Published several preprints and conference papers, including works presented at NeurIPS, ICML, and AISTATS. Specific publications include:
- To bootstrap or to rollout? An optimal and adaptive interpolation
- Refined Risk Bounds for Unbounded Losses via Transductive Priors
- The Statistical Complexity of Interactive Decision Making
- Bridging multiple worlds: multi-marginal optimal transport for causal partial-identification problem
- Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
- Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
- Online Estimation via Offline Estimation: An Information-Theoretic Framework
- How Does Variance Shape the Regret in Contextual Bandits?
- The Non-linear F-Design and Applications to Interactive Learning
- Model-free reinforcement learning with the decision-estimation coefficient
- Convex and Non-Convex Optimization under Generalized Smoothness
- Byzantine-robust federated linear bandits
- Robust Learning Under Clean-label Attack
- Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
- Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
- Importance Resampling for Off-policy Prediction
- Concentration inequalities for multinoulli random variables
Research Experience
Teaching Assistant for Dynamic Programming & Reinforcement Learning (Spring 2022).
Education
Pursuing a Ph.D. at MIT EECS, advised by Sasha Rakhlin.
Background
A final-year Ph.D. student at MIT EECS, focusing on the intersection between machine learning theory and interactive decision making, including online learning, bandits, and reinforcement learning.