Recent Manuscripts and Publications (since 2003)

• Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, and Zhihua Zhang. Estimation and Inference in Distributional Reinforcement Learning, 2023. https://arxiv.org/abs/2309.17262

• Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, and Zhihua Zhang. Robust Markov Decision Processes without Model Estimation. https://arxiv.org/pdf/2302.01248.pdf

• Yuhang Zhang, Yue Liu, and Zhihua Zhang. Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption, 2023. https://arxiv.org/abs/2309.16409

• Xiang Li, Jiadong Liang, Xinyun Chen, and Zhihua Zhang. Stochastic Approximation MCMC, Online Inference, and Applications in Optimization of Queueing Systems, 2023. http://arxiv.org/abs/2309.09545

• Jiadong Liang, Yuze Han, Xiang Li, and Zhihua Zhang. Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach, 2023. http://arxiv.org/abs/2304.12953

• Xiang Li, Jiadong Liang,and Zhihua Zhang. Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data, 2023. http://arxiv.org/abs/2302.07690

• Haishan Ye, Daochao Lin, Xiangyu Chang, and Zhihua Zhang. Towards Explicit Superlinear Convergence Rate for SR1, Mathematical Programming, 199: 1273-1303, 2023.

• Dachao Lin, Yuze Han, Haishan Ye, and Zhihua Zhang. Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS, 2023.

• Boya Zhang, Weijian Luo, and Zhihua Zhang. Enhancing Adversarial Robustness via Score-Based Optimization. NeurIPS, 2023.

• Weijian Luo, Boya Zhang, and Zhihua Zhang. Entropy-based Training Methods for Scalable Neural Implicit Sampler. NeurIPS, 2023.

• Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li,and Zhihua Zhang. Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models. NeurIPS, 2023.

• Chuhan Xie, Wenhao Yang, and Zhihua Zhang. Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes. ICML, 2023.

• Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, and Michael Jordan. A Statistical Analysis of Polyak-Ruppert Averaged Q-Leaning. AISTATS, 2023.

• 王树森、黎彧君、张志华, 深度强化学习，人民邮电出版社，2022.

• Hao Cheng and Zhihua Zhang. Con-NAT: Contrastive Non-autoregressive Neural Machine Translation. Findings of EMNLP, 2022.

• Jiadong Liang, Yuze Han, Xiang Li, and Zhihua Zhang. Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. NeurIPS 2022 (Oral).

• Liangyu Zhang, Yang Peng, Wenhao Yang, and Zhihua Zhang. Semi-infinitely Constrained Markov Decision Processes. NeurIPS 2022.

• Chuhan Xie and Zhihua Zhang. A Statistical Online Inference Approach in Averaged Stochastic Approximation. NeurIPS, 2022.

• Siyun Lin, Yuze Han, Xiang Li, and Zhihua Zhang. Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. NeurIPS, 2022.

• Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, and Michael Jordan. Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient, 2022. https://arxiv.org/abs/2112.14582

• Dachao Lin, Haishan Ye, and Zhihua Zhang. Explicit Superlinear Convergence Rates of Broyden's Methods in Nonlinear Equations. https://arxiv.org/abs/2109.01974

• Wenhao Yang, Liangyu Zhang, and Zhihua Zhang. Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics. The Annals of Statistics, 50(6), 3223-3248, 2022. https://arxiv.org/abs/2105.03863

• Daochao Lin, Haishan Ye, and Zhihua Zhang. Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. Journal of Machine Learning Research (JMLR), 23: 1-40, 2022.

• Daochao Lin, Ruoyu Sun, and Zhihua Zhang. On the Landscape of One-hidden-layer Sparse Networks and Beyond. Artificial Intelligence , 309 (2022) 103739.

• Kun Chen, Daochao Lin, and Zhihua Zhang. On Non-local Convergence Analysis of Deep Linear Networks. ICML, 2022.

• Xiang Li, Jiadong Liang, Xiangyu Chang, and Zhihua Zhang. Statistical Estimation and Online Inference via Local SGD. 35th Annual Conference on Learning Theory, COLT 2022. https://arxiv.org/abs/2109.01326

• Hao Cheng and Zhihua Zhang. MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation. Findings of ACL 2022.

• Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, and Zhihua Zhang. Improving Model Training with Multi-fidelity Hyperparameter Evaluation. Fifth Conference on Machine Learning and Systems, Proceedings of the 5th MLSys Conference 2022.

• Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang and Zhihua Zhang. Federated Reinforcement Learning with Environment Heterogeneity. AISTATS, 2022.

• Dachao Lin, Haishan Ye, and Zhihua Zhang. Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS, 2021.

• Dachao Lin, Ruoyu Sun, and Zhihua Zhang. Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. NeurIPS, 2021.

• Xiang Li and Zhihua Zhang. Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications, 2021. https://arxiv.org/abs/2101.01505

• Yuze Han, Guangzeng Xie, and Zhihua Zhang. Lower Complexity Bounds of Finite-Sum Optimization Problems:The Results and Construction, 2021. https://arxiv.org/abs/2103.08280

• Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang and Zhihua Zhang. Privacy-Preserving Distributed SVD via Federated Power, 2021. https://arxiv.org/abs/2103.00704

• Haishan Ye, Luo Luo, and Zhihua Zhang. Approximate Newton Methods. Journal of Machine Learning Research (JMLR), 22: 1-41, 2021.

• Xiang Li, Kun Chen, Shusen Wang, and Zhihua Zhang. Communication-Efficient Distributed SVD via Local Power Iterations. ICML 2021 , 2021.

• Yuekai Zhao, Li Dong, Yelong Shen, Zhihua Zhang, Furu Wei, and Weizhu Chen. Memory-efficient differentiable transformer architecture search. The ACL-IJCNLP 2021 Findings , 2021.

• Yuekai Zhao, Shuchang Zhou, and Zhihua Zhang. Multi-split Reversible Transformers Can Enhance Neural MachineTranslation. ECAI, 2021.

• Chao Tian, Yifei Wang, Hao Cheng, Yijiang Lian, and Zhihua Zhang. Train Once, and Decode As You Like. COLING, 2020.

• Yuekai Zhao, Haoran Zhang, Shuchang Zhou, and Zhihua Zhang. Active Learning Approaches to Enhancing Neural Machine Translation. Findings of EMNLP, 2020.

• Haishan Ye, Luo Luo, and Zhihua Zhang. Nesterov's Acceleration for Approximate Newton. Journal of Machine Learning Research (JMLR), 21(142):1-37, 2020.

• Haishan Ye, Luo Luo, and Zhihua Zhang. Accelerated Proximal Sub-Sampled Newton Method. IEEE Transactions on Neural Networks and Learning Systems, 2020.

• Guangzeng Xie, Luo Luo, Yijiang Lian, and Zhihua Zhang. Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML, 2020.

• Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, and Yong Yu. Efficient Spectrum-Revealing CUR Matrix Decomposition. AISTATS, 2020.

• Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. On the Convergence of FedAvg on Non-IID Data. ICLR, 2020.

• Xiang Li, Shusen Wang, and Zhihua Zhang. Do Subsampled Newton Methods Work for High-Dimensional Data? The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020.

• Xiang Li, Wenhao Yang, and Zhihua Zhang. A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. NeurIPS, 2019.

• Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang. Lipschitz Generative Adversarial Nets. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97:7584-7593, 2019.

• Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, and Tong Zhang. Robust frequent directions with application in online learning. Journal of Machine Learning Research (JMLR), 20:1-41, 2019.

• Haishan Ye, Guangzeng Xie, Luo Luo, and Zhihua Zhang. Fast stochastic second-order methods logarithmic in condition number. Pattern Recognition, 88: 629-642, 2019.

• Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, and Jian Pei. Sketched Follow-The-Regularized-Leader for Online Factorization Machine. SIGKDD , 2018.

• Shenjian Zhao and Zhihua Zhang. Attention-via-Attention Neural Machine Translation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI'18) , 2018.

• Haishan Ye, Luo Luo, and Zhihua Zhang. Approximate Newton methods and their local convergence. The 34th International Conference on Machine Learning, 2017. Sydney, Australia. August 6th-11th, 2017.

• Tianfan Fu, Luo Luo, and Zhihua Zhang. CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017) , 2017.

• Zihao Chen, Luo Luo, and Zhihua Zhang. Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17) , 2017.

• Zhihua Zhang. The Singular Value Decomposition, Applications, and Beyond. A Tutorial. November 2015. (PDF) http://arxiv.org/abs/1510.08532

• Haishan Ye, Yujun Li, Cheng Chen, and Zhihua Zhang . Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognition. 72: 82-92, 2017.

• Shusen Wang, Zhihua Zhang , and Tong Zhang. Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. Journal of Machine Learning Research (JMLR), 17(210): 1-49, 2016.

• Shusen Wang, Luo Luo, and Zhihua Zhang. SPSD Matrix Approximation vis Column Selection:Theories, Algorithms, and Extensions. Journal of Machine Learning Research, 17(49): 1-49, 2016. (PDF)

• Tianfan Fu, Luo Luo, and Zhihua Zhang. Quasi-Newton Hamiltonian Monte Carlo. In Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016. (PDF)

• Qiaomin Ye, Luo Luo, and Zhihua Zhang. Frequent Direction Algorithms for Approximate Matrix Multiplication with Applicationsin CCA. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'16) , 2016. (PDF)

• Shenjian Zhao, Cong Xie, and Zhihua Zhang. A Scalable and Extensible Framework for Superposition-Structured Models. In Proceedings of the Thirtieth National Conference on Artificial Intelligence (AAAI'16) , 2016. (PDF)

• Wuxuan Jiang, Cong Xie, and Zhihua Zhang. Wishart Mechanism for Differentially Private Principal Components Analysis. In Proceedings of the Thirtieth National Conference on Artificial Intelligence (AAAI'16) , 2016. (PDF)

• Luo Luo, Yubo Xie, Zhihua Zhang, and Wu-Jun Li. Support Matrix Machines. The International Conference on Machine Learning (ICML'15). July 2015. (PDF)

• Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, and David Keyes. A Scalable Community Detection Algorithm for Large Graphs Using Stochastic Block Models. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'15), 2015. (PDF)

• Zhihua Zhang and Jin Li. Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization. Bayesian Analysis. 10 (2): 247-274, 2015.

• Shuchang Zhou, Zhihua Zhang, and Xiaobing Feng. Group obrit optimization: a unified approach to data normalization. October 2014. Technical Report. http://arxiv.org/abs/1410.086

• Cong Xie, Ling Yan, Wu-Jun Li, and Zhihua Zhang. Distributed Power-Law Graph Computing: Theoretical and Empirical Analysis. In Proceedings of Conference on Neural Information Processing Systems (NIPS) 2014. (Long Version)

• Zhihua Zhang, Cheng Chen, Gung Dai, Wu-Jun Li and Dit-Yan Yeung. Multicategory Large Margin Classification Methods: Hinge Losses vs. Coherence Functions. Artificial Intelligence. 215: 55-78, 2014.

• Shusen Wang, Chao Zhang, Hui Qian, and Zhihua Zhang. Improving the Modified Nystrom Method Using Spectral Shifting. In Proceedings of the 20th SIGKDD Conference on Knowledge Discovery and Data Mining, 2014. (PDF)

• Bojun Tu, Zhihua Zhang, Shusen Wang, and Hui Qian. Making Fisher Discriminant Analysis Scalable. The International Conference on Machine Learning (ICML'14). June 2014. <(PDF)>

• Zhihua Zhang, Dakan Wang, Guang Dai, and Michael I. Jordan. Matrix-Variate Dirichlet Process Priors with Applications. Bayesian Analysis, 9(2): 259-286, 2014. (PDF)

• Zhihua Zhang. The Matrix Ridge Approximation: Algorithms and Applications. Machine Learning, 97: 227-258, 2014. (PDF)

• Shusen Wang, Bojun Tu, Congfu Xu, and Zhihua Zhang. Exact Subspace Clustering in Linear Time. In Proceedings of the Twenty-Eithth National Conference on Artificial Intelligence (AAAI'14), 2014. <(PDF)>

• Shusen Wang, Chao Zhang, Hui Qian, and Zhihua Zhang. Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms. In Proceedings of the Twenty-Eithth National Conference on Artificial Intelligence (AAAI'14), 2014. <(PDF)>

• Shusen Wang and Zhihua Zhang. Efficient Algorithms and Error Analysis for the Modified Nystrom Method. The 17th International Conference on Artificial Intelligence and Statistics (AISTATS) (oral), JMLR: W&CP 13, 2014. (PDF)

• Shusen Wang and Zhihua Zhang. Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling. Journal of Machine Learning Research (JMLR), 14: 2729-2769, 2013. (PDF)

• Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, and Zhihua Zhang. A Nearly Unbiased Matrix Completion Approach. In ECML/PKDD , 2013.

• Shusen Wang, Dehua Liu and Zhihua Zhang. Nonconvex Relaxation Approaches to Robust Matrix Recovery. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'13), 2013. (PDF)

• Yifan Pi, Haoruo Peng, Shuchang Zhou and Zhihua Zhang. A Scalable Approach to Column-Based Low-Rank Matrix Approximation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'13), 2013. (PDF)

• Shubao Zhang, Hui Qian, Wei Chen and Zhihua Zhang. A Concave Conjugate Approach for Nonconvex Penalized Regression with the MCP Penalty. In Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI'13), 2013. (PDF)

• Dehua Liu, Bojun Tu, Hui Qian and Zhihua Zhang. Large-Scale Hierarchical Classification via Stochastic Perceptro. In Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI'13), 2013. (PDF)

• Dehua Liu, Hui Qian, Guang Dai and Zhihua Zhang. An Iterative SVM approach to Feature Selection and Classification in High-Dimensional Datasets. Pattern Recognition, 46: 2531-2537, 2013. [doi][bib]

• Zhihua Zhang and Bojun Tu. Nonconvex Penalization Using Laplace Exponents and Concave Conjugates. In Proceedings of the Twenty-Sixth Conference on Neural Information Processing Systems (NIPS) 26, MIT Press, 2012. (PDF)

• Shusen Wang and Zhihua Zhang. A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound. In Proceedings of the Twenty-Sixth Conference on Neural Information Processing Systems (NIPS) 26, MIT Press, 2012. (longer version)

• Zhihua Zhang, Dehua Liu, Guang Dai and Michael I. Jordan. Coherence Functions with Applications in Large-Margin Classification Methods. Journal of Machine Learning Research (JMLR), 13: 2705-2734, 2012. (PDF)

• Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou and Zhihua Zhang. Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2012. (PDF)

• Zhihua Zhang, Shusen Wang, Dehua Liu and Michael I. Jordan. EP-GIG priors and applications in Bayesian sparse learning. Journal of Machine Learning Research (JMLR), 13: 2031-2061, 2012 (PDF)

• Shusen Wang and Zhihua Zhang. Colorization by matrix completion. In Proceedings of the Twenty-Sixth National Conference on Artificial Intelligence (AAAI'12), 2012. (PDF)

• Zhihua Zhang, D. Wang and E. Y. Chang. An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling. The Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 11, 2012. (PDF)

• C. Gao, N. Wang, Q. Yu and Zhihua Zhang. A Feasible Nonconvex Relaxation Approach to Feature Selection. In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence (AAAI'11), 2011. (PDF)

• H. Zhao, J. Han, N. Wang, C. Xu and Zhihua Zhang. A Scalable Spectral Relaxation Approach to Matrix Completion via Kronecker Products. In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence (AAAI'11), 2011. (PDF)

• W.-J. Li, D.-Y. Yeung and Zhihua Zhang. Generalized latent factor models for social network Analysis. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'11), 2011. (PDF)

