Liu, Jialin

19 publications

ECML-PKDD 2025 Community-Aware Graph Transformer: Preserving Community Semantics for Effective Global Aggregation Yutai Duan, Jie Liu, Jianhua Wu, Jialin Liu

ICML 2025 Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin

IJCAI 2025 LiBOG: Lifelong Learning for Black-Box Optimizer Generation Jiyuan Pei, Yi Mei, Jialin Liu, Mengjie Zhang

CVPR 2025 StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation Shangjin Zhai, Zhichao Ye, Jialin Liu, Weijian Xie, Jiaqi Hu, Zhen Peng, Hua Xue, Danpeng Chen, Xiaomeng Wang, Lei Yang, Nan Wang, Haomin Liu, Guofeng Zhang

TMLR 2024 DIG-MILP: A Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee Haoyu Peter Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin

ICLR 2024 Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation Ziqi Wang, Chengpeng Hu, Jialin Liu, Xin Yao

NeurIPS 2024 Rethinking the Capacity of Graph Neural Networks for Branching Strategy Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, Wotao Yin

ICLR 2023 On Representing Linear Programs by Graph Neural Networks Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin

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

ICML 2023 Towards Constituting Mathematical Structures for Learning to Optimize Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, Hanqin Cai

JMLR 2022 Learning to Optimize: A Primer and a Benchmark Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin

ICLR 2021 Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, Arnold Overwijk

NeurIPS 2021 Hyperparameter Tuning Is All You Need for LISTA Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin

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

ICLR 2021 Learning a Minimax Optimizer: A Pilot Study Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang

ICLR 2019 ALISTA: Analytic Weights Are as Good as Learned Weights in LISTA Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin

NeurIPS 2019 Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood

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

NeurIPS 2018 Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin