53 citations · 177 across the 34 of their papers we have counts for
17 papers · 1 filter
GAV: A scientific experimentation environment for PDE agents
Muheng Li, Wenlong Mou
Reliable feedback is essential for coding agents to develop programs for partial differential equations (PDEs). Yet evaluating these programs on new PDE problems often requires exp…
Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
Alexander Ryabchenko, Jian Qian, Wenlong Mou
We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient directi…
What should post-training optimize? A test-time scaling law perspective
Muheng Li, Jian Qian, Wenlong Mou
Large language models are increasingly deployed with test-time strategies: sample responses, score them with a reward model or verifier, and return the best. This deployment ru…
Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations
Xiaofan Xia, Qin Li, Wenlong Mou
We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and w…
Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
Wenlong Mou
We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. We consider model-free algorithms…
Predicting and improving test-time scaling laws via reward tail-guided search
Muheng Li, Jian Qian, Wenlong Mou
Test-time scaling has emerged as a critical avenue for enhancing the reasoning capabilities of Large Language Models (LLMs). Though the straight-forward ''best-of-'' (BoN) strat…
Reinforcement Learning with Action-Triggered Observations
Alexander Ryabchenko, Wenlong Mou
We introduce Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), a reinforcement learning framework for partial observability in which full state observa…
Federated learning over physical channels: adaptive algorithms with near-optimal guarantees
Rui Zhang, Wenlong Mou
In federated learning, communication cost can be significantly reduced by transmitting the information over the air through physical channels. In this paper, we propose a new class…
Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models
Wenlong Mou
We study the problem of learning the optimal control policy for fine-tuning a given diffusion process, using general value function approximation. We develop a new class of algorit…
Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs
Wenlong Mou
We study the estimation of the value function for continuous-time Markov diffusion processes using a single, discretely observed ergodic trajectory. Our work provides non-asymptoti…
To bootstrap or to rollout? An optimal and adaptive interpolation
Wenlong Mou, Jian Qian
Bootstrapping and rollout are two fundamental principles for value function estimation in reinforcement learning (RL). We introduce a novel class of Bellman operators, called subgr…
On Bellman equations for continuous-time policy evaluation I: discretization and approximation
Wenlong Mou, Yuhua Zhu
We study the problem of computing the value function from a discretely-observed trajectory of a continuous-time diffusion process. We develop a new class of algorithms based on eas…
Optimal oracle inequalities for solving projected fixed-point equations
Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright
Linear fixed point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equ…
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
Wenlong Mou, Zheng Wen, Xi Chen
We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. the agent has a prior knowledge that the optimal p…
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
Wenlong Mou, Liwei Wang, Xiyu Zhai +1
Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterati…
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
Kai Zheng, Wenlong Mou, Liwei Wang
Non-interactive Local Differential Privacy (LDP) requires data analysts to collect data from users through noisy channel at once. In this paper, we extend the frontiers of Non-inte…
Efficient Private ERM for Smooth Objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou +1
In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we…