| Publications | |||
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| 1 |
Shangzhe Li, Xinhua Zhang Augmenting Offline Reinforcement Learning with State-only Interactions arXiv:2402.00807, 2024 [PDF] |
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| 2 |
Kaiqi Jiang, Wenzhe Fan, Mao Li, Xinhua Zhang Fairness Risks for Group-conditionally Missing Demographics arXiv:2402.13393, 2024 [PDF] |
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| 1 |
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) |
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| 2 |
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] |
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| 3 |
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] |
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| 4 |
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Smoothing Multivariate Performance Measures Journal of Machine Learning Research (JMLR) |
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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] |
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Refereed Conference Papers |
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Wenzhe Fan, Zishun Yu, Chengdong Ma, Changye Li, Yaodong Yang, Xinhua Zhang Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning AAAI Conference on Artificial Intelligence (AAAI), 2025. [PDF] |
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Zishun Yu*, Siteng Kang*, Xinhua Zhang (*equal contribution) Offline Reward Perturbation Boosts Distributional Shift in Online RL Conference on Uncertainty in Artificial Intelligence (UAI), 2024. [PDF] |
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Siteng Kang, Zhan Shi, Xinhua Zhang Poisoning Generative Replay in Continual Learning to Promote Forgetting International Conference on Machine Learning (ICML), 2023. [PDF] |
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Zishun Yu, Xinhua Zhang Actor-Critic Alignment for Offline-to-Online Reinforcement Learning International Conference on Machine Learning (ICML), 2023. [PDF] |
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Hongwei Jin, Zishun Yu, Xinhua Zhang Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats Advances in Neural Information Processing Systems (NeurIPS), 2022. [PDF] |
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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] |
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Hongwei Jin, Zishun Yu, Xinhua Zhang Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound Uncertainty in Artificial Intelligence (UAI), 2022. [PDF] |
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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] |
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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] |
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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] |
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Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang Distributionally Robust Imitation Learning Advances in Neural Information Processing Systems (NeurIPS), 2021. [PDF] |
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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] |
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Mao Li, Yingyi Ma, Xinhua Zhang Proximal Mapping for Deep Regularization Advances in Neural Information Processing Systems (NeurIPS), 2020. [Spotlight, PDF] |
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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] |
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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] |
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Hongwei Jin, Xinhua Zhang Robust Training of Graph Convolutional Networks via Latent Perturbation European Conference on Machine Learning (ECML), 2020. [PDF] |
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Mao Li, Yingyi Ma, Xinhua Zhang Meta-Learning of Structured Representation by Proximal Mapping Workshop on Meta-Learning at NeurIPS 2019. [PDF] |
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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] |
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Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang Learning Invariant Representations with Kernel Warping International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [PDF] |
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Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart Distributionally Robust Graphical Models Advances in Neural Information Processing Systems (NIPS), 2018. [PDF] |
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Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu Inductive Two-Layer Modeling with Parametric Bregman Transfer International Conference on Machine Learning (ICML), 2018. [PDF] |
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Rizal Fathony*, Sima Behpour*, Xinhua Zhang, Brian Ziebart (*equal contribution) Efficient and Consistent Adversarial Bipartite Matching International Conference on Machine Learning (ICML), 2018. [PDF] |
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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] |
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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] |
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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. [Spotlight, 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. [Spotlight, 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, 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, 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, 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, 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, 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 |
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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] |
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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] |
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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, 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, 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, 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] |
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Refereed Workshop Oral Presentations |
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| 1 |
Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan Conditional Random Fields for Multi-agent Reinforcement Learning Learning Workshop (Snowbird), 2007. [PDF] |
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| 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] |
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Book Chapters |
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| 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. |
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Technical Reports |
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| 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] |
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| 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] | |||