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2020 – today
- 2024
[c84]Eric Ababio Anyimadu
, Clifton David Fuller, Xinhua Zhang, G. Elisabeta Marai, Guadalupe Canahuate:
Collaborative Filtering for the Imputation of Patient Reported Outcomes. DEXA (1) 2024: 231-248- [c83]Xinhua Zhang, Chenwang Shen, Jiaxi Xu, Fenghao Yuan, Chaofan Li, Xu Liu:
Chinese Native Dog Breed Classification Method based on Improved ResNet. ICICT 2024: 244-249 - [i21]Shangzhe Li, Xinhua Zhang:
Distilling Conditional Diffusion Models for Offline Reinforcement Learning through Trajectory Stitching. CoRR abs/2402.00807 (2024) - [i20]Kaiqi Jiang, Wenzhe Fan, Mao Li, Xinhua Zhang:
Fairness Risks for Group-conditionally Missing Demographics. CoRR abs/2402.13393 (2024) - [i19]Andrew Wentzel, Serageldin Attia, Xinhua Zhang, Guadalupe Canahuate, Clifton David Fuller, G. Elisabeta Marai:
DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer. CoRR abs/2407.13107 (2024) - 2023
- [c82]Yaohua Wang, Lisanne van Dijk, Abdallah Sherif Radwan Mohamed, Mohamed A. Naser, Clifton David Fuller, Xinhua Zhang, G. Elisabeta Marai, Guadalupe Canahuate:
Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients. ICHI 2023: 292-300 - [c81]Siteng Kang, Zhan Shi, Xinhua Zhang:
Poisoning Generative Replay in Continual Learning to Promote Forgetting. ICML 2023: 15769-15785 - [c80]Zishun Yu, Xinhua Zhang:
Actor-Critic Alignment for Offline-to-Online Reinforcement Learning. ICML 2023: 40452-40474 - 2022
- [j22]Roxana Bujack, Xinhua Zhang, Tomás Suk, David H. Rogers:
Systematic generation of moment invariant bases for 2D and 3D tensor fields. Pattern Recognit. 123: 108313 (2022) - [j21]Xinhua Zhang, Lin You, Gengran Hu:
An Efficient and Robust Multidimensional Data Aggregation Scheme for Smart Grid Based on Blockchain. IEEE Trans. Netw. Serv. Manag. 19(4): 3949-3959 (2022) - [c79]Yingyi Ma, Xinhua Zhang:
Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning. AISTATS 2022: 7286-7299 - [c78]Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. AISTATS 2022: 8997-9016 - [c77]Hongwei Jin, Zishun Yu, Xinhua Zhang:
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats. NeurIPS 2022 - [c76]Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D. Ziebart, Kevin Gimpel:
Moment Distributionally Robust Tree Structured Prediction. NeurIPS 2022 - [c75]Hongwei Jin, Zishun Yu, Xinhua Zhang:
Orthogonal Gromov-Wasserstein discrepancy with efficient lower bound. UAI 2022: 917-927 - [i18]Xinhua Zhang, Lance R. Williams:
Similarity Equivariant Linear Transformation of Joint Orientation-Scale Space Representations. CoRR abs/2203.06786 (2022) - [i17]Xinhua Zhang, Lance R. Williams:
Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors. CoRR abs/2203.06787 (2022) - [i16]Hongwei Jin, Zishun Yu, Xinhua Zhang:
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound. CoRR abs/2205.05838 (2022) - [i15]Bradley T. Wolfe, Michael J. Falato, Xinhua Zhang, Nga T. T. Nguyen-Fotiadis, J. P. Sauppe, P. M. Kozlowski, P. A. Keiter, R. E. Reinovsky, S. A. Batha, Zhehui Wang:
Machine Learning for Detection of 3D Features using sparse X-ray data. CoRR abs/2206.02564 (2022) - 2021
- [j20]Junzheng Liu, Xinhua Zhang, Zengpei Xu, Jia Wang, Bing Ma, Ruiying Xue, Qian Li:
Evaluation of the impact of urban river bends on the enhancement of aquatic habitats using a two-dimensional habitat suitability model. Ecol. Informatics 65: 101428 (2021) - [j19]Xiaoshuai Duan, Xinhua Zhang, Yongbin Tang, Minghui Hao:
Cogging Torque Reduction in PMSM in Wide Temperature Range by Response Surface Methodology. Symmetry 13(10): 1877 (2021) - [j18]Minnan Piao, Ying Wang, Mingwei Sun, Xinhua Zhang, Zengqiang Chen, Yongyi Yan:
