9 citations · 33 across the 24 of their papers we have counts for
8 papers · 1 filter
A Function-Space Approach to the Statistical Mechanics of Learning Dynamics
Yizhou Zhang, Weichen Wu, Lun Du +1
In the kernel regime, neural-network learning inherits its preferences from a frozen spectrum. During feature learning, this spectrum evolves, yet networks retain systematic biases…
Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems
Wen Ye, Muyan Weng, Chuizheng Meng +3
Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning. However, due to privacy concern…
Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning
Chenyou Guo, Zongqi Liu, Yizhou Zhang +2
While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth scie…
On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
Hao Ye, Jisheng Dang, Junfeng Fang +7
Recent extensive research has demonstrated that the enhanced reasoning capabilities acquired by models through Reinforcement Learning with Verifiable Rewards (RLVR) are primarily c…
Training Generalizable Collaborative Agents via Strategic Risk Aversion
Chengrui Qu, Yizhou Zhang, Nicolas Lanzetti +1
Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches to learning policies for such…
Provably Convergent Actor-Critic for MARL through Risk-aversion
Yizhou Zhang, Eric Mazumdar
Learning stationary policies in infinite-horizon general-sum Markov games (MGs) remains a fundamental open problem in Multi-Agent Reinforcement Learning (MARL). While stationary st…
When Does Learning Renormalize? Sufficient Conditions for Power Law Spectral Dynamics
Yizhou Zhang
Empirical power--law scaling has been widely observed across modern deep learning systems, yet its theoretical origins and scope of validity remain incompletely understood. The Gen…
Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
Yizhou Zhang
Neural scaling laws and double-descent phenomena suggest that deep-network training obeys a simple macroscopic structure despite highly nonlinear optimization dynamics. We derive s…