A Fixed-Point Algorithm for Nonnegative Independent Component Analysis. - Zhenwei Shi; Xueyan Tan; Zhanxing Zhu; Zhiguo Jiang 001 (ICNC (2), 2009)

Quadratic Form Innovation to Blind Source Separation. - Zhenwei Shi; Zhanxing Zhu; Xueyan Tan; Zhiguo Jiang 001 (ICNC (2), 2009)

Automatic Rank Determination in Projective Nonnegative Matrix Factorization. - Zhirong Yang; Zhanxing Zhu; Erkki Oja (LVA/ICA, 2010)

A method of automatically estimating the regularization parameter for Non-negative Matrix Factorization. - Dalong Cheng; Zhenwei Shi; Xueyan Tan; Zhanxing Zhu; Zhiguo Jiang 001 (ICNC, 2010)

Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters. - Zhenwei Shi; Zhenyu An; Xueyan Tan; Zhanxing Zhu; Zhiguo Jiang 001 (ICNC, 2011)

Multiplicative Updates for Learning with Stochastic Matrices. - Zhanxing Zhu; Zhirong Yang; Erkki Oja (SCIA, 2013)

Supervised Distance Preserving Projections. - Zhanxing Zhu; Timo Similä; Francesco Corona (Neural Process. Lett., 2013)

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

Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. - Amos J. Storkey; Zhanxing Zhu; Jinli Hu (ECML/PKDD (1), 2015)

Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. - Zhanxing Zhu; Amos J. Storkey (ECML/PKDD (1), 2015)

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

Integrating local information for inference and optimization in machine learning. - Zhanxing Zhu (2016)

Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix. - Bingfeng Luo; Yansong Feng 002; Zheng Wang 001; Zhanxing Zhu; Songfang Huang; Rui Yan 001; Dongyan Zhao 001 (ACL (1), 2017)

Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. - Nanyang Ye 001; Zhanxing Zhu; Rafal Mantiuk (NIPS, 2017)

Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. - Lei Wu; Zhanxing Zhu; Weinan E (CoRR, 2017)

Langevin Dynamics with Continuous Tempering for High-dimensional Non-convex Optimization. - Nanyang Ye 001; Zhanxing Zhu; Rafal K. Mantiuk (CoRR, 2017)

Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. - Bing Yu; Haoteng Yin; Zhanxing Zhu (CoRR, 2017)

A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems. - Cheng Feng 004; Tingting Li 001; Zhanxing Zhu; Deeph Chana (CoRR, 2017)

Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach. - Guixin Ye; Zhanyong Tang; Dingyi Fang; Zhanxing Zhu; Yansong Feng 002; Pengfei Xu 003; Xiaojiang Chen; Zheng Wang 001 (CCS, 2018)

Stochastic Fractional Hamiltonian Monte Carlo. - Nanyang Ye 001; Zhanxing Zhu (IJCAI, 2018)

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

SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction. - Huizhuo Yuan; Jinzhu Jia; Zhanxing Zhu (ISBI, 2018)

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. - Rui Luo 001; Jianhong Wang; Yaodong Yang 001; Jun Wang 012; Zhanxing Zhu (NeurIPS, 2018)

Reinforced Continual Learning. - Ju Xu; Zhanxing Zhu (NeurIPS, 2018)

Bayesian Adversarial Learning. - Nanyang Ye 001; Zhanxing Zhu (NeurIPS, 2018)

Understanding and Enhancing the Transferability of Adversarial Examples. - Lei Wu; Zhanxing Zhu; Cheng Tai; Weinan E (CoRR, 2018)

The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. - Zhanxing Zhu; Jingfeng Wu; Bing Yu; Lei Wu; Jinwen Ma (CoRR, 2018)

Neural Control Variates for Variance Reduction. - Zhanxing Zhu; Ruosi Wan; Mingjun Zhong (CoRR, 2018)

Tangent-Normal Adversarial Regularization for Semi-supervised Learning. - Bing Yu; Jingfeng Wu; Zhanxing Zhu (CoRR, 2018)

SpHMC: Spectral Hamiltonian Monte Carlo. - Haoyi Xiong; Kafeng Wang; Jiang Bian 003; Zhanxing Zhu; Cheng-Zhong Xu 001; Zhishan Guo; Jun Huan (AAAI, 2019)

Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. - Bing Yu; Jingfeng Wu; Jinwen Ma; Zhanxing Zhu (CVPR, 2019)

Towards Making Deep Transfer Learning Never Hurt. - Ruosi Wan; Haoyi Xiong; Xingjian Li 002; Zhanxing Zhu; Jun Huan (ICDM, 2019)

Interpreting Adversarially Trained Convolutional Neural Networks. - Tianyuan Zhang; Zhanxing Zhu (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 (ICML, 2019)