Fixed-Time-Convergent Generalized Extended State Observer Based Motor Control Subject to Multiple Disturbances. IEEE Trans. Ind. Informatics 17(12): 8066-8079 (2021) - [c74]Mikayla Biggs, Carla Floricel, Lisanne van Dijk, Abdallah Sherif Radwan Mohamed, Clifton David Fuller, G. Elisabeta Marai, Xinhua Zhang, Guadalupe Canahuate:
Identifying Symptom Clusters Through Association Rule Mining. AIME 2021: 491-496 - [c73]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML 2021: 2178-2188 - [c72]Yaohua Wang, Guadalupe Canahuate, Lisanne van Dijk, Abdallah Sherif Radwan Mohamed, Clifton David Fuller, Xinhua Zhang, Georgeta Elisabeta Marai:
Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes. IDEAS 2021: 273-279 - [c71]Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang:
Distributionally Robust Imitation Learning. NeurIPS 2021: 24404-24417 - [c70]Mao Li, Kaiqi Jiang, Xinhua Zhang:
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation. NeurIPS 2021: 25824-25838 - 2020
- [j17]Hengjin Ke, Dan Chen, Tejal Shah, Xianzeng Liu, Xinhua Zhang, Lei Zhang, Xiaoli Li:
Cloud-aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN. Softw. Pract. Exp. 50(5): 596-610 (2020) - [j16]Hengjin Ke, Dan Chen, Benyun Shi, Jindong Zhang, Xianzeng Liu, Xinhua Zhang, Xiaoli Li:
Improving Brain E-Health Services via High-Performance EEG Classification With Grouping Bayesian Optimization. IEEE Trans. Serv. Comput. 13(4): 696-708 (2020) - [c69]Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang:
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. ICML 2020: 6532-6542 - [c68]Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang:
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks. NeurIPS 2020 - [c67]Mao Li, Yingyi Ma, Xinhua Zhang:
Proximal Mapping for Deep Regularization. NeurIPS 2020 - [c66]Hongwei Jin, Xinhua Zhang:
Robust Training of Graph Convolutional Networks via Latent Perturbation. ECML/PKDD (3) 2020: 394-411 - [i14]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. CoRR abs/2002.04197 (2020) - [i13]Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang:
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. CoRR abs/2004.12209 (2020) - [i12]Mao Li, Yingyi Ma, Xinhua Zhang:
Proximal Mapping for Deep Regularization. CoRR abs/2006.07822 (2020)
2010 – 2019
- 2019
- [j15]Peng Li, Xinhua Zhang, Wenlong Zhang:
Direction of Arrival Estimation Using Two Hydrophones: Frequency Diversity Technique for Passive Sonar. Sensors 19(9): 2001 (2019) - [c65]Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang:
Learning Invariant Representations with Kernel Warping. AISTATS 2019: 1003-1012 - [c64]Hengjin Ke, Dan Chen, Lei Zhang, Xinhua Zhang, Xianzeng Liu, Xiaoli Li:
Inter-region Synchronization Analysis Based on Heterogeneous Matrix Similarity Measurement. ICANN (Workshop) 2019: 258-272 - [c63]Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan:
Scaling Multinomial Logistic Regression via Hybrid Parallelism. KDD 2019: 1460-1470 - [c62]Xinhua Zhang, Lance R. Williams:
Euclidean Invariant Recognition of 2D Shapes Using Histograms of Magnitudes of Local Fourier-Mellin Descriptors. WACV 2019: 303-311 - 2018
- [j14]Peng Li, Xinhua Zhang, Lanrui Li, Wenlong Zhang:
An Improved Inverse Beamforming Method: Azimuth Resolution Analysis for Weak Target Detection. Sensors 18(12): 4160 (2018) - [c61]Jun Zhu, Xinhua Zhang:
Foreground Object Detection Combining Gaussian Mixture Model and Inter-Frame Difference in the Application of Classroom recording Apparatus. ICCAE 2018: 111-115 - [c60]Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart:
