Yuxin Chen

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

2020 · arXiv (Cornell University)

Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms in contemporary reinforcement learning. This class of methods is often applied in conjunction with entropy regularization -- an algorithmic scheme that …

Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization

2023 · arXiv (Cornell University)

Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL). The extent to which an agent is influenced by unseen co-players depends on the agent's policy and the specific scenario. A quantitative examination of this …

Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness

2025

Vertical federated learning (VFL) enables a privacy-preserving collaboration among various parties to train a global model by melding their geo-distributed data features. Communication has been recognized as the primary bottleneck that impairs training efficiency due …

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

2025 · arXiv (Cornell University)

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a …