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. - Dinghuai Zhang; Tianyuan Zhang; Yiping Lu 001; Zhanxing Zhu; Bin Dong 001 (NeurIPS, 2019)

How Question Generation Can Help Question Answering over Knowledge Base. - Sen Hu; Lei Zou 001; Zhanxing Zhu (NLPCC (1), 2019)

Neural Control Variates for Monte Carlo Variance Reduction. - Ruosi Wan; Mingjun Zhong; Haoyi Xiong; Zhanxing Zhu (ECML/PKDD (2), 2019)

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. - Ke Sun 013; Zhouchen Lin; Hantao Guo; Zhanxing Zhu (PRCV (1), 2019)

Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent. - Wenqing Hu; Zhanxing Zhu; Haoyi Xiong; Jun Huan (CoRR, 2019)

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (CoRR, 2019)

3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting. - Bing Yu; Mengzhang Li; Jiyong Zhang; Zhanxing Zhu (CoRR, 2019)

ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling. - Bing Yu; Haoteng Yin; Zhanxing Zhu (CoRR, 2019)

Bayesian Optimized Continual Learning with Attention Mechanism. - Ju Xu; Jin Ma; Zhanxing Zhu (CoRR, 2019)

On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks. - Bing Yu; Junzhao Zhang; Zhanxing Zhu (CoRR, 2019)

Differentiable Neural Architecture Search via Proximal Iterations. - Quanming Yao; Ju Xu; Wei-Wei Tu; Zhanxing Zhu (CoRR, 2019)

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. - Jingfeng Wu; Wenqing Hu; Haoyi Xiong; Jun Huan; Zhanxing Zhu (CoRR, 2019)

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. - Ke Sun 013; Zhouchen Lin; Zhanxing Zhu (AAAI, 2020)

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

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

Simplifying Graph Attention Networks with Source-Target Separation. - Hantao Guo; Rui Yan 001; Yansong Feng 002; Xuesong Gao; Zhanxing Zhu (ECAI, 2020)

On Breaking Deep Generative Model-based Defenses and Beyond. - Yanzhi Chen; Renjie Xie; Zhanxing Zhu (ICML, 2020)

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. - Baifeng Shi; Dinghuai Zhang; Qi Dai 001; Zhanxing Zhu; Yadong Mu; Jingdong Wang 001 (ICML, 2020)

On the Noisy Gradient Descent that Generalizes as SGD. - Jingfeng Wu; Wenqing Hu; Haoyi Xiong; Jun Huan; Vladimir Braverman; Zhanxing Zhu (ICML, 2020)

Learning to Search Efficient DenseNet with Layer-wise Pruning. - Xuanyang Zhang; Hao Liu; Zhanxing Zhu; Zenglin Xu (IJCNN, 2020)

Automatic Data Augmentation for 3D Medical Image Segmentation. - Ju Xu; Mengzhang Li; Zhanxing Zhu (MICCAI (1), 2020)

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

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. - Dinghuai Zhang; Mao Ye 006; Chengyue Gong; Zhanxing Zhu; Qiang Liu 001 (NeurIPS, 2020)

Global Robustness Verification Networks. - Weidi Sun; Yuteng Lu; Xiyue Zhang 001; Zhanxing Zhu; Meng Sun 002 (CoRR, 2020)

Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data. - Bing Yu; Ke Sun 013; He Wang 002; Zhouchen Lin; Zhanxing Zhu (CoRR, 2020)

Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay. - Ruosi Wan; Zhanxing Zhu; Xiangyu Zhang 005; Jian Sun 001 (CoRR, 2020)

Adversarial attacks on Faster R-CNN object detector. - Yutong Wang 001; Kunfeng Wang; Zhanxing Zhu; Feiyue Wang 001 (Neurocomputing, 2020)

Using Generative Adversarial Networks to Break and Protect Text Captchas. - Guixin Ye; Zhanyong Tang; Dingyi Fang; Zhanxing Zhu; Yansong Feng 002; Pengfei Xu 003; Xiaojiang Chen; Jungong Han; Zheng Wang 001 (ACM Trans. Priv. Secur., 2020)

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

Amata: An Annealing Mechanism for Adversarial Training Acceleration. - Nanyang Ye 001; Qianxiao Li; Xiao-Yun Zhou 001; Zhanxing Zhu (AAAI, 2021)

Adversarial Invariant Learning. - Nanyang Ye 001; Jingxuan Tang; Huayu Deng; Xiao-Yun Zhou 001; Qianxiao Li; Zhenguo Li; Guang-Zhong Yang; Zhanxing Zhu (CVPR, 2021)

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

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. - Ke Sun 013; Zhanxing Zhu; Zhouchen Lin (ICLR, 2021)

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

Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay. - Ruosi Wan; Zhanxing Zhu; Xiangyu Zhang 005; Jian Sun 001 (NeurIPS, 2021)

