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Refereed Journal Papers

    1

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

Generalized Conditional Gradient for Sparse Estimation

Journal of Machine Learning Research (JMLR)

Accepted with minor revision. 2017. [PDF] [Code]

    2

Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Accelerated Training of Max-Margin Markov Networks with Kernels

Journal of Theoretical Computer Science (TCS)

Vol 519, pages 88--102, January 2014. [PDF]

    3

Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Smoothing Multivariate Performance Measures

Journal of Machine Learning Research (JMLR)

Vol 13, pages 3589--3646, December, 2012. [PDF] [Code]

4

Xiang Yan, Xinhua Zhang, and Liang Huang

Computational Analysis and Optimization of the Integrity Distribution

Journal of Engineering Mathematics, 20(5), 2003. (in Chinese)   [link]

 
 

Refereed Conference Papers

   

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Mohammad Ali Bashiri and Xinhua Zhang

Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search

Advances in Neural Information Processing Systems (NIPS), 2017. [PDF]

   

.

Zhan Shi, Xinhua Zhang, Yaoliang Yu

Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction

Advances in Neural Information Processing Systems (NIPS), 2017. [Spotlight, PDF]

   

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Shin Matsushima, Hyokun Yun, Xinhua Zhang, S.V.N. Vishwanathan

Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem

European Conference on Machine Learning (ECML), 2017. [PDF]

   

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Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

Convex Two-Layer Modeling with Latent Structure

Advances in Neural Information Processing Systems (NIPS), 2016. [PDF]

   

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Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans

Scalable and Sound Low-Rank Tensor Learning

International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. [PDF]

   

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Parameswaran Kamalaruban, Robert C Williamson, Xinhua Zhang

Exp-Concavity of Proper Composite Losses

Conference on Learning Theory (COLT), 2015. [PDF]

   

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Ozlem Aslan, Xinhua Zhang, Dale Schuurmans

Convex Deep Learning via Normalized Kernels

Advances in Neural Information Processing Systems (NIPS), 2014. [PDF]

   

.

Changyou Chen, Jun Zhu, Xinhua Zhang

Robust Bayesian Max-Margin Clustering

Advances in Neural Information Processing Systems (NIPS), 2014. [PDF] [Appendix]

   

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Hengshuai Yao, Csaba Szepesvari, Bernardo Avila Pires, Xinhua Zhang

Pseudo-MDPs and Factored Linear Action Models

Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL), 2014. [PDF]

   

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Xianghang Liu, Xinhua Zhang, Tiberio Caetano

Bayesian Models for Structured Sparse Estimation via Set Cover Prior

European Conference on Machine Learning (ECML), 2014. [PDF] [Long]

   

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Xinhua Zhang, Wee Sun Lee, Yee Whye Teh

Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space

Advances in Neural Information Processing Systems (NIPS), 2013. [PDF]

   

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Xinhua Zhang, Yaoliang Yu, Dale Schuurmans

Polar Operators for Structured Sparse Estimation

Advances in Neural Information Processing Systems (NIPS), 2013. [PDF]

   

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Ozlem Aslan, Hao Cheng, Dale Schuurmans, Xinhua Zhang

Convex Two-Layer Modeling

Advances in Neural Information Processing Systems (NIPS), 2013. [PDF]

   

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Hao Cheng, Xinhua Zhang, Dale Schuurmans

Convex Relaxations of Bregman Divergence Clustering

Uncertainty in Artificial Intelligence (UAI), 2013. [PDF]

   

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Yi Shi, Xinhua Zhang, Xiaoping Liao, Guohui Lin, and Dale Schuurmans

Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM

Pacific Symposium on Biocomputing (PSB), 2013. [PDF]

   

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Xinhua Zhang, Yaoliang Yu, and Dale Schuurmans

Accelerated Training for Matrix-norm Regularization: A Boosting Approach

Advances in Neural Information Processing Systems (NIPS), 2012. [PDF] [Code]

   

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Martha White, Yaoliang Yu, Xinhua Zhang, and Dale Schuurmans

Convex Multi-view Subspace Learning

Advances in Neural Information Processing Systems (NIPS), 2012. [PDF]

   

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Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, and Dale Schuurmans

Sparse Learning based Linear Coherent Bi-clustering

Workshop on Algorithms in Bioinformatics (WABI), 2012.

