10 papers · 1 filter
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…
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…