Xinhua Zhang

发表

Conditional Random Fields for Reinforcement Learning

Xinhua Zhang, D. Aberdeen, S. Vishwanathan, 2007 .

Generalized Conditional Gradient for Sparse Estimation pdf

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2014, J. Mach. Learn. Res..

Scalable and Sound Low-Rank Tensor Learning

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2016, AISTATS.

Kernel Measures of Independence for non-iid Data

Le Song, Alexander J. Smola, Xinhua Zhang, 2008, NIPS.

Polar Operators for Structured Sparse Estimation

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2013, NIPS.

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

Martha White, Yaoliang Yu, Xinhua Zhang, 2011, AAAI.

Tailoring density estimation via reproducing kernel moment matching

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2008, ICML '08.

Accelerated Training for Matrix-norm Regularization: A Boosting Approach

Yaoliang Yu, Xinhua Zhang, Dale Schuurmans, 2012, NIPS.

Efficient and Consistent Adversarial Bipartite Matching

Xinhua Zhang, Brian D. Ziebart, Sima Behpour, 2018, ICML.

Latent Adversarial Training of Graph Convolution Networks

Hongwei Jin, Xinhua Zhang, Hongwei Jin, 2019 .

Robust Training of Graph Convolutional Networks via Latent Perturbation

Xinhua Zhang, Hongwei Jin, 2020, ECML/PKDD.

Regularizers versus losses for nonlinear dimensionality reduction

Ryan Kiros, Xinhua Zhang, D. Schuurmans, 2012, ICML 2012.

Meta-Learning of Structured Representation by Proximal Mapping

Xinhua Zhang, Mao Li, Yingyi Ma, 2019 .

Lower bounds for Boosting with Hadamard Matrices

Manfred K. Warmuth, Xinhua Zhang, 2013 .

O(1= ) Lower Bounds for Convex Optimization with Stochastic Oracles

Xinhua Zhang, Yaoliang Yu, 2010 .

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

Xinhua Zhang, Yingyi Ma, 2022, AISTATS.

Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound

Xinhua Zhang, Hongwei Jin, Zishun Yu, 2022, ArXiv.

Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound

Xinhua Zhang, Hongwei Jin, Zishun Yu, 2022, UAI.

A Deconvolutional Competitive Algorithm for Building Sparse Hierarchical Representations

Xinhua Zhang, Garrett T. Kenyon, Peter F. Schultz, 2016, BICT.

A Deconvolutional Strategy for Implementing Large Patch Sizes Supports Improved Image Classification

Xinhua Zhang, Garrett T. Kenyon, Xinhua Zhang, 2016, BICT.

Bayesian Models for Structured Sparse Estimation via Set Cover Prior

Xinhua Zhang, Tibério S. Caetano, Xianghang Liu, 2014, ECML/PKDD.

Convex Deep Learning via Normalized Kernels

Xinhua Zhang, Dale Schuurmans, Özlem Aslan, 2014, NIPS.

Smoothing multivariate performance measures

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2011, J. Mach. Learn. Res..

Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances

Yee Whye Teh, Wee Sun Lee, Xinhua Zhang, 2006 .

Convex Two-Layer Modeling

Xinhua Zhang, Dale Schuurmans, Hao Cheng, 2013, NIPS.

Consistent image analogies using semi-supervised learning

Xinhua Zhang, S. V. N. Vishwanathan, Li Cheng, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Xinhua Zhang, Wee Sun Lee, Xinhua Zhang, 2006, NIPS.

Consistent Robust Adversarial Prediction for General Multiclass Classification pdf

Xinhua Zhang, Brian D. Ziebart, Anqi Liu, 2018, ArXiv.

Distributionally Robust Graphical Models

Xinhua Zhang, Brian D. Ziebart, Rizal Fathony, 2018, NeurIPS.

Graphical Models: Modeling, Optimization, and Hilbert Space Embedding

Xinhua Zhang, 2010 .

Generalised Lipschitz Regularisation Equals Distributional Robustness pdf

Richard Nock, Xinhua Zhang, Zhan Shi, 2020, ICML.

Can Deep Learning Learn the Principle of Closed Contour Detection?

Xinhua Zhang, Garrett T. Kenyon, Yijing Watkins, 2018, ISVC.

Pseudo-MDPs and factored linear action models

Xinhua Zhang, Csaba Szepesvári, Bernardo Ávila Pires, 2014, 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Accelerated training of max-margin Markov networks with kernels

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2011, Theor. Comput. Sci..

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

Xinhua Zhang, Yaoliang Yu, Yingyi Ma, 2020, ICML.

Distributionally Robust Imitation Learning

Brian D. Ziebart, Mohammad Ali Bashiri, Xinhua Zhang, 2021, NeurIPS.

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

Xinhua Zhang, Zhan Shi, Hongwei Jin, 2020, NeurIPS.

Convex Multi-view Subspace Learning

Martha White, Yaoliang Yu, Xinhua Zhang, 2012, NIPS.

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

Xinhua Zhang, Thore Graepel, Ralf Herbrich, 2010, AISTATS.

Open Problem: Lower bounds for Boosting with Hadamard Matrices

Xinhua Zhang, S. V. N. Vishwanathan, Jiazhong Nie, 2013, COLT.

Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM

Xinhua Zhang, Dale Schuurmans, Yi Shi, 2012, Pacific Symposium on Biocomputing.

Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem

Xinhua Zhang, S. V. N. Vishwanathan, Shin Matsushima, 2017, ECML/PKDD.

Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space

Yee Whye Teh, Xinhua Zhang, Wee Sun Lee, 2013, NIPS.

Lower Bounds on Rate of Convergence of Cutting Plane Methods

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2010, NIPS.

Lower Bounds for BMRM and Faster Rates for Training SVMs pdf

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2009, ArXiv.

Four Applications of Graphical Models in Machine Learning

Xinhua Zhang, 2009 .

Robust Bayesian Max-Margin Clustering

Xinhua Zhang, Jun Zhu, Changyou Chen, 2014, NIPS.

Inductive Two-layer Modeling with Parametric Bregman Transfer

Yaoliang Yu, Xinhua Zhang, Zhan Shi, 2018, ICML.

Faster Rates for training Max-Margin Markov Networks pdf

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2010, ArXiv.

DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression pdf

Xinhua Zhang, S. V. N. Vishwanathan, Shin Matsushima, 2016, ArXiv.

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

Yaoliang Yu, Xinhua Zhang, Zhan Shi, 2017, NIPS.

Smoothing multivariate performance measures

Xinhua Zhang, S. Vishwanathan, Ankan Saha, 2011, J. Mach. Learn. Res..

Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies pdf

Xinhua Zhang, S. V. N. Vishwanathan, Ankan Saha, 2010, ArXiv.

Proximal Mapping for Deep Regularization pdf

Mao Li, Xinhua Zhang, Yingyi Ma, 2020, NeurIPS.

Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Wee Sun Lee, Xinhua Zhang, 2006, NIPS.