Preprints

• Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang , “Adversarially Robust Generalization Just Requires More Unlabeled Data ”

• Jing Xu, Sen Wang, Liwei Wang , Andrew Chi-Chih Yao , “FedCM: Federated Learning with Client-level Momentum ”

Conference Papers:

• Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, Liwei Wang , , “CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds ”, Conference on Neural Information Processing Systems (NeurIPS), 2022

• Bohang Zhang, Du Jiang, Di He, Liwei Wang , , “Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective ”, Conference on Neural Information Processing Systems (NeurIPS), 2022 (Oral)

• Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang , Di He, , “Your Transformer May Not be as Powerful as You Expect ”, Conference on Neural Information Processing Systems (NeurIPS), 2022

• Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft,Liwei Wang , , “Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power ”, Conference on Neural Information Processing Systems (NeurIPS), 2022

• Chuwei Wang, Shanda Li, Di He, Liwei Wang , “Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network ”, Conference on Neural Information Processing Systems (NeurIPS), 2022

• Dong Wang, Zhao Zhang, Ziwei Zhao, Yuhang Liu, Yihong Chen, Liwei Wang , , “PointScatter: Point Set Representation for Tubular Structure Extraction ”, European Conference on Computer Vision (ECCV), 2022 (Oral)

• Ziwei Zhao, Dong Wang, Yihong Chen, Ziteng Wang, Liwei Wang , , “Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection ”, European Conference on Computer Vision (ECCV), 2022

• Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang , Simon S. Du, Jiantao Jiao , “Nearly Optimal Policy Optimization with Stable at Any Time Guarantee ”, The 39th International Conference on Machine Learning (ICML 2022)

• Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang , “Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation ”, The 39th International Conference on Machine Learning (ICML 2022)

• Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang , Tong Zhang, Zhaoran Wang, Zhuoran Yang , “Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets ”, The 39th International Conference on Machine Learning (ICML 2022)

• Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, Liwei Wang , “RBGNet: Ray-based Grouping for 3D Object Detection ”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2022

• Xiaoyu Chen, Jiachen Hu, Lin F. Yang, Liwei Wang , “Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver ”, International Conference on Learning Representations (ICLR), 2022 (Spotlight)

• Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang , “Understanding Domain Randomization for Sim-to-real Transfer ”, International Conference on Learning Representations (ICLR), 2022 (Spotlight)

• Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang , Simon Shaolei Du , “A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning ”, International Conference on Learning Representations (ICLR), 2022

• Bohang Zhang, Du Jiang, Di He, Liwei Wang , “Boosting the Certified Robustness of L-infinity Distance Nets ”, International Conference on Learning Representations (ICLR), 2022

• Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang , Tie-Yan Liu , “Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding ”, NeurIPS 2021

• Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang , “Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis ”, NeurIPS 2021

• Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang , “Towards a Theoretical Framework of Out-of-Distribution Generalization ”, NeurIPS 2021

• Han Zhong, Jiayi Huang, Lin F. Yang, Liwei Wang , “Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs ”, NeurIPS 2021

• Hanzhe Hu，Fangyun Wei，Han Hu，Qiwei Ye，Jinshi Cui，Liwei Wang , “Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning ”, NeurIPS 2021

• Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang , “Towards Certifying $\ell_\infty$ Robustness using Neural Networks with $\ell_\infty$-dist Neurons ”, The 38th International Conference on Machine Learning, (ICML), 2021.

• Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang , “GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training ”, The 38th International Conference on Machine Learning, (ICML), 2021.

• Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang , “Near-optimal Representation Learning for Linear Bandits and Linear RL ”, The 38th International Conference on Machine Learning, (ICML), 2021.

• Tianhao Wu, Yunchang Yang, Simon S. Du, Liwei Wang , “On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP ”, The 38th International Conference on Machine Learning, (ICML), 2021.

• Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, Liwei Wang , “Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection ”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

• Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang , “Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL ”, International Conference on Learning Representations (ICLR), 2021

• Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang , Jason D. Lee , “Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot ”, Conference on Neural Information Processing Systems (NeurIPS), 2020

• Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang , “Locally Differentially Private (Contextual) Bandits Learning ”, Conference on Neural Information Processing Systems (NeurIPS), 2020

• Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang , Stephen Lin, Han Hu , “RepPoints V2: Verification Meets Regression for Object Detection ”, Conference on Neural Information Processing Systems (NeurIPS), 2020

• Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang , “Improved Analysis of Clipping Algorithms for Non-convex Optimization ”, Conference on Neural Information Processing Systems (NeurIPS), 2020

• Ze Yang, Yinghao Xu, Han Xue, Zheng Zhang, Raquel Urtasun, Liwei Wang , Stephen Lin, Han Hu , “Dense RepPoints: Representing Visual Objects with Dense Point Sets ”, European Conference on Computer Vision (ECCV), 2020.

• Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang , Tie-Yan Liu , “On Layer Normalization in the Transformer Architecture ”, The 37th International Conference on Machine Learning, (ICML), 2020.

• Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang , “(Locally) Differentially Private Combinatorial Semi-Bandits ”, The 37th International Conference on Machine Learning, (ICML), 2020.

