Publications

Check my Google Scholar profile for more information!

(* stands for equal contribution. Listed reverse chronologically.)

Manuscripts

• uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
  Yu Chen*, Jiatai Huang*, Yan Dai*, and Longbo Huang.
  In submission.

Conference Publications

• [SIGMETRICS 2025] Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
  Yan Dai and Longbo Huang.
  
• [COLT 2024] Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
  Yan Dai, Qiwen Cui, and Simon S. Du.
  (slides)
• [ICML 2024] Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
  Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, and Yan Dai.
  
• [NeurIPS 2023] The Crucial Role of Normalization in Sharpness-Aware Minimization
  Yan Dai*, Kwangjun Ahn*, and Suvrit Sra.
  (slides, video)
• [ICML 2023] Refined Regret for Adversarial MDPs with Linear Function Approximation
  Yan Dai, Haipeng Luo, Chen-Yu Wei, and Julian Zimmert.
  (slides (long), slides (short), video)
• [ICML 2023] Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
  Jiatai Huang*, Yan Dai*, and Longbo Huang.
  (slides, video)
• [ICLR 2023] Variance-Aware Sparse Linear Bandits
  Yan Dai, Ruosong Wang, and Simon S. Du.
  (slides (long), slides (short), video)
• [NeurIPS 2022] Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
  Yan Dai, Haipeng Luo, and Liyu Chen.
  (slides, video)
• [ICML 2022] Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
  Jiatai Huang*, Yan Dai*, and Longbo Huang.
  (video)