• J. Shi, X. Ren, G. Dai, J. Wang and Zhihua Zhang. A non-convex relaxation approach to sparse dictionary learning. In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2011. (oral) (PDF)

• Zhihua Zhang, G. Dai and M. I. Jordan. Bayesian generalized kernel mixed models. Journal of Machine Learning Research, 12, 31-59, 2011. (PDF)

• Zhihua Zhang, G. Dai, C. Xu and M. I. Jordan. Regularized Discriminant Analysis, Ridge Regression and Beyond. Journal of Machine Learning Research, 11, 2199-2228, 2010. (PDF)

• Y. Zhang, D. Wang, G. Wang, W. Chen, Zhihua Zhang, B. Hu and L. Zhang. Learning Click Models via Probit Bayesian Inference. In Proceedings of the International Conference on Information and Knowledge Management (CIKM), 2010. (PDF)

• Y. Liu, F. Wu, Zhihua Zhang, Y. Zhuang and S. Yan. Sparse Representation using Nonnegative Curds and Whey. In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2010. (PDF)

• W. Dou, G. Dai, C. Xu and Zhihua Zhang. Sparse Unsupervised Dimensionality Reduction Algorithms. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2010. (PDF)

• Zhihua Zhang, G. Dai and M. I. Jordan. Matrix-Variate Dirichlet Process Mixture Models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 9, 2010. (PDF)

• Zhihua Zhang, G. Dai, Donghui Wang and M. I. Jordan. Bayesian Generalized Kernel Models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 9, 2010. (PDF)

• Zhihua Zhang, G. Wang, D.-Y. Yeung, G. Dai and F. Lochovsky. A regularization framework for multiclass classification: A deterministic annealing approach. Pattern Recognition, 43(7): 2466-2475, 2010.

• Zhihua Zhang and G. Dai. Optimal Scoring for Unsupervised Learning. In Advances in Neural Information Processing Systems 23, MIT Press, 2009. (Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, 7-12 December 2009.) (PDF)

• W.-J. Li, D.-Y. Yeung and Zhihua Zhang. Probabilistic Relational PCA. In Advances in Neural Information Processing Systems 23, MIT Press, 2009. (Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, 7-12 December 2009.) (PDF)

• Zhihua Zhang, G. Dai and M. I. Jordan. A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2009. (PDF)

• W.-J. Li, Zhihua Zhang and D.-Y. Yeung. Latent Wishart processes for relational kernel learning. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA, 16-18 April 2009. (PDF)

• Zhihua Zhang, M. I. Jordan, W.-J. Li and D.-Y. Yeung. Coherence Functions for Multicategory Margin-based Classification Methods. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA, 16-18 April 2009. (PDF)

• Zhihua Zhang and M. I. Jordan. Latent variable models for nonlinear dimensionality reduction. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA, 16-18 April 2009 (PDF)

• Zhihua Zhang, M. I. Jordan and D.-Y. Yeung. Posterior consistency of the Silverman g-prior in Bayesian model choice. Advances in Neural Information Processing Systems (NIPS) 22, Proceedings of the Twenty-Second Conference, 2008. (PDF)

• Zhihua Zhang and M. I. Jordan. Multiway spectral clustering: a margin-based perspective. Statistical Science, 23(3), 383-403, 2008. (PDF)

• Zhihua Zhang, J. T. Kwok, D.-Y. Yeung and E. Y. Chang. Sliced coordinate analysis for effective dimension reduction and nonlinear extensions. Journal of Computational and Graphical Statistics, 17(1): 225-242, 2008.

• Zhihua Zhang, G. Wu and E. Y. Chang. Semiparametric regression using student t processes. IEEE Transactions on Neural Networks, 18(6): 1572-1588, 2007.

• Zhihua Zhang, J. T. Kwok and D.-Y. Yeung. Surrogate maximization/minimization algorithms and extensions. Machine Learning, 69(1):1-33, 2007.

• Zhihua Zhang. Pseudo-Inverse Multivariate/Matrix-variate Distributions. Journal of Multivariate Analysis, 98(8): 1684-1692, 2007.

• A. Jain, Zhihua Zhang and E. Y. Chang. Adaptive Nonlinear Clustering in Data Streams, ACM International Conference on Information and Knowledge Management (CIKM), November 2006.

• Zhihua Zhang and M. I. Jordan. Bayesian Multicategory Support Vector Machines. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Second Conference, 2006. (PDF)

• Zhihua Zhang, J. T. Kwok and D. Y. Yeung. Model-based Transductive Learning of the Kernel Matrix. Machine Learning, 63(1): 69-101, 2006.

• G. Wang, H. Zhao, Zhihua Zhang and F. Lochovsky. A Bernoulli Relational Model for Nonlinear Embedding. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05), New Orleans, Louisiana, U.S.A., 2005.

• G. Wu, Zhihua Zhang and E. Y. Chang. Kronecker Factorization for Speeding up Kernel Machines. SIAM International Conference on Data Mining (SDM), 2005.

• G. Wang, Zhihua Zhang and F. Lochovsky. Annealed Discriminant Analysis. In Proceedings of the 16th European Conference on Machine Learning (ECML-05), 2005.

• G. Wu, E. Y. Chang and Zhihua Zhang. Learning with Non-metric Proximity Matrices. ACM Multimedia 2005: 411-414.

• Zhihua Zhang, G. Wang, D.-Y. Yeung and J. T. Kwok. Probabilistic Kernel Principal Component Analysis. Technical Report HKUST-CS04-03., June 2004. (abstract) (PS)

• Zhihua Zhang, James T. Kwok and D.Y. Yeung. Gaussian-Wishart Process Classification. Technical Report HKUST-CS04-02., June 2004. (abstract) (PS)

• Zhihua Zhang, K. L. Chan, Y. Wu and C. Chen. Learning a Multivariate Gaussian Mixture with the Reversible Jump MCMC Algorithm. Statistics and Computing, 14(4), 343-355, 2004.

• Zhihua Zhang , K.L. Chan, J.T. Kwok, D.Y. Yeung. Bayesian inference on principal component analysis using reversible jump Markov chain Monte Carlo. In Proceedings of Nineteenth National Conference on Artificial Intelligence (AAAI'04), San Jose, California, USA, 25-29 July 2004. (PS)

• Zhihua Zhang, D.Y. Yeung and J. T. Kwok. Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm. In Proceedings of Twenty-First International Conference on Machine Learning (ICML), Banff, Alberta, Canada, 4-8 July 2004. (abstract) (PDF)

• Zhihua Zhang, J. T. Kwok and D.Y. Yeung. Surrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model. In Proceedings of Twenty-First International Conference on Machine Learning (ICML), Banff, Alberta, Canada, 4-8 July 2004. (abstract) (PDF)

• R. K. W. Ho, I. Hu, Zhihua Zhang. The Reversible Jump MCMC Algorithm for Multivariate Gaussian Mixtures with Applications to Random-Effects Models. April 2004. (abstract) (PDF)

• Zhihua Zhang. Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward to Expected Euclidean Representation. In Proceedings of the Twentieth International Conference on Machine Learning (ICML'03), Washington, D.C., USA, August 2003. (PDF)

• Zhihua Zhang, J. T. Kwok and D. Y. Yeung. Parametric Distance Metric Learning with Label Information. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico, August 2003. (longer version)

• Zhihua Zhang, C. Chen, J. Sun and K. L. Chan. EM algorithms for Learning Gaussian Mixture Models with Split-and-Merge Operation. Pattern Recognition, 36(9): 1973 -1983, 2003. (PDF)

• L. Wang, K. L. Chan and Zhihua Zhang. Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval. In Proceedings of Computer Vision and Pattern Recognition (CVPR-03), Feb. 2003. (PDF)

• Y. Huang, K. L. Chan and Zhihua Zhang. Texture classification by multi-model feature integration using Bayesian network. Pattern Recognition Letter, 24: 393-401, 2003. (PDF)