Averaging random projection: A fast online solution for large-scale constrained stochastic optimization. - Jialin Liu 003; Yuantao Gu; Mengdi Wang (ICASSP, 2015)

Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints. - Mengdi Wang; Yichen Chen; Jialin Liu 003; Yuantao Gu (CoRR, 2015)

Online convolutional dictionary learning. - Jialin Liu 003; Cristina Garcia-Cardona; Brendt Wohlberg; Wotao Yin (ICIP, 2017)

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. - Xiaohan Chen; Jialin Liu 003; Zhangyang Wang; Wotao Yin (NeurIPS, 2018)

First- and Second-Order Methods for Online Convolutional Dictionary Learning. - Jialin Liu 003; Cristina Garcia-Cardona; Brendt Wohlberg; Wotao Yin (SIAM J. Imaging Sci., 2018)

ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA. - Jialin Liu 003; Xiaohan Chen; Zhangyang Wang; Wotao Yin (ICLR (Poster), 2019)

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers. - Ernest K. Ryu; Jialin Liu 003; Sicheng Wang 003; Xiaohan Chen; Zhangyang Wang; Wotao Yin (ICML, 2019)

Learning A Minimax Optimizer: A Pilot Study. - Jiayi Shen; Xiaohan Chen; Howard Heaton; Tianlong Chen 001; Jialin Liu 003; Wotao Yin; Zhangyang Wang (ICLR, 2021)

Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection. - HanQin Cai; Jialin Liu 003; Wotao Yin (NeurIPS, 2021)

Hyperparameter Tuning is All You Need for LISTA. - Xiaohan Chen; Jialin Liu 003; Zhangyang Wang; Wotao Yin (NeurIPS, 2021)

Multilevel Optimal Transport: A Fast Approximation of Wasserstein-1 Distances. - Jialin Liu 003; Wotao Yin; Wuchen Li; Yat Tin Chow (SIAM J. Sci. Comput., 2021)

Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration. - Jian Kang 005; Danfeng Hong; Jialin Liu 003; Gerald Baier; Naoto Yokoya; Begüm Demir (IEEE Trans. Neural Networks Learn. Syst., 2021)

On Representing Linear Programs by Graph Neural Networks. - Ziang Chen; Jialin Liu 003; Xinshang Wang; Jianfeng Lu 001; Wotao Yin (CoRR, 2022)

On Representing Mixed-Integer Linear Programs by Graph Neural Networks. - Ziang Chen; Jialin Liu 003; Xinshang Wang; Jianfeng Lu 001; Wotao Yin (CoRR, 2022)

Learning to Optimize: A Primer and A Benchmark. - Tianlong Chen 001; Xiaohan Chen; Wuyang Chen 001; Howard Heaton; Jialin Liu 003; Zhangyang Wang; Wotao Yin (J. Mach. Learn. Res., 2022)

Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration. - Xiang Ding; Jian Kang 005; Zhe Zhang 026; Yan Huang 018; Jialin Liu 003; Naoto Yokoya (IEEE Trans. Geosci. Remote. Sens., 2022)

On Representing Linear Programs by Graph Neural Networks. - Ziang Chen; Jialin Liu 003; Xinshang Wang; Wotao Yin (ICLR, 2023)

On Representing Mixed-Integer Linear Programs by Graph Neural Networks. - Ziang Chen; Jialin Liu 003; Xinshang Wang; Wotao Yin (ICLR, 2023)

Towards Constituting Mathematical Structures for Learning to Optimize. - Jialin Liu 003; Xiaohan Chen; Zhangyang Wang; Wotao Yin; HanQin Cai (ICML, 2023)

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee. - Haoyu Wang 004; Jialin Liu 003; Xiaohan Chen; Xinshang Wang; Pan Li 005; Wotao Yin (CoRR, 2023)

Rethinking the Capacity of Graph Neural Networks for Branching Strategy. - Ziang Chen; Jialin Liu 003; Xiaohan Chen; Xinshang Wang; Wotao Yin (NeurIPS, 2024)

Learning to optimize: A tutorial for continuous and mixed-integer optimization. - Xiaohan Chen; Jialin Liu 003; Wotao Yin (CoRR, 2024)

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs. - Ziang Chen; Xiaohan Chen; Jialin Liu 003; Xinshang Wang; Wotao Yin (CoRR, 2024)

S3Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching. - Xue Wang 010; Tian Zhou 004; Jianqing Zhu; Jialin Liu 003; Kun Yuan 001; Tao Yao; Wotao Yin; Rong Jin 001; HanQin Cai (CoRR, 2024)

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration. - Xiang Ding; Jian Kang 005; Yusong Bai; Anping Zhang; Jialin Liu 003; Naoto Yokoya (IEEE Trans. Computational Imaging, 2024)

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee. - Haoyu Peter Wang; Jialin Liu 003; Xiaohan Chen; Xinshang Wang; Pan Li 005; Wotao Yin (Trans. Mach. Learn. Res., 2024)

S$^ text{3}$Attention: Improving Long Sequence Attention With Smoothed Skeleton Sketching. - Xue Wang 010; Tian Zhou 004; Jianqing Zhu; Jialin Liu 003; Kun Yuan 001; Tao Yao; Wotao Yin; Rong Jin 001; HanQin Cai (IEEE J. Sel. Top. Signal Process., 2024)

Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery. - HanQin Cai; Chandra Kundu; Jialin Liu 003; Wotao Yin (CoRR, 2025)