Zhu, Zhanxing

42 publications

ICLR 2025 A Solvable Attention for Neural Scaling Laws Bochen Lyu, Di Wang, Zhanxing Zhu

ICLR 2025 DyCAST: Learning Dynamic Causal Structure from Time Series Yue Cheng, Bochen Lyu, Weiwei Xing, Zhanxing Zhu

AAAI 2025 Effects of Momentum in Implicit Bias of Gradient Flow for Diagonal Linear Networks Bochen Lyu, He Wang, Zheng Wang, Zhanxing Zhu

NeurIPS 2025 Heavy-Ball Momentum Method in Continuous Time and Discretization Error Analysis Bochen Lyu, Xiaojing Zhang, Fangyi Zheng, He Wang, Zheng Wang, Zhanxing Zhu

ICML 2025 Unisoma: A Unified Transformer-Based Solver for Multi-Solid Systems Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu

NeurIPS 2024 Memory-Efficient Gradient Unrolling for Large-Scale Bi-Level Optimization Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi

NeurIPS 2023 Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network Bochen Lyu, Zhanxing Zhu

ICML 2023 MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows Mingxuan Yi, Zhanxing Zhu, Song Liu

NeurIPS 2023 Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling Ting Li, Jianguo Li, Zhanxing Zhu

ACML 2023 Patch-Level Neighborhood Interpolation: A General and Effective Graph-Based Regularization Strategy Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu

ICLR 2022 Fine-Grained Differentiable Physics: A Yarn-Level Model for Fabrics Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang

ICLR 2022 Implicit Bias of Adversarial Training for Deep Neural Networks Bochen Lv, Zhanxing Zhu

ICLR 2021 AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Ke Sun, Zhanxing Zhu, Zhouchen Lin

CVPR 2021 Adversarial Invariant Learning Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu

AAAI 2021 Amata: An Annealing Mechanism for Adversarial Training Acceleration Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu

ICLR 2021 Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

ICML 2021 Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama

AAAI 2021 Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting Mengzhang Li, Zhanxing Zhu

NeurIPS 2021 Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network Using SGD and Weight Decay Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun

NeurIPS 2020 Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

AAAI 2020 Efficient Neural Architecture Search via Proximal Iterations Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu

ICML 2020 Informative Dropout for Robust Representation Learning: A Shape-Bias Perspective Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

NeurIPS 2020 Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher Guangda Ji, Zhanxing Zhu

AAAI 2020 Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes Ke Sun, Zhouchen Lin, Zhanxing Zhu

ICML 2020 On Breaking Deep Generative Model-Based Defenses and Beyond Yanzhi Chen, Renjie Xie, Zhanxing Zhu

ICML 2020 On the Noisy Gradient Descent That Generalizes as SGD Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

ACML 2020 Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks Lei Wu, Zhanxing Zhu

ICML 2019 Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang, Zhanxing Zhu

ECML-PKDD 2019 Neural Control Variates for Monte Carlo Variance Reduction Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu

AAAI 2019 SpHMC: Spectral Hamiltonian Monte Carlo Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan

ICML 2019 The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

NeurIPS 2019 You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

NeurIPS 2018 Bayesian Adversarial Learning Nanyang Ye, Zhanxing Zhu

NeurIPS 2018 Reinforced Continual Learning Ju Xu, Zhanxing Zhu

IJCAI 2018 Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting Bing Yu, Haoteng Yin, Zhanxing Zhu

IJCAI 2018 Stochastic Fractional Hamiltonian Monte Carlo Nanyang Ye, Zhanxing Zhu

NeurIPS 2018 Thermostat-Assisted Continuously-Tempered Hamiltonian Monte Carlo for Bayesian Learning Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu

NeurIPS 2017 Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk

AAAI 2016 Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems Zhanxing Zhu, Amos J. Storkey

ECML-PKDD 2015 Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems Zhanxing Zhu, Amos J. Storkey

ECML-PKDD 2015 Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets Amos J. Storkey, Zhanxing Zhu, Jinli Hu

NeurIPS 2015 Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey