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ORCIDZhihua Zhang 0004
Person information
- affiliation: Peking University, Center for Statistical Science, School of Mathematical Sciences, China
Other persons with the same name
- Zhihua Zhang (aka: Zhi-Hua Zhang) — disambiguation page
- Zhihua Zhang 0001
![[0000-0002-9387-7308]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-9387-7308")
— Kaiserslautern University of Technology, Germany - Zhihua Zhang 0002 — Nara Institute of Science and Technology, Nara, Japan (and 1 more)
- Zhihua Zhang 0003 — Shandong University, Interdisciplinary Data Mining Group, School of Mathematics, Jinan, China
- Zhihua Zhang 0005 — Jiangsu Shipping College, Department of Transportation Engineering, Nantong, China (and 2 more)
- Zhihua Zhang 0006 — Beijing University of Chemical Technology, College of Mechanical and Electrical Engineering, China
- Zhihua Zhang 0007 — Chinese Academy of Sciences, CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, China (and 1 more)
- Zhihua Zhang 0008 — Shanghai Jiao Tong University, Department of Computer Science and Engineering, China
- Zhihua Zhang 0009 — Beijing University of Posts and Telecommunications, BUPT, China (and 1 more)
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2020 – today
- 2024
[j22]Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang:
Fedpower: privacy-preserving distributed eigenspace estimation. Mach. Learn. 113(11): 8427-8458 (2024)- [j21]Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang:
Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3722-3735 (2024) - [c49]Yang Peng, Liangyu Zhang, Zhihua Zhang:
Statistical Efficiency of Distributional Temporal Difference Learning. NeurIPS 2024 - [i45]Yang Peng, Liangyu Zhang, Zhihua Zhang:
Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces. CoRR abs/2403.05811 (2024) - [i44]Hao Jin, Yang Peng, Liangyu Zhang, Zhihua Zhang:
Federated Control in Markov Decision Processes. CoRR abs/2405.04026 (2024) - 2023
- [j20]Haishan Ye, Dachao Lin, Xiangyu Chang, Zhihua Zhang:
Towards explicit superlinear convergence rate for SR1. Math. Program. 199(1): 1273-1303 (2023) - [c48]Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS 2023: 2207-2261 - [c47]Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang:
Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants. Allerton 2023: 1 - [c46]Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang:
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS 2023 - [i43]Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang:
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. CoRR abs/2304.07504 (2023) - [i42]Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang:
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning. CoRR abs/2305.00254 (2023) - [i41]Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, Zhihua Zhang:
Estimation and Inference in Distributional Reinforcement Learning. CoRR abs/2309.17262 (2023) - 2022
- [j19]Dachao Lin, Ruoyu Sun, Zhihua Zhang:
On the landscape of one-hidden-layer sparse networks and beyond. Artif. Intell. 309: 103739 (2022) - [j18]Dachao Lin, Haishan Ye, Zhihua Zhang:
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. J. Mach. Learn. Res. 23: 162:1-162:40 (2022) - [c45]Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang:
Federated Reinforcement Learning with Environment Heterogeneity. AISTATS 2022: 18-37 - [c44]Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang:
Statistical Estimation and Online Inference via Local SGD. COLT 2022: 1613-1661 - [c43]Kun Chen, Dachao Lin, Zhihua Zhang:
On Non-local Convergence Analysis of Deep Linear Networks. ICML 2022: 3417-3443 - [c42]Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang:
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. NeurIPS 2022 - [c41]Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang:
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. NeurIPS 2022 - [c40]Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang:
Semi-infinitely Constrained Markov Decision Processes. NeurIPS 2022 - [i40]Kun Chen, Dachao Lin, Zhihua Zhang:
Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer. CoRR abs/2201.02761 (2022) - [i39]Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang:
Federated Reinforcement Learning with Environment Heterogeneity. CoRR abs/2204.02634 (2022) - [i38]Dachao Lin, Zhihua Zhang:
On the Convergence of Policy in Unregularized Policy Mirror Descent. CoRR abs/2205.08176 (2022) - 2021
- [j17]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods. J. Mach. Learn. Res. 22: 66:1-66:41 (2021) - [j16]Haishan Ye, Luo Luo, Zhihua Zhang:
Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4374-4388 (2021) - [c39]Yuekai Zhao, Shuchang Zhou, Zhihua Zhang:
Multi-split Reversible Transformers Can Enhance Neural Machine Translation. EACL 2021: 244-254 - [c38]Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang:
Communication-Efficient Distributed SVD via Local Power Iterations. ICML 2021: 6504-6514 - [c37]Dachao Lin, Ruoyu Sun, Zhihua Zhang:
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. NeurIPS 2021: 4647-4660 - [c36]Dachao Lin, Haishan Ye, Zhihua Zhang:
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS 2021: 6646-6657 - [i37]Xiang Li, Zhihua Zhang:
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications. CoRR abs/2101.01505 (2021) - [i36]Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang:
Privacy-Preserving Distributed SVD via Federated Power. CoRR abs/2103.00704 (2021) - [i35]Guangzeng Xie, Hao Jin, Dachao Lin, Zhihua Zhang:
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent. CoRR abs/2104.05447 (2021) - [i34]Dachao Lin, Zhihua Zhang:
Directional Convergence Analysis under Spherically Symmetric Distribution. CoRR abs/2105.03879 (2021) - [i33]Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang:
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR abs/2106.01761 (2021) - [i32]Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang:
Statistical Estimation and Inference via Local SGD in Federated Learning. CoRR abs/2109.01326 (2021) - [i31]Haishan Ye, Dachao Lin, Zhihua Zhang:
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. CoRR abs/2110.08572 (2021) - [i30]Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan:
Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. CoRR abs/2112.14582 (2021) - 2020
- [j15]Haishan Ye, Luo Luo, Zhihua Zhang:
Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. 21: 142:1-142:37 (2020) - [c35]Xiang Li, Shusen Wang, Zhihua Zhang:
Do Subsampled Newton Methods Work for High-Dimensional Data? AAAI 2020: 4723-4730 - [c34]Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu:
Efficient Spectrum-Revealing CUR Matrix Decomposition. AISTATS 2020: 766-775 - [c33]Yuekai Zhao, Haoran Zhang, Shuchang Zhou, Zhihua Zhang:
Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study. EMNLP (Findings) 2020: 1796-1806 - [c32]Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang:
On the Convergence of FedAvg on Non-IID Data. ICLR 2020 - [c31]Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang:
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML 2020: 10504-10513 - [i29]Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang:
Communication-Efficient Distributed SVD via Local Power Iterations. CoRR abs/2002.08014 (2020) - [i28]Dachao Lin, Peiqin Sun, Guangzeng Xie, Shuchang Zhou, Zhihua Zhang:
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond. CoRR abs/2008.13128 (2020) - [i27]Dachao Lin, Ruoyu Sun, Zhihua Zhang:
Landscape of Sparse Linear Network: A Brief Investigation. CoRR abs/2009.07439 (2020) - [i26]Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang:
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs. CoRR abs/2011.00213 (2020)
2010 – 2019
- 2019
- [j14]Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang:
Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. 20: 45:1-45:41 (2019) - [j13]Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang:
Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. 88: 629-642 (2019) - [c30]Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang:
Lipschitz Generative Adversarial Nets. ICML 2019: 7584-7593 - [c29]Wenhao Yang, Xiang Li, Zhihua Zhang:
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. NeurIPS 2019: 5938-5948 - [i25]Xiang Li, Shusen Wang, Zhihua Zhang:
Do Subsampled Newton Methods Work for High-Dimensional Data? CoRR abs/1902.04952 (2019) - [i24]Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang:
Lipschitz Generative Adversarial Nets. CoRR abs/1902.05687 (2019) - [i23]Xiang Li, Wenhao Yang, Zhihua Zhang:
