Thanh Nguyen-Tang

Intro: I am a Postdoctoral Research Fellow at Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, working with Raman Arora. I was an Associate Research Fellow at the Applied AI Institute, Deakin University in July 2021-June 2022 and completed my PhD there in Feb 2022. I did my Master in Computer Science and Engineering at Ulsan National University of Science and Technology (UNIST) in 2018.

Research interests:

• Reinforcement Learning

• Probabilistic Deep Learning

• Representation Learning

• Learning under Distributional Shifts

I’m always actively open to research collaborations and chat!

Here are my Google Scholar, Semantic Scholar, Github, Twitter .

Latest News

• [Sep 14, 2022] One paper got accepted to NeurIPS, 2022.

• [Aug 8, 2022] I am acknowledged in Mengyan Zhang's PhD thesis.

• [Jan 21, 2022] One paper got accepted to ICLR, 2022.

• [Oct 25, 2021] A short version of our work has been accepted to the NeurIPS’21 Workshop on Offline Reinforcement Learning.

• [Jul 8, 2021] A short version of our work has been accepted to the ICML’21 Workshop on Reinforcement Learning Theory.

• [Jul 1, 2021] I start my postdoc at A\(^2\)I\(^2\), Deakin University after submitting my Ph.D. thesis in 24 Jun.

• [May 20, 2021] I have been accepted to the Deep Learning Theory Summer School at Princeton, acceptance rate: 180/500 = 36%.

Publications

2022

• On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
  Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora
  Under review, 2022
  

• On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces
  Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh
  Under review, 2022
  

• Learning White Noises in Neural Stochastic Differential Equations
  Anh Tong, Thanh Nguyen-Tang, Toan Tran, Jaesik Choi
  NeurIPS, 2022
  

• Two-Stage Neural Contextual Bandits for Personalised News Recommendation
  Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong
  Under review, 2022
  [arXiv]

• Partially Offline Contextual Bandit Learning under Support Defficiency
  Hung Tran-The, Thanh Nguyen-Tang, Sunil Gupta, Santu Rana, Svetha Venkatesh
  Under review, 2022
  

• Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
  Thanh Nguyen-Tang, Sunil Gupta, A.Tuan Nguyen, and Svetha Venkatesh
  ICLR, 2022
  Workshop on Offline Reinforcement Learning, NeurIPS, 2021
  [arXiv] [Poster] [Slides] [Code]

2021

• Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
  Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh
  Workshop on Reinforcement Learning Theory, ICML, 2021
  [arXiv] [Slides] [Talk]

• Distributional Reinforcement Learning via Moment Matching
  Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh
  AAAI, 2021
  [arXiv] [Code] [Slides] [Poster] [Talk]

2020

• Distributionally Robust Bayesian Quadrature Optimization
  Thanh Tang Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh
  AISTATS, 2020
  [arXiv] [Code] [Slides] [Talk]

2019

• Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks
  Thanh Tang Nguyen, Jaesik Choi
  Entropy, 21(10), 976, 2019
  [Code]

• Bayesian Optimization with Unknown Search Space
  Huong Ha, Santu Rana, Sunil Gupta, Thanh Tang Nguyen, Hung Tran-The, Svetha Venkatesh
  NeurIPS, 2019
  [Code] [Poster]

Dissertations

• On Practical Reinforcement Learning: Provable Robustness, Scalability and Statistical Efficiency
  Ph.D. dissertation, Deakin University, Australia, July 2021

• Parametric Information Bottleneck to Optimize Stochastic Neural Networks
  Master Thesis, Ulsan National University of Science and Technology, South Korea, 2018
  [Slides] [Talk]

Academic Service

• Senior Program Committee: AAAI (2023)

• Reviewer/Program Committee: NeurIPS (2022, 2021, 2020), ICML (2022, 2021), ICLR (2023, 2022, 2021- Outstanding reviewer award), AISTATS (2021), AAAI (2022, 2021-Top 25% of Program Committees, 2020), EWRL (2022), L4DC (2022), NeurIPS Workshop on Offline Reinforcement Learning (2022, 2021)

• Volunteer: ICML (2022), AutoML (2022)