Thanh Nguyen-Tang

Intro. I am currently an Associate Research Fellow and have recently finished my PhD (Feb 2022) at the Applied AI Institute, Deakin University. Prior to that, I was a researcher at Ulsan National University of Science and Technology (UNIST) from Mar 2018 to Dec 2018, and I obtained my Master at UNIST in 2018. I was also serving as a ML technical consultant for a local startup company on NLP product solutions.

Research interests. Algorithmic and theoretical foundations of modern machine learning - reinforcement learning, deep learning and representation learning.

We are running a mini Modern Statistical Learning and Optimization Seminar (MSLOS).

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

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

Latest News

• [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

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

• 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]

Supervision and mentoring

• Ragja Palakkadavath – Ph.D. student at A2I2, Deakin University – Topic: Domain Generalization

• Nguyen Ngoc Hieu – Resident at FPT.AI – Topic: Learning Theory for Implicit Deep Models.

• Qiyao Wei, senior undergrad at University of Toronto – Topic: Research statement in offline RL for PhD application

Academic Service

• Reviewer, NeurIPS, 2022

• Program Committee, EWRL, 2022

• Reviewer, L4DC, 2022 (1 paper)

• Reviewer, ICML, 2022 (3 papers)

• Program Committee, NeurIPS Workshop on Offline Reinforcement Learning, 2021

• Program Committee, AAAI, 2022

• Reviewer, ICLR, 2022

• Reviewer, NeurIPS, 2021

• Reviewer, ICML, 2021

• Reviewer, AISTATS, 2021

• Program Committee, AAAI, 2021

• Reviewer, ICLR, 2021 (outstanding reviewer award)

• Reviewer, NeurIPS, 2020

I created the “ML Theory Exchange Network” Discord channel (currently 66 members as of 25 Oct 2021) to connect ML-theory passionate self-learners (like myself) with senior researchers for exchanging ideas and learning resources.