A Unified Framework for Regularized Reinforcement Learning. CoRR abs/1903.00725 (2019) - [i22]Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang:
On the Convergence of FedAvg on Non-IID Data. CoRR abs/1907.02189 (2019) - [i21]Hao Jin, Dachao Lin, Zhihua Zhang:
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks. CoRR abs/1908.06395 (2019) - [i20]Guangzeng Xie, Luo Luo, Zhihua Zhang:
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. CoRR abs/1908.08394 (2019) - [i19]Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang:
A Stochastic Proximal Point Algorithm for Saddle-Point Problems. CoRR abs/1909.06946 (2019) - [i18]Xiang Li, Wenhao Yang, Shusen Wang, Zhihua Zhang:
Communication Efficient Decentralized Training with Multiple Local Updates. CoRR abs/1910.09126 (2019) - [i17]Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang:
Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR abs/1912.12008 (2019) - 2018
- [c28]Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei:
Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD 2018: 1900-1909 - [i16]Guangzeng Xie, Yitan Wang, Shuchang Zhou, Zhihua Zhang:
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks. CoRR abs/1805.06753 (2018) - 2017
- [j12]Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang:
Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognit. 72: 82-92 (2017) - [c27]Zihao Chen, Luo Luo, Zhihua Zhang:
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. AAAI 2017: 1812-1818 - [c26]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods and Their Local Convergence. ICML 2017: 3931-3939 - [i15]Haishan Ye, Luo Luo, Zhihua Zhang:
A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR abs/1702.08124 (2017) - [i14]Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li:
Online Learning Via Regularized Frequent Directions. CoRR abs/1705.05067 (2017) - [i13]Haishan Ye, Zhihua Zhang:
Nesterov's Acceleration For Approximate Newton. CoRR abs/1710.08496 (2017) - 2016
- [j11]Shusen Wang, Luo Luo, Zhihua Zhang:
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. J. Mach. Learn. Res. 17: 49:1-49:49 (2016) - [j10]Shusen Wang, Zhihua Zhang, Tong Zhang:
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. J. Mach. Learn. Res. 17: 210:1-210:49 (2016) - [c25]Qiaomin Ye, Luo Luo, Zhihua Zhang:
Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. IJCAI 2016: 2301-2307 - [c24]Tianfan Fu, Luo Luo, Zhihua Zhang:
Quasi-Newton Hamiltonian Monte Carlo. UAI 2016 - [i12]Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li:
Variance-Reduced Second-Order Methods. CoRR abs/1602.00223 (2016) - [i11]Haishan Ye, Luo Luo, Zhihua Zhang:
Revisiting Sub-sampled Newton Methods. CoRR abs/1608.02875 (2016) - [i10]Haishan Ye, Qiaoming Ye, Zhihua Zhang:
Tighter bound of Sketched Generalized Matrix Approximation. CoRR abs/1609.02258 (2016) - [i9]Zihao Chen, Luo Luo, Zhihua Zhang:
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. CoRR abs/1612.00599 (2016) - 2015
- [c23]Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li:
Support Matrix Machines. ICML 2015: 938-947 - [i8]Shusen Wang, Zhihua Zhang, Tong Zhang:
Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition. CoRR abs/1503.08395 (2015) - [i7]Shuang Liu, Cheng Chen, Zhihua Zhang:
Distributed Multi-Armed Bandits: Regret vs. Communication. CoRR abs/1504.03509 (2015) - [i6]Shusen Wang, Zhihua Zhang, Tong Zhang:
Improved Analyses of the Randomized Power Method and Block Lanczos Method. CoRR abs/1508.06429 (2015) - [i5]Cheng Chen, Shuang Liu, Zhihua Zhang, Wu-Jun Li:
A Parallel algorithm for $\mathcal{X}$-Armed bandits. CoRR abs/1510.07471 (2015) - 2014
- [j9]Zhihua Zhang, Cheng Chen, Guang Dai, Wu-Jun Li, Dit-Yan Yeung:
Multicategory large margin classification methods: Hinge losses vs. coherence functions. Artif. Intell. 215: 55-78 (2014) - [i4]Shusen Wang, Luo Luo, Zhihua Zhang:
The Modified Nystrom Method: Theories, Algorithms, and Extension. CoRR abs/1406.5675 (2014) - [i3]Shuchang Zhou, Zhihua Zhang, Xiaobing Feng:
Group Orbit Optimization: A Unified Approach to Data Normalization. CoRR abs/1410.0868 (2014) - [i2]Shusen Wang, Tong Zhang, Zhihua Zhang:
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion. CoRR abs/1412.7938 (2014) - 2013
- [c22]Yifan Pi, Haoruo Peng, Shuchang Zhou, Zhihua Zhang:
A Scalable Approach to Column-Based Low-Rank Matrix Approximation. IJCAI 2013: 1600-1606 - 2012
- [j8]Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan:
EP-GIG Priors and Applications in Bayesian Sparse Learning. J. Mach. Learn. Res. 13: 2031-2061 (2012) - [j7]Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan:
Coherence functions with applications in large-margin classification methods. J. Mach. Learn. Res. 13: 2705-2734 (2012) - [c21]Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou, Zhihua Zhang:
Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets. ECML/PKDD (1) 2012: 553-568 - [c20]Zhihua Zhang, Dakan Wang, Edward Y. Chang:
An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling. AISTATS 2012: 1416-1424 - [i1]Zhihua Zhang, Michael I. Jordan:
Bayesian Multicategory Support Vector Machines. CoRR abs/1206.6863 (2012) - 2011
- [j6]Zhihua Zhang, Guang Dai, Michael I. Jordan:
Bayesian Generalized Kernel Mixed Models. J. Mach. Learn. Res. 12: 111-139 (2011) - [c19]Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang:
Generalized Latent Factor Models for Social Network Analysis. IJCAI 2011: 1705-1710 - 2010
- [j5]Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan:
Regularized Discriminant Analysis, Ridge Regression and Beyond. J. Mach. Learn. Res. 11: 2199-2228 (2010) - [j4]Zhihua Zhang, Gang Wang, Dit-Yan Yeung, Guang Dai, Frederick H. Lochovsky:
A regularization framework for multiclass classification: A deterministic annealing approach. Pattern Recognit. 43(7): 2466-2475 (2010) - [c18]Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan:
Bayesian Generalized Kernel Models. AISTATS 2010: 972-979 - [c17]Zhihua Zhang, Guang Dai, Michael I. Jordan:
Matrix-Variate Dirichlet Process Mixture Models. AISTATS 2010: 980-987
2000 – 2009
- 2009
- [c16]Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang:
Probabilistic Relational PCA. NIPS 2009: 1123-1131 - [c15]Zhihua Zhang, Guang Dai, Michael I. Jordan:
A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) 2009: 632-647 - [c14]Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung:
Latent Wishart Processes for Relational Kernel Learning. AISTATS 2009: 336-343 - [c13]Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung:
Coherence Functions for Multicategory Margin-based Classification Methods. AISTATS 2009: 647-654 - [c12]Zhihua Zhang, Michael I. Jordan:
Latent Variable Models for Dimensionality Reduction. AISTATS 2009: 655-662 - 2008
- [c11]Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung:
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS 2008: 1969-1976 - 2007
- [j3]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms and extensions. Mach. Learn. 69(1): 1-33 (2007) - [j2]Zhihua Zhang, Gang Wu, Edward Y. Chang:
Semiparametric Regression Using Student t Processes. IEEE Trans. Neural Networks 18(6): 1572-1588 (2007) - 2006
- [j1]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Model-based transductive learning of the kernel matrix. Mach. Learn. 63(1): 69-101 (2006) - [c10]Ankur Jain, Zhihua Zhang, Edward Y. Chang:
Adaptive non-linear clustering in data streams. CIKM 2006: 122-131 - [c9]Zhihua Zhang, Michael I. Jordan:
Bayesian Multicategory Support Vector Machines. UAI 2006 - 2005
- [c8]Gang Wang, Zhihua Zhang, Frederick H. Lochovsky:
Annealed Discriminant Analysis. ECML 2005: 449-460 - [c7]Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. Lochovsky:
A Bernoulli Relational Model for Nonlinear Embedding. ICDM 2005: 458-465 - [c6]Gang Wu, Edward Y. Chang, Zhihua Zhang:
Learning with non-metric proximity matrices. ACM Multimedia 2005: 411-414 - [c5]Gang Wu, Zhihua Zhang, Edward Y. Chang:
Kronecker Factorization for Speeding up Kernel Machines. SDM 2005: 611-615 - 2004
- [c4]Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung:
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. AAAI 2004: 372-377 - [c3]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. ICML 2004 - [c2]Zhihua Zhang, Dit-Yan Yeung, James T. Kwok:
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. ICML 2004 - 2003
- [c1]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Parametric Distance Metric Learning with Label Information. IJCAI 2003: 1450-
Coauthor Index
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