slides]

Professional Service

• Session chair for Machine Learning/Intelligence Cluster at 2020 INFORMS annual meeting (online)
• Co-chair of Ubicomp 2019 Workshop on Continual and Multimodal Learning for Internet of Things, London, UK
• Reviewers for Journals: Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, The IEEE Control Systems Letters, Statistica Sinica, Biostatistics, IEEE Journal on Selected Areas in Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory, Artificial Intelligence
• Reviewers for Conferences: SODA 2022, AAAI 2021-2022, ICLR 2021-2022, NeurIPS 2019-2021, AISTATS 2020-2022, ICML 2020-2022, UAI 2020-2021