works on

From the 2 of 7 linked papers with an AI index.

most citedThe Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Multi-Mask Diffusion Language Models for Few-Step Generation

Sijin Chen, Yinuo Ren, Heyang Zhao +3

Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDMs, all forward trajectories…

math.OC2026

Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies

Ziheng Cheng, Xin Guo, Huyên Pham +1

The paper proposes a model‑free reinforcement learning framework for continuous‑time extended mean field control using deterministic feedback policies, deriving deterministic polic…

cs.LG20261 cited

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication

Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5

The paper analyzes the theoretical limits of Local SGD for distributed learning with heterogeneous data, showing existing heterogeneity assumptions are insufficient for proving its…

cs.LG2026

Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State

Ziheng Cheng, Xin Guo, Yufei Zhang

The theory of continuous-time reinforcement learning (RL) has progressed rapidly in recent years. While the ultimate objective of RL is typically to learn deterministic control pol…

cs.LG2025

OVERT: A Benchmark for Over-Refusal Evaluation on Text-to-Image Models

Ziheng Cheng, Yixiao Huang, Hui Xu +4

Text-to-Image (T2I) models have achieved remarkable success in generating visual content from text inputs. Although multiple safety alignment strategies have been proposed to preve…

cs.LG2025

Data-Efficient Training by Evolved Sampling

Ziheng Cheng, Zhong Li, Jiang Bian

Data selection is designed to accelerate learning with preserved performance. To achieve this, a fundamental thought is to identify informative data samples with significant contri…