Jialin Liu
Home
Papers

Publication List

my Google Scholar page

The mark * denotes equal contribution or alphabetical order; + denotes corresponding author

Papers that I served as (one of) the primary authors or the corresponding author

  • Jialin Liu*, Xiaohan Chen*, Zhangyang Wang, Wotao Yin, HanQin Cai. “Towards Constituting Mathematical Structures for Learning to Optimize.” International Conference on Machine Learning (ICML), 2023. (pdf)

  • Ziang Chen, Jialin Liu+, Xinshang Wang, Jianfeng Lu, Wotao Yin. “On Representing Linear Programs by Graph Neural Networks.” International Conference on Learning Representations (ICLR), 2023. (pdf, software, Spotlight paper)

  • Ziang Chen, Jialin Liu+, Xinshang Wang, Jianfeng Lu, Wotao Yin. “On Representing Mixed-Integer Linear Programs by Graph Neural Networks.” International Conference on Learning Representations (ICLR), 2023. (pdf, software)

  • Tianlong Chen*, Wuyang Chen*, Xiaohan Chen*, Howard Heaton*, Jialin Liu*, Zhangyang Wang*, Wotao Yin*. “Learning to optimize: A primer and a benchmark.” Journal of Machine Learning Research (JMLR), 2022.

  • HanQin Cai*, Jialin Liu*, Wotao Yin*. “Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection.” Advances in Neural Information Processing Systems (NeurIPS), 2021. (pdf)

  • Xiaohan Chen*, Jialin Liu*, Zhangyang Wang, Wotao Yin. “Hyperparameter Tuning is All You Need for LISTA.” Advances in Neural Information Processing Systems (NeurIPS), 2021.

  • Jialin Liu, Wotao Yin, Wuchen Li, Yat Tin Chow, “Multilevel Optimal Transport: a Fast Approximation of Wasserstein-1 distances.” SIAM Journal on Scientific Computing, 2021. (pdf, software)

  • Jialin Liu*, Xiaohan Chen*, Zhangyang Wang, Wotao Yin. “ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA.” International Conference on Learning Representations (ICLR), 2019. (pdf, poster, software, Top-rated paper)

  • Xiaohan Chen*, Jialin Liu*, Zhangyang Wang, Wotao Yin. “Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds.” Advances in Neural Information Processing Systems (NeurIPS), 2018. (pdf, slides, software, Spotlight paper)

  • Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, and Wotao Yin. “First- and Second-Order Methods for Online Convolutional Dictionary Learning.” SIAM Journal on Imaging Sciences, 2018. (pdf, slides, software)

  • Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, and Wotao Yin. “Online Convolutional Dictionary Learning.” IEEE International Conference on Image Processing (ICIP), 2017. (pdf, slides, software, Best Student Paper)

  • Jialin Liu, Yuantao Gu, and Mengdi Wang. “Averaging random projection: A fast online solution for large-scale constrained stochastic optimization.” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015.

Other papers

  • Xiang Ding, Jian Kang, Zhe Zhang, Yan Huang, Jialin Liu, Naoto Yokoya. “Coherence-guided complex convolutional sparse coding for interferometric phase restoration.” IEEE Transactions on Geoscience and Remote Sensing, 2022.

  • Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang. “Learning A Minimax Optimizer: A Pilot Study.” International Conference on Learning Representations (ICLR), 2021. (pdf)

  • Jian Kang, Danfeng Hong, Jialin Liu, Gerald Baier, Naoto Yokoya, Begum Demir. “Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration.” IEEE Transactions on Neural Networks and Learning Systems, 2020. (pdf)

  • Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin. “Plug-and-Play Methods Provably Converge with Properly Trained Denoisers.” International Conference on Machine Learning (ICML), 2019. (pdf, software)