Efficient and Consistent Adversarial Bipartite Matching. ICML 2018: 1456-1465 - [c59]Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu:
Inductive Two-layer Modeling with Parametric Bregman Transfer. ICML 2018: 1622-1631 - [c58]Xinhua Zhang, Yijing Watkins, Garrett T. Kenyon:
Can Deep Learning Learn the Principle of Closed Contour Detection? ISVC 2018: 455-460 - [c57]Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Graphical Models. NeurIPS 2018: 8354-8365 - [i11]Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang:
Exp-Concavity of Proper Composite Losses. CoRR abs/1805.07737 (2018) - [i10]Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Graphical Models. CoRR abs/1811.02728 (2018) - [i9]Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart:
Consistent Robust Adversarial Prediction for General Multiclass Classification. CoRR abs/1812.07526 (2018) - 2017
- [j13]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. J. Mach. Learn. Res. 18: 144:1-144:46 (2017) - [c56]Xinhua Zhang, Changri Luo, Tingting He, Xiaoli Yang, Zizhou Lu, Baohua Huang:
Online learner emotional analysis based on big dataset of online learning forum. CISP-BMEI 2017: 1-5 - [c55]Mohammad Ali Bashiri, Xinhua Zhang:
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search. NIPS 2017: 2690-2700 - [c54]Zhan Shi, Xinhua Zhang, Yaoliang Yu:
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. NIPS 2017: 6031-6041 - [c53]Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan:
Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem. ECML/PKDD (1) 2017: 460-476 - [r14]Xinhua Zhang:
Covariance Matrix. Encyclopedia of Machine Learning and Data Mining 2017: 290-293 - [r13]Xinhua Zhang:
Empirical Risk Minimization. Encyclopedia of Machine Learning and Data Mining 2017: 392-393 - [r12]Xinhua Zhang:
Gaussian Distribution. Encyclopedia of Machine Learning and Data Mining 2017: 531-535 - [r11]Xinhua Zhang:
Kernel Methods. Encyclopedia of Machine Learning and Data Mining 2017: 690-695 - [r10]Xinhua Zhang:
Regularization. Encyclopedia of Machine Learning and Data Mining 2017: 1083-1088 - [r9]Xinhua Zhang:
Structural Risk Minimization. Encyclopedia of Machine Learning and Data Mining 2017: 1200-1201 - [r8]Xinhua Zhang:
Support Vector Machines. Encyclopedia of Machine Learning and Data Mining 2017: 1214-1220 - 2016
- [j12]Sheng Chen, Yudong Tian, Ali Behrangi, Junjun Hu, Yang Hong, Zengxin Zhang, Phillip M. Stepanian, Baoqing Hu, Xinhua Zhang:
Precipitation Spectra Analysis Over China With High-Resolution Measurements From Optimally Merged Satellite/Gauge Observations - Part I: Spatial and Seasonal Analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 9(7): 2966-2978 (2016) - [j11]Sheng Chen, Ali Behrangi, Yudong Tian, Junjun Hu, Yang Hong, Qiuhong Tang, Xiao-Ming Hu, Phillip M. Stepanian, Baoqing Hu, Xinhua Zhang:
Precipitation Spectra Analysis Over China With High-Resolution Measurements From Optimally-Merged Satellite/Gauge Observations - Part II: Diurnal Variability Analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 9(7): 2979-2988 (2016) - [c52]Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans:
Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016: 1114-1123 - [c51]Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen:
Convex Two-Layer Modeling with Latent Structure. NIPS 2016: 1280-1288 - [i8]Parameswaran Raman, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan:
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression. CoRR abs/1604.04706 (2016) - 2015
- [j10]Changri Luo, Tingting He, Xinhua Zhang, Zibo Zhou:
Learning Forum Posts Topic Discovery and Its Application in Recommendation System. J. Softw. 10(4): 392-402 (2015) - [j9]Hao Guo, Sheng Chen, Anming Bao, Jujun Hu, Abebe S. Gebregiorgis, Xianwu Xue, Xinhua Zhang:
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia. Remote. Sens. 7(6): 7181-7211 (2015) - [j8]Sheng Chen, Junjun Hu, Zengxin Zhang, Ali Behrangi, Yang Hong, Abebe S. Gebregiorgis, Jianrong Cao, Baoqing Hu, Xianwu Xue, Xinhua Zhang:
Hydrologic Evaluation of the TRMM Multisatellite Precipitation Analysis Over Ganjiang Basin in Humid Southeastern China. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 8(9): 4568-4580 (2015) - [j7]Sheng Chen, Jian Zhang, Esther Mullens, Yang Hong, Ali Behrangi, Yudong Tian, Xiao-Ming Hu, Junjun Hu, Zengxin Zhang, Xinhua Zhang:
Mapping the Precipitation Type Distribution Over the Contiguous United States Using NOAA/NSSL National Multi-Sensor Mosaic QPE. IEEE Trans. Geosci. Remote. Sens. 53(8): 4434-4443 (2015) - [j6]Sheng Chen, Yang Hong, Qing Cao, Yudong Tian, Junjun Hu, Xinhua Zhang, Weiyue Li, Nicholas Carr, Xinyi Shen, Lei Qiao:
Intercomparison of Precipitation Estimates From WSR-88D Radar and TRMM Measurement Over Continental United States. IEEE Trans. Geosci. Remote. Sens. 53(8): 4444-4456 (2015) - [c50]Xinhua Zhang, Garrett T. Kenyon:
A Deconvolutional Strategy for Implementing Large Patch Sizes Supports Improved Image Classification. BICT 2015: 529-534 - [c49]Dylan M. Paiton, Sheng Y. Lundquist, William Shainin, Xinhua Zhang, Peter F. Schultz, Garrett T. Kenyon:
A Deconvolutional Competitive Algorithm for Building Sparse Hierarchical Representations. BICT 2015: 535-542 - [c48]Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang:
Exp-Concavity of Proper Composite Losses. COLT 2015: 1035-1065 - 2014
- [j5]Quanxiang Wang, Zhiyue Zhang, Xinhua Zhang, Quanyong Zhu:
Energy-preserving finite volume element method for the improved Boussinesq equation. J. Comput. Phys. 270: 58-69 (2014) - [j4]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Accelerated training of max-margin Markov networks with kernels. Theor. Comput. Sci. 519: 88-102 (2014) - [c47]Hengshuai Yao, Csaba Szepesvári, Bernardo Ávila Pires, Xinhua Zhang:
Pseudo-MDPs and factored linear action models. ADPRL 2014: 1-9 - [c46]Changyou Chen, Jun Zhu, Xinhua Zhang:
Robust Bayesian Max-Margin Clustering. NIPS 2014: 532-540 - [c45]Özlem Aslan, Xinhua Zhang, Dale Schuurmans:
Convex Deep Learning via Normalized Kernels. NIPS 2014: 3275-3283 - [c44]Xianghang Liu, Xinhua Zhang, Tibério S. Caetano:
Bayesian Models for Structured Sparse Estimation via Set Cover Prior. ECML/PKDD (2) 2014: 273-289 - [i7]Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Generalized Conditional Gradient for Sparse Estimation. CoRR abs/1410.4828 (2014) - 2013
- [c43]Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang:
Open Problem: Lower bounds for Boosting with Hadamard Matrices. COLT 2013: 1076-1079 - [c42]Jinghua Ji, Wenxiang Zhao, Qian Chen, Xinhua Zhang, Jianxun Tang, Guohai Liu:
Multi-phase fault-tolerant switched-flux permanent magnet motors having odd rotor pole number. ISIE 2013: 1-4 - [c41]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Polar Operators for Structured Sparse Estimation. NIPS 2013: 82-90 - [c40]Xinhua Zhang, Wee Sun Lee, Yee Whye Teh:
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space. NIPS 2013: 2031-2039 - [c39]Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Two-Layer Modeling. NIPS 2013: 2985-2993 - [c38]Yi Shi, Xinhua Zhang, Xiaoping Liao, Guohui Lin, Dale Schuurmans:
Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM. Pacific Symposium on Biocomputing 2013: 41-52 - [c37]Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Relaxations of Bregman Divergence Clustering. UAI 2013 - [i6]Hao Cheng, Xinhua Zhang, Dale Schuurmans:
