Zhang, Xinhua

53 publications

AISTATS 2025 Fairness Risks for Group-Conditionally Missing Demographics Kaiqi Jiang, Wenzhe Fan, Mao Li, Xinhua Zhang

AAAI 2025 Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning Wenzhe Fan, Zishun Yu, Chengdong Ma, Changye Li, Yaodong Yang, Xinhua Zhang

UAI 2024 Offline Reward Perturbation Boosts Distributional Shift in Online RL Zishun Yu, Siteng Kang, Xinhua Zhang

ICML 2023 Actor-Critic Alignment for Offline-to-Online Reinforcement Learning Zishun Yu, Xinhua Zhang

ICML 2023 Poisoning Generative Replay in Continual Learning to Promote Forgetting Siteng Kang, Zhan Shi, Xinhua Zhang

AISTATS 2022 Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks Yeshu Li, Zhan Shi, Xinhua Zhang, Brian Ziebart

AISTATS 2022 Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning Yingyi Ma, Xinhua Zhang

NeurIPS 2022 Certifying Robust Graph Classification Under Orthogonal Gromov-Wasserstein Threats Hongwei Jin, Zishun Yu, Xinhua Zhang

NeurIPSW 2022 Continual Poisoning of Generative Models to Promote Catastrophic Forgetting Siteng Kang, Zhan Shi, Xinhua Zhang

NeurIPS 2022 Moment Distributionally Robust Tree Structured Prediction Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian Ziebart, Kevin Gimpel

UAI 2022 Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound Hongwei Jin, Zishun Yu, Xinhua Zhang

NeurIPSW 2022 Poisoning Generative Models to Promote Catastrophic Forgetting Siteng Kang, Xinhua Zhang

NeurIPS 2021 Distributionally Robust Imitation Learning Mohammad Ali Bashiri, Brian Ziebart, Xinhua Zhang

ICML 2021 Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith

NeurIPS 2021 Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation Mao Li, Kaiqi Jiang, Xinhua Zhang

NeurIPS 2020 Certified Robustness of Graph Convolution Networks for Graph Classification Under Topological Attacks Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang

ICML 2020 Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

NeurIPS 2020 Proximal Mapping for Deep Regularization Mao Li, Yingyi Ma, Xinhua Zhang

ECML-PKDD 2020 Robust Training of Graph Convolutional Networks via Latent Perturbation Hongwei Jin, Xinhua Zhang

WACV 2019 Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors Xinhua Zhang, Lance R. Williams

AISTATS 2019 Learning Invariant Representations with Kernel Warping Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang

NeurIPS 2018 Distributionally Robust Graphical Models Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart

ICML 2018 Efficient and Consistent Adversarial Bipartite Matching Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart

ICML 2018 Inductive Two-Layer Modeling with Parametric Bregman Transfer Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu

NeurIPS 2017 Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction Zhan Shi, Xinhua Zhang, Yaoliang Yu

NeurIPS 2017 Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search Mohammad Ali Bashiri, Xinhua Zhang

ECML-PKDD 2017 Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan

JMLR 2017 Generalized Conditional Gradient for Sparse Estimation Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

NeurIPS 2016 Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

AISTATS 2016 Scalable and Sound Low-Rank Tensor Learning Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans

COLT 2015 Exp-Concavity of Proper Composite Losses Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang

ECML-PKDD 2014 Bayesian Models for Structured Sparse Estimation via Set Cover Prior Xianghang Liu, Xinhua Zhang, Tibério S. Caetano

NeurIPS 2014 Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans

NeurIPS 2014 Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang

UAI 2013 Convex Relaxations of Bregman Divergence Clustering Hao Cheng, Xinhua Zhang, Dale Schuurmans

NeurIPS 2013 Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans

NeurIPS 2013 Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh

COLT 2013 Open Problem: Lower Bounds for Boosting with Hadamard Matrices Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang

NeurIPS 2013 Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans

NeurIPS 2012 Accelerated Training for Matrix-Norm Regularization: A Boosting Approach Xinhua Zhang, Dale Schuurmans, Yao-liang Yu

NeurIPS 2012 Convex Multi-View Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu

ICML 2012 Regularizers Versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans

JMLR 2012 Smoothing Multivariate Performance Measures Xinhua Zhang, Ankan Saha, S.V.N. Vishwanathan

ALT 2011 Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

AAAI 2011 Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans

UAI 2011 Smoothing Multivariate Performance Measures Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

AISTATS 2010 Bayesian Online Learning for Multi-Label and Multi-Variate Performance Measures Xinhua Zhang, Thore Graepel, Ralf Herbrich

NeurIPS 2010 Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang, Ankan Saha, S.v.n. Vishwanathan

CVPR 2008 Consistent Image Analogies Using Semi-Supervised Learning Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang

NeurIPS 2008 Kernel Measures of Independence for Non-Iid Data Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola

ICML 2008 Tailoring Density Estimation via Reproducing Kernel Moment Matching Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf

ICML 2007 Conditional Random Fields for Multi-Agent Reinforcement Learning Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanathan

NeurIPS 2006 Hyperparameter Learning for Graph Based Semi-Supervised Learning Algorithms Xinhua Zhang, Wee S. Lee