I am currently a Research Fellow at the School of Computing - National University of Singapore (NUS), working with Professors Leong Tze Yun and Arnab Bhattacharyya. My research interests include causal inference, causal discovery, reinforcement learning, and point processes.
I completed my PhD in computer science at the NUS on problems related to causal inference from observational data. I was advised by Professor Leong Tze Yun.
News
02 May 2024 | Our paper 'Reward Shaping for Reinforcement Learning with An Assistant Reward Agent' was just accepted to ICML 2024. |
26 Apr 2024 | Our paper 'Decoupled Prompt-Adapter Tuning for Continual Activity Recognition' was just accepted to CoLLAs 2024. |
21 Dec 2023 | Our paper 'Mixed-Initiative Bayesian Sub-Goal Optimization in Hierarchical Reinforcement Learning' was just accepted to AAMAS 2024 as a full paper publication. |
07 Apr 2023 | Our paper 'Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations' was just accepted to CogSci 2023 as full paper publication. |
04 Jan 2023 | Our paper 'Hierarchical Reinforcement Learning with Human-AI Collaborative Sub-Goals Optimization' was just accepted to AAMAS 2023 as an extended abstract. |
08 Oct 2022 | I received the NeurIPS 2022 Scholar Award. |
15 Sep 2022 | Our paper 'An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects' was accepted to NeurIPS 2022. |
02 Aug 2022 | Our paper 'Adaptive Multi-Source Causal Inference from Observational Data' was accepted to CIKM 2022 full paper track. |
29 Jul 2022 | I am awarded the Dean's Graduate Research Excellence Award AY2021/2022. |
11 Jun 2022 | Our paper 'Transfer Kernel Learning for Multi-source Transfer Regression' was accepted to IEEE TPAMI. |
16 May 2022 | Our paper 'Bayesian Federated Estimation of Causal Effects from Observational Data' was accepted to UAI 2022. |
Selected publications
- Reward Shaping for Reinforcement Learning with An Assistant Reward Agent.
Haozhe Ma, Kuankuan Sima, Thanh Vinh Vo, Di Fu, Tze-Yun Leong.
41st International Conference on Machine Learning (ICML), 2024.
[PDF] [OpenReview] - Decoupled Prompt-Adapter Tuning for Continual Activity Recognition.
Di Fu, Thanh Vinh Vo, Haozhe Ma, Tze-Yun Leong.
Conference on Lifelong Learning Agents (CoLLAs), 2024.
[PDF] - Mixed-Initiative Bayesian Sub-Goal Optimization in Hierarchical Reinforcement Learning.
Haozhe Ma, Thanh Vinh Vo, Tze-Yun Leong.
23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024.
(full paper)
[PDF] - Hierarchical Reinforcement Learning with Human-AI Collaborative Sub-Goals Optimization.
Haozhe Ma, Thanh Vinh Vo, Tze-Yun Leong.
22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
(extended abstract)
[PDF] - An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects.
Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong.
36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
[PDF] [OpenReview] [Supplementary] [Slides] [Poster] [Code] - Bayesian Federated Estimation of Causal Effects from Observational Data.
Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong.
38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
[PDF] [OpenReview] [Supplementary] [Code] - Adaptive Multi-Source Causal Inference from Observational Data.
Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong.
31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.
(full paper track)
[PDF] [Slides] [Code] - Transfer Kernel Learning for Multi-source Transfer Regression.
Pengfei Wei, Thanh Vinh Vo, Xinghua Qu, Yew Soon Ong, Zejun Ma.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022. (Impact Factor: 24.314)
[PDF] - Causal Modeling with Stochastic Confounders.
Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze-Yun Leong.
24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
[PDF] [Supplementary] [Code] - Z-Transforms and its Inference on Partially Observable Point Processes.
Young Lee, Thanh Vinh Vo, Kar Wai Lim, Harold Soh.
27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.
[PDF] - Generation Meets Recommendation: Proposing Novel Items for Groups of Users.
Thanh Vinh Vo, Harold Soh.
ACM Recommender Systems Conference (RecSys), 2018. (Best Long Paper Award Runner-up) - Instance Reduction for Time Series Classification using MDL Principle.
Thanh Vinh Vo, Duong Tuan Anh.
Intelligent Data Analysis 21(3), IOS Press, 2017.
Preprints and work in progress
- Self-supervised Mask Graph Autoencoder via Neighbor Reconstruction for Graphs with Heterophily.
Jiele Wu, Thanh Vinh Vo, Tze-Yun Leong. - Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning.
Haozhe Ma, Zhengding Luo, Thanh Vinh Vo, Kuankuan Sima, Tze-Yun Leong. - Federated Causal Inference from Observational Data.
Thanh Vinh Vo, Young Lee, Tze-Yun Leong.
[PDF] - A Distribution Over DAGs for Uncertain Causal Discovery.
Thanh Vinh Vo, Young Lee, Arnab Bhattacharyya, Tze-Yun Leong.
Services
- Reviewer/PC member:
- NeurIPS 2023
- ICLR 2024, 2025
- UAI 2023, 2024
- AAAI 2023, 2024, 2025
- AISTATS 2022, 2023, 2024, 2025
- IJCAI 2024
- AAMAS 2025
- KDD 2024, 2025
- Journal reviewer: Expert Systems with Applications, Computers & Industrial Engineering.