Corrupted sensing with sub-Gaussian measurements. - Jinchi Chen; Yulong Liu 002 (ISIT, 2017)

Data-Time Tradeoffs for Corrupted Sensing. - Jinchi Chen; Yulong Liu 002 (IEEE Signal Process. Lett., 2018)

Towards More Usable Dataset Search: From Query Characterization to Snippet Generation. - Jinchi Chen; Xiaxia Wang 001; Gong Cheng 001; Evgeny Kharlamov; Yuzhong Qu (CIKM, 2019)

A Framework for Evaluating Snippet Generation for Dataset Search. - Xiaxia Wang 001; Jinchi Chen; Shuxin Li; Gong Cheng 001; Jeff Z. Pan; Evgeny Kharlamov; Yuzhong Qu (ISWC (1), 2019)

Exact matrix completion based on low rank Hankel structure in the Fourier domain. - Jinchi Chen; Weiguo Gao; Ke Wei 001 (CoRR, 2019)

Stable Recovery of Structured Signals From Corrupted Sub-Gaussian Measurements. - Jinchi Chen; Yulong Liu 002 (IEEE Trans. Inf. Theory, 2019)

Simulated or Physical? An Empirical Study on Input Validation for Context-aware Systems in Different Environments. - Jinchi Chen; Yi Qin 002; Huiyan Wang 001; Chang Xu 001 (Internetware, 2020)

Low Rank Vectorized Hankel Lift for Matrix Recovery via Fast Iterative Hard Thresholding. - Zengying Zhu; Jinchi Chen; Weiguo Gao (CoRR, 2021)

Blind Super-Resolution via Projected Gradient Descent. - Sihan Mao; Jinchi Chen (ISIT, 2022)

Vectorized Hankel Lift: A Convex Approach for Blind Super-Resolution of Point Sources. - Jinchi Chen; Weiguo Gao; Sihan Mao; Ke Wei 001 (IEEE Trans. Inf. Theory, 2022)

Blind Super-Resolution of Point Sources via Projected Gradient Descent. - Sihan Mao; Jinchi Chen (IEEE Trans. Signal Process., 2022)

On the Linear Convergence of Policy Gradient under Hadamard Parameterization. - Jiacai Liu; Jinchi Chen; Ke Wei 001 (CoRR, 2023)

Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion. - Haifeng Wang; Jinchi Chen; Ke Wei 001 (J. Mach. Learn. Res., 2023)

Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift. - Haifeng Wang; Jinchi Chen; Hulei Fan; Yuxiang Zhao; Li Yu (ICC, 2024)

Fast and Provable Simultaneous Blind Super-Resolution and Demixing for Point Source Signals: Scaled Gradient Descent without Regularization. - Jinchi Chen (CoRR, 2024)

Riemannian Gradient Descent Method to Joint Blind Super-Resolution and Demixing in ISAC. - Zeyu Xiang; Haifeng Wang; Jiayi Lv; Yujie Wang; Yuxue Wang; Yuxuan Ma; Jinchi Chen (CoRR, 2024)

Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning. - Jinchi Chen; Jie Feng; Weiguo Gao; Ke Wei 001 (J. Mach. Learn. Res., 2024)

Global Convergence of Natural Policy Gradient with Hessian-Aided Momentum Variance Reduction. - Jie Feng; Ke Wei 001; Jinchi Chen (J. Sci. Comput., 2024)