Vincent Y. F. Tan 陈延福 (Pronouns: he/him/his/他)

Professor, National Research Foundation (NRF) Investigator, Department of Mathematics, Department of Electrical and Computer Engineering (ECE), National University of Singapore (NUS) Affiliate, Institute of Operations Research and Analytics (IORA), Institute of Data Science (IDS) Office: S17-05-20 (Mathematics) and E4-06-06 (ECE) Phone: (65) 6516-2133 Email: vtan @ nus. edu. sg My Curriculum Vitae. My Google Scholar Profile. My Erdős number is 3 (Tan -> Willsky -> Bollobás -> Erdős).

News (Archive)

• Advertisement: Looking to hire motivated postdocs. Please see this updated advertisement for more details.


• Feb 2026: Invited to be a speaker at the 2026 PKU Workshop on Signal Processing and Optimization to be held in Peking University, China in Jun 2026.


• Feb 2026: Invited to be a keynote speaker at the NYCU Theory Day to be held in Hsinchu, Taiwan in April 2026.


• Feb 2026: Awarded an NRF Investigatorship (Class of 2026) for the proposal titled "Fundamental Limits of Language Models via Information Theory". I am looking to hire multiple postdoctoral scholars. Please see this advertisement.


• Feb 2026: Posted a paper on the arxiv "ODELoRA: Training Low-Rank Adaptation by Solving Ordinary Differential Equations". Joint work with Yihang Gao.


• Feb 2026: Posted a paper on the arxiv "Almost Asymptotically Optimal Active Clustering Through Pairwise Observations". Joint work with Rachel Teo, P. N. Karthik and Ramya Vinayak.


• Jan 2026: Two papers accepted to ICLR 2026: (i) "Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set" and (ii) "Muon Outperforms Adam in Tail-End Associative Memory Learning".


• Jan 2025: Received a Faculty (of Science) Teaching Excellence Award (FTEA).
  
  
  
  
• Jan 2026: Congrats to former Research Fellow Srinivas Kota Reddy for starting as Assistant Professor at the Department of Artificial Intelligence at IIT Kharagpur.


• Jan 2026: Paper "MIMO Capacity Analysis and Channel Estimation for Electromagnetic Information Theory" accepted to the IEEE Journal on Selected Areas of Communications (JSAC). Joint work with Jieao Zhu and Linglong Dai.


• Jan 2026: Posted a paper on the arxiv: "Quantum-Enhanced Neural Contextual Bandit Algorithms". Joint work with Ph.D. student Yuqi Huang and collaborator Sharu Jose.


• Dec 2025: Posted an updated version of a paper with Yujie Liu and Yunbei Xu titled "Finite-Time Minimax Bounds and an Optimal Lyapunov Policy in Queueing Control". See our slides.


• Nov 2025: Paper "On the Exponential Convergence for Offline RLHF with Pairwise Comparisons" accepted to the Alignment Track of AAAI'26 and to be presented as an Oral Presentation. Joint work with former PhD student Zhirui Chen.


• Nov 2025: Congrats to PhD student Yunlong Hou for successfully defending his excellent thesis.


• Oct 2025: Posted a paper on the arxiv: "Transformers Provably Learn Directed Acyclic Graphs via Kernel-Guided Mutual Information". Joint work with Ph.D. student Yuan Cheng and collaborators Yu Huang, Zhe Xiong, and Yingbin Liang.


• Oct 2025: Appointed as a Senior Area Chair of ICML 2026.


• Oct 2025: Invited to speak at the Learning and Information Theory workshop at EPFL Bernoulli Center in Feb 2026.


• Oct 2025: Invited to speak at the Tenth Van der Meulen Seminar in TU Eindhoven. I will give a talk on recent joint work with Hamdi Joudeh on guessing-based decoding with abandonment.


• Oct 2025: Posted a paper on the arxiv: "Parameter-free Algorithms for the Stochastically Extended Adversarial Model". To be presented at NeurIPS 2025.


• Oct 2025: Posted a paper on the arxiv: "Muon Outperforms Adam in Tail-End Associative Memory Learning".


• Sep 2025: Delivered an invited talk on BanditSpec at RLChina.


• Sep 2025: Paper "Parameter-free Algorithms for the Stochastically Extended Adversarial Model" accepted to NeurIPS 2025. Joint work with current PhD student Shuche Wang, former postdoc Adarsh Barik, and collaborator Peng Zhao. The paper presents new parameter-free algorithms for the Stochastically Extended Adversarial (SEA) model, eliminating the need for pre-determined parameters (such as the Lipschitz constant of the loss function or domain diameter) by leveraging the Optimistic Online Newton Step (OONS) algorithm.


• Sep 2025: Paper "A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval" accepted to the IEEE Transactions on Information Theory. Joint work with Adarsh Barik and Anand Krishna.


• Sep 2025: Posted a paper on the arxiv: "Memory Limitations of Prompt Tuning in Transformers". Joint work with Maxime Meyer, Mario Michelessa, and Caroline Chaux.


• Aug 2025: Obtained an MOE AcRF Tier 2 Grant "Online Optimization with Egalitarian and Fairness Constraints" (Direct Cost: $483,740).


• Aug 2025: Gave talks on BanditSpec at Google Deepmind and IISc in Bangalore. Here are the slides and video.


• Aug 2025: Paper "Ensemble-Tight Second-Order Asymptotics and Exponents for Guessing-Based Decoding with Abandonment" accepted to the IEEE Transactions on Information Theory. Joint work with Hamdi Joudeh (TU Eindhoven).

Research Interests and Open Positions

Online Decision Making, Multi-Armed Bandits, Reinforcement Learning
Information Theory with Applications to Machine Learning
Statistical Signal Processing

I am actively recruiting graduate students in the areas of my research interest above. There are various channels to be admitted to NUS including to the Faculty of Science, the College of Design and Engineering, the Institute of Operations Research and Analytics, and the Integrative Sciences and Engineering Programme (Institute of Data Science).

There are also multiple positions for talented postdoctoral scholars. Postdoctoral scholars with strong publication records and showing interest in the above research topics are also encouraged to contact me to check with me if there are available positions. Please see this advertisement as well as this.

Selected Recent Publications

1. Ensemble-Tight Second-Order Asymptotics and Exponents for Guessing-Based Decoding with Abandonment
   Vincent Y. F. Tan and Hamdi Joudeh
   IEEE Transactions on Information Theory, Vol. 71, No. 10, Pages 7555 - 7567, Oct 2025

2. Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks
   Yihang Gao, Michael Kwok-Po Ng, and Vincent Y. F. Tan
   IEEE Transactions on Signal Processing, Vol. 73, Pages 3107 - 3123, 2025

3. Best Arm Identification with Possibly Biased Offline Data
   Le Yang, Vincent Y. F. Tan, and Wang Chi Cheung
   Proc. of the 41st Conference on Uncertainty in Artificial Intelligence (UAI), Rio de Janeiro, Brazil, Jul 2025 (AR ≈ 30.7%)

4. Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget
   Jie Bian and Vincent Y. F. Tan
   Proc. of the 41st Conference on Uncertainty in Artificial Intelligence (UAI), Rio de Janeiro, Brazil, Jul 2025 (AR ≈ 30.7%)

5. BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms [Poster] [Slides]
   Yunlong Hou, Fengzhuo Zhang, Cunxiao Du, Xuan Zhang, Jiachun Pan, Tianyu Pang, Chao Du, Vincent Y. F. Tan, and Zhuoran Yang
   Proc. of the 42nd International Conference on Machine Learning (ICML), Vancouver, Canada, Jul 2025 (AR ≈ 26.9%)

Selected Older Publications

1. Common Information, Noise Stability, and Their Extensions
   Lei Yu and Vincent Y. F. Tan
   Foundations and Trends® in Communications and Information Theory, Vol. 19, No. 2, Pages 107 - 389, 2022

2. Asymptotic Estimates in Information Theory with Non-Vanishing Error Probabilities
   Vincent Y. F. Tan
   Foundations and Trends® in Communications and Information Theory, Vol. 11, Nos. 1-2, Pages 1 – 184, 2014

3. Time-Division is Optimal for Covert Communication over Some Broadcast Channels [Slides]
   Vincent Y. F. Tan and Si-Hyeon Lee
   IEEE Transactions on Information Forensics and Security, Vol. 14, No. 5, Pages 1377 - 1389, May 2019

4. Automatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence [Slides] [Code]
   Vincent Y. F. Tan and Cédric Févotte
   IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 7, Pages 1592 - 1605, Jul 2013

5. A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures [Slides]
   Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong and Alan S. Willsky
   IEEE Transactions on Information Theory, Vol. 57, No. 3, Pages 1714 - 1735, Mar 2011