17 citations · 33 across the 26 of their papers we have counts for
7 papers · 1 filter
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
Runyu Zhang, Jiawei Zhang, Gioele Zardini +2
Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-specific objectives. A common appro…
Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning
Binghang Lu, Zheyuan Deng, Runyu Zhang +6
A central challenge in continual learning for large language models (LLMs) is catastrophic forgetting, where adapting to new tasks can substantially degrade performance on previous…
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
Binghang Lu, Runyu Zhang, Changhong Mou +2
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems governed by partial differential equations (PDEs), but standard PINN…
Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
Runyu Zhang, Na Li, Asuman Ozdaglar +2
Risk sensitivity has become a central theme in reinforcement learning (RL), where convex risk measures and robust formulations provide principled ways to model preferences beyond e…
Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
Phevos Paschalidis, Runyu Zhang, Na Li
In this paper, we formulate the multi-agent graph bandit problem as a multi-agent extension of the graph bandit problem introduced by Zhang, Johansson, and Li [CISS 57, 1-6 (2023)]…
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
Runyu Zhang, Qinghua Liu, Huan Wang +3
This paper studies policy optimization algorithms for multi-agent reinforcement learning. We begin by proposing an algorithm framework for two-player zero-sum Markov Games in the f…
Gradient play in stochastic games: stationary points, convergence, and sample complexity
Runyu Zhang, Zhaolin Ren, Na Li
We study the performance of the gradient play algorithm for stochastic games (SGs), where each agent tries to maximize its own total discounted reward by making decisions independe…