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Invited talks
Upcoming talks
Structure-driven design of reinforcement learning algorithms: a tale of two estimators, Northwestern University, Nov 2024
Continuous-time reinforcement learning: blessings of diffusion structures and high-order approximations, CMS Winter Meeting, Vancouver, Dec 2024
Past talks
Structure-driven design of reinforcement learning algorithms: a tale of bootstrapping and rollout, Georgia Institute of Technology, Nov 2024
To bootstrap or to rollout? An optimal and adaptive interpolation, INFORMS Annual Meeting, Seattle, Oct 2024
A decorrelation method for general regression adjustment in randomized experiments, IMS-Bernoulli World Congress, Bochum, Aug 2024
Optimal stochastic approximation under general norms with applications to reinforcement learning, Shanghai Jiaotong University, July 2024
To bootstrap or to rollout? An optimal and adaptive interpolation, International Conference on Frontiers of Data Science, Hangzhou, Jul 2024
A decorrelation method for general regression adjustment in randomized experiments, Banff International Research Station, Feb 2024
Policy Evaluation With General Function Approximation: Efficient Algorithms And Instance-dependent Guarantees, INFORMS Annual Meeting, Phoenix, Oct 2023
Statistical theory for reinforcement learning: Oracle inequalities, Markov chains, and stochastic approximation, Young Researchers Workshop, Cornell University, Oct 2022
Optimal and Instance-dependent Guarantees for Markovian Linear Stochastic Approximation, APS student paper competition at INFORMS Annual Meeting, Indianapolis, Oct 2022
On The Statistical Complexity Of Reinforcement Learning With Function Approximation, INFORMS Annual Meeting, Indianapolis, Oct 2022
Rethinking semi-parametric efficiency for off-policy estimation: a non-asymptotic perspective, BLISS seminar, UC Berkeley, Oct 2022
Optimal variance-reduced stochastic approximation in Banach spaces, Applied and Computational Math seminar, Georgia Institute of Technology, Nov 2022
Optimal algorithms for reinforcement learning: Oracle inequalities, Markov chains, and stochastic approximation, International Conference on Continuous Optimization, Lehigh University, July 2022
Statistical theory for reinforcement learning: Oracle inequalities, Markov chains, and stochastic approximation, Neyman seminar, Department of Statistics, UC Berkeley, January 2022
High-Order Langevin diffusion yields an accelerated MCMC algorithm, Simons Institute program on Geometric Methods in Optimization and Sampling, October 2021
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