12 citations · 44 across the 12 of their papers we have counts for
12 papers
Global Convergence and Rich Feature Learning in -Layer Infinite-Width Neural Networks under P Parametrization
Zixiang Chen, Greg Yang, Qingyue Zhao +1
Despite deep neural networks' powerful representation learning capabilities, theoretical understanding of how networks can simultaneously achieve meaningful feature learning and gl…
Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis
Zikun Zhang, Zixiang Chen, Quanquan Gu
Diffusion models have achieved great success in generating high-dimensional samples across various applications. While the theoretical guarantees for continuous-state diffusion mod…
Self-Play Preference Optimization for Language Model Alignment
Yue Wu, Zhiqing Sun, Huizhuo Yuan +3
Standard reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrat…
Matching the Statistical Query Lower Bound for -Sparse Parity Problems with Sign Stochastic Gradient Descent
Yiwen Kou, Zixiang Chen, Quanquan Gu +1
The -sparse parity problem is a classical problem in computational complexity and algorithmic theory, serving as a key benchmark for understanding computational classes. In this…
Guided Discrete Diffusion for Electronic Health Record Generation
Jun Han, Zixiang Chen, Yongqian Li +4
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design…
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
Yan Wang, Lihao Wang, Yuning Shen +4
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as m…
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji +1
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tun…
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Zixiang Chen, Yihe Deng, Huizhuo Yuan +2
Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect o…
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Zixiang Chen, Huizhuo Yuan, Yongqian Li +3
Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration…
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
Yiwen Kou, Zixiang Chen, Quanquan Gu
The implicit bias towards solutions with favorable properties is believed to be a key reason why neural networks trained by gradient-based optimization can generalize well. While t…
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
Zixiang Chen, Junkai Zhang, Yiwen Kou +3
The challenge of overfitting, in which the model memorizes the training data and fails to generalize to test data, has become increasingly significant in the training of large neur…
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
Jingfeng Wu, Difan Zou, Zixiang Chen +3
Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjus…