Kwok, James T.

83 publications

ICCV 2025 Depth Any Event Stream: Enhancing Event-Based Monocular Depth Estimation via Dense-to-Sparse Distillation Jinjing Zhu, Tianbo Pan, Zidong Cao, Yexin Liu, James T. Kwok, Hui Xiong

CVPR 2025 EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions Kai Chen, Yunhao Gou, Runhui Huang, Zhili Liu, Daxin Tan, Jing Xu, Chunwei Wang, Yi Zhu, Yihan Zeng, Kuo Yang, Dingdong Wang, Kun Xiang, Haoyuan Li, Haoli Bai, Jianhua Han, Xiaohui Li, Weike Jin, Nian Xie, Yu Zhang, James T. Kwok, Hengshuang Zhao, Xiaodan Liang, Dit-Yan Yeung, Xiao Chen, Zhenguo Li, Wei Zhang, Qun Liu, Lanqing Hong, Lu Hou, Hang Xu

MLJ 2025 Sandbox: Safeguarded Multi-Label Learning Through Safe Optimal Transport Lefei Zhang, Geng Yu, Jiangchao Yao, Yew-Soon Ong, Ivor W. Tsang, James T. Kwok

NeurIPS 2024 GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning Yanbin Wei, Shuai Fu, Weisen Jiang, Zejian Zhang, Zhixiong Zeng, Qi Wu, James T. Kwok, Yu Zhang

JMLR 2024 Mentored Learning: Improving Generalization and Convergence of Student Learner Xiaofeng Cao, Yaming Guo, Heng Tao Shen, Ivor W. Tsang, James T. Kwok

NeurIPS 2024 RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, Yu Zhang

NeurIPS 2023 Efficient Hyper-Parameter Optimization with Cubic Regularization Zhenqian Shen, Hansi Yang, Yong Li, James T. Kwok, Quanming Yao

NeurIPS 2023 Nonparametric Teaching for Multiple Learners Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok

JMLR 2022 Low-Rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok

NeurIPS 2022 Multi-Objective Deep Learning with Adaptive Reference Vectors Weiyu Chen, James T. Kwok

ECML-PKDD 2021 Dropout's Dream Land: Generalization from Learned Simulators to Reality Zac Wellmer, James T. Kwok

NeurIPS 2021 Effective Meta-Regularization by Kernelized Proximal Regularization Weisen Jiang, James T. Kwok, Yu Zhang

NeurIPS 2021 TOHAN: A One-Step Approach Towards Few-Shot Hypothesis Adaptation Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William Cheung, James T. Kwok

AAAI 2021 Time Series Anomaly Detection with Multiresolution Ensemble Decoding Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok

NeurIPS 2020 Bridging the Gap Between Sample-Based and One-Shot Neural Architecture Search with BONAS Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang

AAAI 2020 Effective Decoding in Graph Auto-Encoder Using Triadic Closure Han Shi, Haozheng Fan, James T. Kwok

NeurIPS 2020 Timeseries Anomaly Detection Using Temporal Hierarchical One-Class Network Lifeng Shen, Zhuocong Li, James T. Kwok

ICLR 2019 Analysis of Quantized Models Lu Hou, Ruiliang Zhang, James T. Kwok

ECML-PKDD 2019 Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space Zac Wellmer, James T. Kwok

IJCAI 2019 Privacy-Preserving Stacking with Application to Cross-Organizational Diabetes Prediction Quanming Yao, Xiawei Guo, James T. Kwok, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang

ICLR 2018 Loss-Aware Weight Quantization of Deep Networks Lu Hou, James T. Kwok

IJCAI 2017 Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems Quanming Yao, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu

ICML 2017 Follow the Moving Leader in Deep Learning Shuai Zheng, James T. Kwok

ICLR 2017 Loss-Aware Binarization of Deep Networks Lu Hou, Quanming Yao, James T. Kwok

ECML-PKDD 2016 Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering Xiawei Guo, James T. Kwok

AAAI 2016 Asynchronous Distributed Semi-Stochastic Gradient Optimization Ruiliang Zhang, Shuai Zheng, James T. Kwok

AAAI 2016 Efficient Learning of Timeseries Shapelets Lu Hou, James T. Kwok, Jacek M. Zurada

AAAI 2016 Fast Nonsmooth Regularized Risk Minimization with Continuation Shuai Zheng, Ruiliang Zhang, James T. Kwok

IJCAI 2016 Fast-and-Light Stochastic ADMM Shuai Zheng, James T. Kwok

IJCAI 2016 Greedy Learning of Generalized Low-Rank Models Quanming Yao, James T. Kwok

AAAI 2016 Towards Safe Semi-Supervised Learning for Multivariate Performance Measures Yufeng Li, James T. Kwok, Zhi-Hua Zhou

IJCAI 2015 Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion Quanming Yao, James T. Kwok

AAAI 2015 Colorization by Patch-Based Local Low-Rank Matrix Completion Quanming Yao, James T. Kwok

AAAI 2014 Accurate Integration of Aerosol Predictions by Smoothing on a Manifold Shuai Zheng, James T. Kwok

AAAI 2014 Gradient Descent with Proximal Average for Nonconvex and Composite Regularization Wenliang Zhong, James T. Kwok

UAI 2014 Learning to Predict from Crowdsourced Data Wei Bi, Liwei Wang, James T. Kwok, Zhuowen Tu

AAAI 2014 Multilabel Classification with Label Correlations and Missing Labels Wei Bi, James T. Kwok

IJCAI 2013 Accurate Probability Calibration for Multiple Classifiers Wenliang Zhong, James T. Kwok

JMLR 2013 Convex and Scalable Weakly Labeled SVMs Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou

IJCAI 2013 Efficient Kernel Learning from Side Information Using ADMM En-Liang Hu, James T. Kwok

NeurIPS 2012 Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification Wei Bi, James T. Kwok

NeurIPS 2012 Priors for Diversity in Generative Latent Variable Models James T. Kwok, Ryan P. Adams

ICML 2011 Efficient Sparse Modeling with Automatic Feature Grouping Wenliang Zhong, James T. Kwok

ICML 2011 MultiLabel Classification on Tree- and DAG-Structured Hierarchies Wei Bi, James T. Kwok

CVPR 2011 Time and Space Efficient Spectral Clustering via Column Sampling Mu Li, Xiao-Chen Lian, James T. Kwok, Bao-Liang Lu

AAAI 2010 Cost-Sensitive Semi-Supervised Support Vector Machine Yufeng Li, James T. Kwok, Zhi-Hua Zhou

ICML 2010 Making Large-Scale Nyström Approximation Possible Mu Li, James T. Kwok, Bao-Liang Lu

CVPR 2010 Online Multiple Instance Learning with No Regret Mu Li, James T. Kwok, Bao-Liang Lu

ECML-PKDD 2009 A Convex Method for Locating Regions of Interest with Multi-Instance Learning Yufeng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou

NeurIPS 2009 Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu, Weike Pan, James T. Kwok

IJCAI 2009 Domain Adaptation via Transfer Component Analysis Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang

ICML 2009 Prototype Vector Machine for Large Scale Semi-Supervised Learning Kai Zhang, James T. Kwok, Bahram Parvin

ICML 2009 Semi-Supervised Learning Using Label Mean Yufeng Li, James T. Kwok, Zhi-Hua Zhou

ICML 2008 Improved Nyström Low-Rank Approximation and Error Analysis Kai Zhang, Ivor W. Tsang, James T. Kwok

AAAI 2008 Transfer Learning via Dimensionality Reduction Sinno Jialin Pan, James T. Kwok, Qiang Yang

AAAI 2008 Transferring Localization Models Across Space Sinno Jialin Pan, Dou Shen, Qiang Yang, James T. Kwok

AAAI 2007 Adaptive Localization in a Dynamic WiFi Environment Through Multi-View Learning Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan

IJCAI 2007 Ensembles of Partially Trained SVMs with Multiplicative Updates Ivor W. Tsang, James T. Kwok

IJCAI 2007 Marginalized Multi-Instance Kernels James T. Kwok, Pak-Ming Cheung

ICML 2007 Maximum Margin Clustering Made Practical Kai Zhang, Ivor W. Tsang, James T. Kwok

ICML 2007 Simpler Core Vector Machines with Enclosing Balls Ivor W. Tsang, András Kocsor, James T. Kwok

MLJ 2007 Surrogate Maximization/minimization Algorithms and Extensions Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

ICML 2006 A Regularization Framework for Multiple-Instance Learning Pak-Ming Cheung, James T. Kwok

ECCV 2006 Accelerated Convergence Using Dynamic Mean Shift Kai Zhang, James T. Kwok, Ming Tang

ICML 2006 Block-Quantized Kernel Matrix for Fast Spectral Embedding Kai Zhang, James T. Kwok

ECML-PKDD 2006 Diversified SVM Ensembles for Large Data Sets Ivor W. Tsang, András Kocsor, James T. Kwok

NeurIPS 2006 Large-Scale Sparsified Manifold Regularization Ivor W. Tsang, James T. Kwok

ICML 2006 Locally Adaptive Classification Piloted by Uncertainty Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok

MLJ 2006 Model-Based Transductive Learning of the Kernel Matrix Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

NeurIPS 2006 Simplifying Mixture Models Through Function Approximation Kai Zhang, James T. Kwok

IJCAI 2005 Accurate and Low-Cost Location Estimation Using Kernels Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen

CVPR 2005 Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation Kai Zhang, Ming Tang, James T. Kwok

JMLR 2005 Core Vector Machines: Fast SVM Training on Very Large Data Sets Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung

ICML 2005 Core Vector Regression for Very Large Regression Problems Ivor W. Tsang, James T. Kwok, Kimo T. Lai

ICML 2004 Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm Zhihua Zhang, Dit-Yan Yeung, James T. Kwok

AAAI 2004 Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung

ECML-PKDD 2004 Efficient Hyperkernel Learning Using Second-Order Cone Programming Ivor W. Tsang, James T. Kwok

ICML 2004 Surrogate Maximization/minimization Algorithms for AdaBoost and the Logistic Regression Model Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

NeurIPS 2003 Eigenvoice Speaker Adaptation via Composite Kernel Principal Component Analysis James T. Kwok, Brian Mak, Simon Ho

ICML 2003 Learning with Idealized Kernels James T. Kwok, Ivor W. Tsang

IJCAI 2003 Parametric Distance Metric Learning with Label Information Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

ICML 2003 The Pre-Image Problem in Kernel Methods James T. Kwok, Ivor W. Tsang

ECML-PKDD 2001 Applying the Bayesian Evidence Framework to \nu -Support Vector Regression Martin H. C. Law, James T. Kwok