41 citations · 158 across the 21 of their papers we have counts for
5 papers · 1 filter
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Gen Li, Laixi Shi, Yuxin Chen +1
Achieving sample efficiency in online episodic reinforcement learning (RL) requires optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic Mar…
The Rate of Convergence of Variation-Constrained Deep Neural Networks
Gen Li, Jie Ding
Multi-layer feedforward networks have been used to approximate a wide range of nonlinear functions. An important and fundamental problem is to understand the learnability of a netw…
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li, Yuxin Chen, Yuejie Chi +2
Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario…
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Gen Li, Changxiao Cai, H. Vincent Poor +1
Eigenvector perturbation analysis plays a vital role in various data science applications. A large body of prior works, however, focused on establishing eigenvector pert…
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
Gen Li, Yuting Wei, Yuejie Chi +1
The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimizatio…