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).
Iām 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
2021
2020
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
Supervision and mentoring
Ragja Palakkadavath – Ph.D. student at A2I2, Deakin University ā 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 ā 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.
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