Lecture Notes in Bioinformatics 7534, 346-364. [PDF]

   

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Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, and Dale Schuurmans

Regularizers versus Losses for Nonlinear Dimensionality Reduction

International Conference on Machine Learning (ICML), 2012.  [PDF] [Supplementary]

   

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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Accelerated Training of Max-Margin Markov Networks with Kernels

Algorithmic Learning Theory (ALT), 2011.  [PDF] [Talk]

   

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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Smoothing Multivariate Performance Measures

Uncertainty in Artificial Intelligence (UAI), 2011.  [PDF] [Long] [code]

   

.

Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, and Dale Schuurmans

Convex Sparse Coding, Subspace Learning, and Semi-supervised Extensions

AAAI Conference on Artificial Intelligence (AAAI), 2011. [PDF]

   

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Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang

New Approximation Algorithms for Minimum Enclosing Convex Shapes

ACM-SIAM Syposium on Discrete Algorithms (SODA), 2011. [PDF]

   

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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Lower Bounds on Rate of Convergence of Cutting Plane Methods

Advances in Neural Information Processing Systems (NIPS), 2010.

[PDF] [Long] [Detail on Nesterov (arXiv)] [Formalization of weak/strong lower bounds]

   

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Xinhua Zhang, Thore Graepel, Ralf Herbrich

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures

International Conference on Artificial Intelligence and Statistics, (AISTATS) 2010. [PDF]

   

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Xinhua Zhang, Le Song, Arthur Gretton, Alex Smola

Kernel Measures of Independence for non-iid Data

Advances in Neural Information Processing Systems (NIPS), 2008. [PDF]  [Appendix] [Spotlight]

   

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Le Song, Xinhua Zhang, Alex Smola, Arthur Gretton, and Bernhard Schoelkopf

Tailoring Density Estimation via Reproducing Kernel Moment Matching

International Conference on Machine Learning (ICML), 2008.  [PDF]

   

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Li Cheng, S. V. N. Vishwanathan, and Xinhua Zhang

Consistent Image Analogies using Semi-supervised Learning

IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2008.  [PDF]

   

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Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan

Conditional Random Fields for Multi-agent Reinforcement Learning

International Conference on Machine Learning (ICML), 2007.  [PDF]

(Best student paper award)

   

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Xinhua Zhang and Wee Sun Lee

Hyperparameter Learning for Graph based Semi-supervised Learning Algorithms

Advances in Neural Information Processing Systems (NIPS), 2006. [PDF]

   

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Xinhua Zhang and Peter K K Loh

A Fault-tolerant Routing Strategy for Fibonacci-class Cubes

Asia-Pacific Computer Systems Architecture Conference (ACSAC), 2005.  [PDF

       
 

Refereed Workshop Oral Presentations

    1

Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan

Conditional Random Fields for Multi-agent Reinforcement Learning

Learning Workshop (Snowbird), 2007.  [PDF]

    2

Peter K K Loh and Xinhua Zhang

A Fault-tolerant Routing Strategy for Gaussian Cube using Gaussian Tree

International Conference on Parallel Processing (ICPP) Workshops, 2003.  [PDF]

   
 

Book Chapters

    1

Xinhua Zhang

Seven articles: Support vector machines, kernel, regularization, empirical risk minimization, structural risk minimization, covariance matrix, Gaussian distribution.

In Claude Sammut and Geoffrey Webb, editors

Encyclopedia on Machine Learning. Springer, 2010.

       
 

Technical Reports

    1

Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Regularized risk minimization by Nesterov’s accelerated gradient methods: Algorithmic extensions and empirical studies

http://arxiv.org/abs/1011.0472, 2011.  [PDF]

       
Theses
   
  PhD Thesis (Australian National University)
    Graphical Models: Modeling, Optimization, and Hilbert Space Embedding [PDF, 3.5 MB]
       
  MSc Thesis (National University of Singapore)
    Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms  [PDF]
   
  Undergraduate Final Year Project (Nanyang Technological University)
    Analysis of Fuzzy-Neuro Network Communications  [PDF]