Convex Relaxations of Bregman Divergence Clustering. CoRR abs/1309.6823 (2013) - 2012
- [j3]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing multivariate performance measures. J. Mach. Learn. Res. 13: 3623-3680 (2012) - [c36]James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans:
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. ICML 2012 - [c35]Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Convex Multi-view Subspace Learning. NIPS 2012: 1682-1690 - [c34]Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012: 2915-2923 - [c33]Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, Dale Schuurmans:
Sparse Learning Based Linear Coherent Bi-clustering. WABI 2012: 346-364 - [i5]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing Multivariate Performance Measures. CoRR abs/1202.3776 (2012) - 2011
- [c32]Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans:
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. AAAI 2011: 567-573 - [c31]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Accelerated Training of Max-Margin Markov Networks with Kernels. ALT 2011: 292-307 - [c30]Fujun Ren, Xinhua Zhang, Long Wang:
A new method of the image pattern recognition based on neural networks. EMEIT 2011: 3840-3843 - [c29]Xinhua Zhang, Wentao Fan, Zhijun Xia, Chunyu Kang:
Tow ship interference cancelling based on blind source separation algorithm. iCAST 2011: 465-468 - [c28]Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang:
New Approximation Algorithms for Minimum Enclosing Convex Shapes. SODA 2011: 1146-1160 - [c27]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing Multivariate Performance Measures. UAI 2011: 814-821 - 2010
- [j2]Kelin Li, Liping Zhang, Xinhua Zhang, Zuoan Li:
Stability in impulsive Cohen-Grossberg-type BAM neural networks with distributed delays. Appl. Math. Comput. 215(11): 3970-3984 (2010) - [j1]Dong Han, Xinhua Zhang:
Optimal Matrix Filter Design With Application to Filtering Short Data Records. IEEE Signal Process. Lett. 17(5): 521-524 (2010) - [c26]Xinhua Zhang, Zuoan Li, Kelin Li:
Existence and exponential stability of periodic solution for Cohen-Grossberg neural networks with hybrid delays. ICNC 2010: 1093-1097 - [c25]Jinming Liang, Xinhua Zhang:
Periodicity of Cohen-Grossberg-type fuzzy neural networks with time-varying delays and impulses. ICIS 2010: 357-362 - [c24]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Lower Bounds on Rate of Convergence of Cutting Plane Methods. NIPS 2010: 2541-2549 - [c23]Xinhua Zhang, Thore Graepel, Ralf Herbrich:
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. AISTATS 2010: 956-963 - [r7]Xinhua Zhang:
Covariance Matrix. Encyclopedia of Machine Learning 2010: 235-238 - [r6]Xinhua Zhang:
Empirical Risk Minimization. Encyclopedia of Machine Learning 2010: 312 - [r5]Xinhua Zhang:
Gaussian Distribution. Encyclopedia of Machine Learning 2010: 425-428 - [r4]Xinhua Zhang:
Kernel Methods. Encyclopedia of Machine Learning 2010: 566-570 - [r3]Xinhua Zhang:
Regularization. Encyclopedia of Machine Learning 2010: 845-849 - [r2]Xinhua Zhang:
Structural Risk Minimization. Encyclopedia of Machine Learning 2010: 929-930 - [r1]Xinhua Zhang:
Support Vector Machines. Encyclopedia of Machine Learning 2010: 941-946 - [i4]Peng Li, Xinhua Zhang, Yinglin Wang:
SJTU CIT at TAC 2010: Guided Summarization Task. TAC 2010 - [i3]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Faster Rates for training Max-Margin Markov Networks. CoRR abs/1003.1354 (2010) - [i2]Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies. CoRR abs/1011.0472 (2010)
2000 – 2009
- 2009