Cheng, James

45 publications

NeurIPS 2025 A Signed Graph Approach to Understanding and Mitigating Oversmoothing Jiaqi Wang, Xinyi Wu, James Cheng, Yifei Wang

ICLR 2025 BrainOOD: Out-of-Distribution Generalizable Brain Network Analysis Jiaxing Xu, Yongqiang Chen, Xia Dong, Mengcheng Lan, Tiancheng Huang, Qingtian Bian, James Cheng, Yiping Ke

TMLR 2025 DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng

ICML 2025 Hierarchical Graph Tokenization for Molecule-Language Alignment Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian

ICLRW 2025 On the Language of Thoughts in Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang

NeurIPS 2025 Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models Jiaqi Wang, Kevin Qinghong Lin, James Cheng, Mike Zheng Shou

NeurIPS 2024 Discovery of the Hidden World with Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

MLJ 2024 Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping Chenhan Jin, Kaiwen Zhou, Bo Han, James Cheng, Tieyong Zeng

AAAI 2024 Enhancing Evolving Domain Generalization Through Dynamic Latent Representations Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng

ICLR 2024 Enhancing Neural Subset Selection: Integrating Background Information into Set Representations Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng

NeurIPS 2024 HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection Binghui Xie, Yixuan Wang, Yongqiang Chen, Kaiwen Zhou, Yu Li, Wei Meng, James Cheng

ICML 2024 How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen, Yatao Bian, Bo Han, James Cheng

ICMLW 2024 Improving Graph-Language Alignment with Hierarchical Graph Tokenization Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian

ICLRW 2024 Interpretable and Generalizable Graph Learning via Subgraph Multilinear Extension Yongqiang Chen, Yatao Bian, Bo Han, James Cheng

TMLR 2023 Calibrating and Improving Graph Contrastive Learning Ma Kaili, Garry Yang, Han Yang, Yongqiang Chen, James Cheng

NeurIPS 2023 Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng

ICLR 2023 Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Ma Kaili, Han Yang, Peilin Zhao, Bo Han, James Cheng

NeurIPSW 2023 Towards Out-of-Distribution Generalizable Predictions of Chemical Kinetic Properties Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong

NeurIPS 2023 Understanding and Improving Feature Learning for Out-of-Distribution Generalization Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng

AISTATS 2022 Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums Kaiwen Zhou, Lai Tian, Anthony Man-Cho So, James Cheng

NeurIPSW 2022 Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou, Anthony Man-Cho So, James Cheng

NeurIPS 2022 Exact Shape Correspondence via 2D Graph Convolution Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Ma Kaili, Bo Han, Bo Li, James Cheng

ICML 2022 Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

ICMLW 2022 Invariance Principle Meets Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

NeurIPS 2022 Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

ICLR 2022 Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Yongqiang Chen, Han Yang, Yonggang Zhang, Ma Kaili, Tongliang Liu, Bo Han, James Cheng

AAAI 2021 Rethinking Graph Regularization for Graph Neural Networks Han Yang, Kaili Ma, James Cheng

UAI 2020 Amortized Nesterov’s Momentum: A Robust Momentum and Its Application to Deep Learning Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng

NeurIPS 2020 Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates Kaiwen Zhou, Anthony Man-Cho So, James Cheng

ICLR 2020 Measuring and Improving the Use of Graph Information in Graph Neural Networks Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang

AAAI 2020 Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search Xinyan Dai, Xiao Yan, Kelvin Kai Wing Ng, Jiu Liu, James Cheng

IJCAI 2020 Tight Convergence Rate of Gradient Descent for Eigenvalue Computation Qinghua Ding, Kaiwen Zhou, James Cheng

AAAI 2020 Understanding and Improving Proximity Graph Based Maximum Inner Product Search Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang

AISTATS 2019 Direct Acceleration of SAGA Using Sampled Negative Momentum Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo

ICML 2018 A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou, Fanhua Shang, James Cheng

ACML 2018 ASVRG: Accelerated Proximal SVRG Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin

AISTATS 2018 Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida

NeurIPS 2018 Norm-Ranging LSH for Maximum Inner Product Search Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng

NeurIPS 2017 Accelerated First-Order Methods for Geodesically Convex Optimization on Riemannian Manifolds Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao

AAAI 2017 Accelerated Variance Reduced Stochastic ADMM Yuanyuan Liu, Fanhua Shang, James Cheng

AAAI 2016 Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization Fanhua Shang, Yuanyuan Liu, James Cheng

AISTATS 2016 Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization Fanhua Shang, Yuanyuan Liu, James Cheng

NeurIPS 2014 Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng

AAAI 2014 Generalized Higher-Order Tensor Decomposition via Parallel ADMM Fanhua Shang, Yuanyuan Liu, James Cheng

UAI 2014 Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng