Multi-Channel missing data recovery by exploiting the low-rank hankel structures. - Shuai Zhang 015; Yingshuai Hao; Meng Wang 003; Joe H. Chow (CAMSAP, 2017)

Correction of Simultaneous Bad Measurements by Exploiting the Low-rank Hankel Structure. - Shuai Zhang 015; Meng Wang 003 (ISIT, 2018)

Multichannel Hankel Matrix Completion Through Nonconvex Optimization. - Shuai Zhang 015; Yingshuai Hao; Meng Wang 003; Joe H. Chow (IEEE J. Sel. Top. Signal Process., 2018)

Correction of Corrupted Columns Through Fast Robust Hankel Matrix Completion. - Shuai Zhang 015; Meng Wang 003 (IEEE Trans. Signal Process., 2019)

Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (CISS, 2020)

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (ICML, 2020)

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (NeurIPS, 2021)

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (CoRR, 2021)

Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case. - Shuai Zhang 015; Meng Wang 003; Jinjun Xiong; Sijia Liu 001; Pin-Yu Chen (IEEE Trans. Neural Networks Learn. Syst., 2021)

Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data. - Hongkang Li; Shuai Zhang 015; Meng Wang 003 (CISS, 2022)

How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (ICLR, 2022)

How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis. - Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen; Jinjun Xiong (CoRR, 2022)

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. - Shuai Zhang 015; Meng Wang 003; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Miao Liu 001 (ICLR, 2023)

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. - Mohammed Nowaz Rabbani Chowdhury; Shuai Zhang 015; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen (ICML, 2023)

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. - Shuai Zhang 015; Hongkang Li; Meng Wang 003; Miao Liu 001; Pin-Yu Chen; Songtao Lu; Sijia Liu 001; Keerthiram Murugesan; Subhajit Chaudhury (NeurIPS, 2023)

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. - Shuai Zhang 015; Heshan Devaka Fernando; Miao Liu 001; Keerthiram Murugesan; Songtao Lu; Pin-Yu Chen; Tianyi Chen; Meng Wang 003 (ICML, 2024)

Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. - Hongkang Li; Meng Wang 003; Shuai Zhang 015; Sijia Liu 001; Pin-Yu Chen (SAM, 2024)

How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance. - Hongkang Li; Shuai Zhang 015; Yihua Zhang; Meng Wang 003; Sijia Liu 001; Pin-Yu Chen (CoRR, 2024)

When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers. - Hongkang Li; Yihua Zhang; Shuai Zhang 015; Pin-Yu Chen; Sijia Liu 001; Meng Wang 003 (ICLR, 2025)