12 citations · 12 across the 7 of their papers we have counts for
7 papers
Simple Denoising Diffusion Language Models
Huaisheng Zhu, Zhengyu Chen, Shijie Zhou +8
Recent Uniform State Diffusion Models (USDMs), initialized from a uniform prior, offer the promise of fast text generation due to their inherent self-correction ability compared to…
On a Connection Between Imitation Learning and RLHF
Teng Xiao, Yige Yuan, Mingxiao Li +2
This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcem…
SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters
Teng Xiao, Yige Yuan, Zhengyu Chen +4
Existing preference optimization objectives for language model alignment require additional hyperparameters that must be extensively tuned to achieve optimal performance, increasin…
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
Jinluan Yang, Zhengyu Chen, Teng Xiao +3
Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a…
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
Jinluan Yang, Ruihao Zhang, Zhengyu Chen +4
This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the…
Simple and Asymmetric Graph Contrastive Learning without Augmentations
Teng Xiao, Huaisheng Zhu, Zhengyu Chen +1
Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefab…
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders
Teng Xiao, Zhengyu Chen, Suhang Wang
This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent w…