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. - Quanshi Zhang; Tian Han 001; Lixin Fan; Zhanxing Zhu; Hang Su; Ying Nian Wu; Jie Ren 018; Hao Zhang 063 (CoRR, 2021)

Sampling Sparse Representations with Randomized Measurement Langevin Dynamics. - Kafeng Wang; Haoyi Xiong; Jiang Bian 003; Zhanxing Zhu; Qian Gao; Zhishan Guo; Cheng-Zhong Xu 001; Jun Huan; Dejing Dou (ACM Trans. Knowl. Discov. Data, 2021)

Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling. - He Wang 002; Edmond S. L. Ho; Hubert P. H. Shum; Zhanxing Zhu (IEEE Trans. Vis. Comput. Graph., 2021)

Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics. - Deshan Gong; Zhanxing Zhu; Andrew J. Bulpitt; He Wang 002 (ICLR, 2022)

Implicit Bias of Adversarial Training for Deep Neural Networks. - Bochen Lv; Zhanxing Zhu (ICLR, 2022)

Adaptive Progressive Continual Learning. - Ju Xu; Jin Ma; Xuesong Gao; Zhanxing Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

GrOD: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training. - Haoyi Xiong; Ruosi Wan; Jian Zhao 006; Zeyu Chen; Xingjian Li 002; Zhanxing Zhu; Jun Huan (ACM Trans. Knowl. Discov. Data, 2022)

Introduction to Data Science - Gaoyan Ou; Zhanxing Zhu; Bin Dong 001; Weinan E; Binyang Li; Shumin Shi (WorldScientific, 2023)

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. - Ke Sun 013; Bing Yu; Zhouchen Lin; Zhanxing Zhu (ACML, 2023)

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

Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling. - Ting Li; Jianguo Li; Zhanxing Zhu (NeurIPS, 2023)

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

MonoFlow: Rethinking Divergence GANs via the Perspective of Differential Equations. - Mingxuan Yi; Zhanxing Zhu; Song Liu (CoRR, 2023)

Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability. - Haoyi Xiong; Xuhong Li 002; Boyang Yu; Zhanxing Zhu; Dongrui Wu; Dejing Dou (CoRR, 2023)

An Annealing Mechanism for Adversarial Training Acceleration. - Nanyang Ye 001; Qianxiao Li; Xiao-Yun Zhou 001; Zhanxing Zhu (IEEE Trans. Neural Networks Learn. Syst., 2023)

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, 2024)

Functional Relation Field: A Model-Agnostic Framework for Multivariate Time Series Forecasting. - Ting Li; Bing Yu; Jianguo Li; Zhanxing Zhu (Artif. Intell., 2024)

Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer. - Haoyi Xiong; Xuhong Li 002; Boyang Yu; Dongrui Wu; Zhanxing Zhu; Dejing Dou (Mach. Learn. Sci. Technol., 2024)

Effects of Momentum in Implicit Bias of Gradient Flow for Diagonal Linear Networks. - Bochen Lyu; He Wang; Zheng Wang 001; Zhanxing Zhu (AAAI, 2025)

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

A Solvable Attention for Neural Scaling Laws. - Bochen Lyu; Di Wang; Zhanxing Zhu (ICLR, 2025)

ACU: Analytic Continual Unlearning for Efficient and Exact Forgetting with Privacy Preservation. - Jianheng Tang 001; Huiping Zhuang; Di Fang 004; Jiaxu Li; Feijiang Han; Yajiang Huang; Kejia Fan; Leye Wang; Zhanxing Zhu; Shanghang Zhang; Houbing Herbert Song; Yunhuai Liu (CoRR, 2025)

AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data. - Jianheng Tang 001; Huiping Zhuang; Jingyu He; Run He; Jingchao Wang; Kejia Fan; Anfeng Liu; Tian Wang 001; Leye Wang; Zhanxing Zhu; Shanghang Zhang; Houbing Herbert Song; et al. (CoRR, 2025)

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids. - Shilong Tao; Zhe Feng; Haonan Sun; Zhanxing Zhu; Yunhuai Liu (CoRR, 2025)

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems. - Shilong Tao; Zhe Feng; Haonan Sun; Zhanxing Zhu; Yunhuai Liu (CoRR, 2025)

D2R: dual regularization loss with collaborative adversarial generation for model robustness. - Zhenyu Liu; Huizhi Liang; Rajiv Ranjan 001; Zhanxing Zhu; Václav Snásel; Varun Ojha 001 (CoRR, 2025)

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

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction. - Ruochen Li 002; Tanqiu Qiao; Stamos Katsigiannis; Zhanxing Zhu; Hubert P. H. Shum (IEEE Trans. Circuits Syst. Video Technol., 2025)

Analyzing the Implicit Bias of Adversarial Training From a Generalized Margin Perspective. - Bochen Lyu; Zhanxing Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2025)