Refereed Journal Papers

Elisa Tardini, Xinhua Zhang, Guadalupe Canahuate, Andrew Wentzel, Abdallah S R Mohamed, Lisanne Van Dijk, Clifton D Fuller, G Elisabeta Marai

Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad

Journal of Medical Internet Research (JMIR)

Vol 24, No 4, 2022. [Paper] [Data] [Code]

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

Generalized Conditional Gradient for Sparse Estimation

Journal of Machine Learning Research (JMLR)

Vol 18(144):1−46, 2017. [PDF] [Longer version with non-convexity] [Code]

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]

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]

5

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]

Siteng Kang, Zhan Shi, Xinhua Zhang

Poisoning Generative Replay in Continual Learning to Promote Forgetting

International Conference on Machine Learning (ICML), 2023. [PDF]

Zishun Yu, Xinhua Zhang

Actor-Critic Alignment for Offline-to-Online Reinforcement Learning

International Conference on Machine Learning (ICML), 2023. [PDF]

Hongwei Jin, Zishun Yu, Xinhua Zhang

Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats

Advances in Neural Information Processing Systems (NeurIPS), 2022. [PDF]

Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D Ziebart, Kevin Gimpel

Moment Distributionally Robust Tree Structured Prediction

Advances in Neural Information Processing Systems (NeurIPS), 2022. [PDF]

Hongwei Jin, Zishun Yu, Xinhua Zhang

Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound

Uncertainty in Artificial Intelligence (UAI), 2022. [PDF]

Yingyi Ma, Xinhua Zhang

Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. [PDF]

Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart

Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. [PDF]

Mao Li, Kaiqi Jiang, Xinhua Zhang

Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation

Advances in Neural Information Processing Systems (NeurIPS), 2021. [PDF]

Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang

Distributionally Robust Imitation Learning

Advances in Neural Information Processing Systems (NeurIPS), 2021. [PDF]

Zac Cranko*, Zhan Shi*, Xinhua Zhang, Richard Nock, Simon Kornblith (*equal contribution)

Generalised Lipschitz Regularisation Equals Distributional Robustness

International Conference on Machine Learning (ICML), 2021. [PDF]

Mao Li, Yingyi Ma, Xinhua Zhang

Proximal Mapping for Deep Regularization

Advances in Neural Information Processing Systems (NeurIPS), 2020. [Spotlight, PDF]

Hongwei Jin*, Zhan Shi*, Ashish Peruri, Xinhua Zhang (*equal contribution)

Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks

Advances in Neural Information Processing Systems (NeurIPS), 2020. [Spotlight, PDF]

Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

International Conference on Machine Learning (ICML), 2020. [PDF]

Hongwei Jin, Xinhua Zhang

Robust Training of Graph Convolutional Networks via Latent Perturbation

European Conference on Machine Learning (ECML), 2020. [PDF]

Mao Li, Yingyi Ma, Xinhua Zhang

Meta-Learning of Structured Representation by Proximal Mapping

Workshop on Meta-Learning at NeurIPS 2019. [PDF]

Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S.V.N Vishwanathan

Scaling Multinomial Logistic Regression via Hybrid Parallelism

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019. [PDF][Code][Promo Video]

Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang

Learning Invariant Representations with Kernel Warping

International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [PDF]

Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart

Distributionally Robust Graphical Models

Advances in Neural Information Processing Systems (NIPS), 2018. [PDF]

Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu

Inductive Two-Layer Modeling with Parametric Bregman Transfer

International Conference on Machine Learning (ICML), 2018. [PDF]

Rizal Fathony*, Sima Behpour*, Xinhua Zhang, Brian Ziebart (*equal contribution)

Efficient and Consistent Adversarial Bipartite Matching

International Conference on Machine Learning (ICML), 2018. [PDF]

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]

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]

Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

Convex Two-Layer Modeling with Latent Structure

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

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]

Parameswaran Kamalaruban, Robert C Williamson, Xinhua Zhang

Exp-Concavity of Proper Composite Losses

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

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]

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]

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]

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. [Spotlight, PDF]

Xinhua Zhang, Yaoliang Yu, Dale Schuurmans

Polar Operators for Structured Sparse Estimation

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

Ozlem Aslan, Hao Cheng, Dale Schuurmans, Xinhua Zhang

Convex Two-Layer Modeling

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

Hao Cheng, Xinhua Zhang, Dale Schuurmans

Convex Relaxations of Bregman Divergence Clustering

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

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

Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM

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

Xinhua Zhang, Yaoliang Yu, Dale Schuurmans

Accelerated Training for Matrix-norm Regularization: A Boosting Approach

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

Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

Convex Multi-view Subspace Learning

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

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

Sparse Learning based Linear Coherent Bi-clustering

Workshop on Algorithms in Bioinformatics (WABI), 2012.

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

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

Regularizers versus Losses for Nonlinear Dimensionality Reduction

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

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

Accelerated Training of Max-Margin Markov Networks with Kernels

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

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, Dale Schuurmans

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

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

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]

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]

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]

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]

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

Tailoring Density Estimation via Reproducing Kernel Moment Matching

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

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

Consistent Image Analogies using Semi-supervised Learning

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

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

Conditional Random Fields for Multi-agent Reinforcement Learning

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

(Best student paper award)

Xinhua Zhang and Wee Sun Lee

Hyperparameter Learning for Graph based Semi-supervised Learning Algorithms

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

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] 

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

Conditional Random Fields for Multi-agent Reinforcement Learning

Learning Workshop (Snowbird), 2007.  [PDF]

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]

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.

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]