• Yihong Chen, Yue Cao, Han Hu, Liwei Wang , “Memory Enhanced Global-Local Aggregation for Video Object Detection ”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

• Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang , “FocalMix: Semi-Supervised Learning for 3D Medical Image Detection ”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

• Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, Liwei Wang , “Boosting Few-Shot Learning With Adaptive Margin Loss ”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

• Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang, “MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius”, International Conference on Learning Representations (ICLR), 2020

• Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Liwei Wang, Hao Su, “Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories”, International Conference on Learning Representations (ICLR), 2020

• Yuanhao Wang, Kefan Dong, Xiaoyu Chen, Liwei Wang, “Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP”, International Conference on Learning Representations (ICLR), 2020

• Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, Liwei Wang, “Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication”, International Conference on Learning Representations (ICLR), 2020

• Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft, “Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks”, International Conference on Learning Representations (ICLR), 2020

• Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang , John E. Hopcroft, “Robust Local Features for Improving the Generalization of Adversarial Training”, International Conference on Learning Representations (ICLR), 2020

• Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee, “Convergence of Adversarial Training in Overparametrized Networks”, Conference on Neural Information Processing Systems (NeurIPS), 2019.

• Rui (Ray) Zhang, Xingwu Liu, Yuyi Wang, Liwei Wang, “McDiarmid-Type Inequalities for Graph-DependentVariables and Stability Bounds”, Conference on Neural Information Processing Systems (NeurIPS), 2019.

• Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang, “Equipping Experts/Bandits with Long-term Memory”, Conference on Neural Information Processing Systems (NeurIPS), 2019.

• Ze Yang, Liwei Wang, “Learning Relationships for Multi-View 3D Object Recognition”, International Conference on Computer Vision (ICCV), 2019.

• Ze Yang, Shaohui Liu, Han Hu, Liwei Wang, Steve Lin, “RepPoints: Point Set Representation for Object Detection”, International Conference on Computer Vision (ICCV), 2019.

• Tiange Luo, Aoxue Li, Tao Xiang, Weiran Huang, Liwei Wang, “Few-Shot Learning with Global Class Representations”, International Conference on Computer Vision (ICCV), 2019.

• Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai, “Gradient Descent Finds Global Minima of Deep Neural Networks”, The 36th International Conference on Machine Learning (ICML), 2019.

• Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tieyan Liu, “Efficient Training of BERT by Progressively Stacking”, The 36th International Conference on Machine Learning (ICML), 2019.

• Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, Liwei Wang, “Large-Scale Few-shot Learning: Knowledge Transfer With Class Hierarchy”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

• Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tieyan Liu, “Representation Degeneration Problem in Training Natural Language Generation Models”, The 7th International Conference on Learning Representations (ICLR), 2019.

• Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft, “Improving the Generalization of Adversarial Training with Domain Adaptation”, The 7th International Conference on Learning Representations (ICLR), 2019.

• Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John Hopcroft, “To What Extent Do Different Neural Networks Learn the Same Representation: A Neuron Activation Subspace Match Approach”, Conference on Neural Information Processing Systems (NeurIPS), 2018.

• Chengyue Gong,Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu, “FRAGE: Frequency-Agnostic Word Representation”, Conference on Neural Information Processing Systems (NeurIPS), 2018.

• Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang, “Learning to Navigate for Fine-grained Classification”, European Conference on Computer Vision (ECCV), 2018.

• Jiayuan Gu, Han Hu, Liwei Wang, Yichen Wei, Jifeng Dai, “Learning Region Features for Object Detection”, European Conference on Computer Vision (ECCV), 2018.

• Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang, “Dropout Training, Data-dependent Regularization, and Generalization Bounds”, The 35th International Conference on Machine Learning (ICML), 2018.

• Di He, Zhuohan Li, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tieyan Liu, “Towards Binary-Valued Gates for Robust LSTM Training”, The 35th International Conference on Machine Learning (ICML), 2018.

• Wenlong Mou, Liwei Wang, Xiyu Zhai, Kai Zheng, “Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints”, Annual Conference on Learning Theory (COLT), 2018.

• Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tieyan Liu, “Decoding with Value Networks for Neural Machine Translation”, Conference on Neural Information Processing Systems (NeurIPS), 2017.

• Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang, “The Expressive Power of Neural Networks: A View from the Width”, Conference on Neural Information Processing Systems (NeurIPS), 2017.

• Jia Ding, Aoxue Li, Zhiqiang Hu, Liwei Wang, “Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks”, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2017.

• Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang, “Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes”, Annual Conference on Learning Theory (COLT), 2017.

• Kai Zheng, Wenlong Mou, Liwei Wang, “Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible”, The 34th International Conference on Machine Learning (ICML), 2017.

• Jiaqi Zhang, Kai Zheng, Wenlong Mou, Liwei Wang, “Efficient Private ERM for Smooth Objectives”, The Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), 2017.

• Wenlong Mou, Liwei Wang, “A Refined Analysis of LSH for Well-dispersed Data Points”, Meeting on Analytic Algorithms and Combinatorics (ANALCO), 2017.

• Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma, “Dual Learning for Machine Translation”, Advances in Neural Information Processing Systems (NeurIPS), 2016.

• Shizhao Sun, Wei Chen, Liwei Wang, Xiaoguang Liu, Tie-Yan Liu, “On the Depth of Deep Neural Networks: A Theoretical View”, The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), 2016.

• Tong Lin, Yao Liu, Bo Wang, Liwei Wang, Hongbin Zha, “Local Orthogonality Preserving Alignment for Nonlinear Dimensionality Reduction”, The Fourth International Conference on Computational Visual Media, 2016.

• Zhiwu Lu, Xin Gao, Songfang Huang, Liwei Wang, Ji-Rong Wen, “Social Image Parsing by Cross-Modal Data Refinement”, The Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015.

• Zhiwu Lu, Xin Gao, Liwei Wang, Ji-Rong Wen, Songfang Huang, “Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding”, The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015.

• Zhiwu Lu, Liwei Wang, Ji-Rong Wen, “Direct Semantic Analysis for Social Image Classification”, the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014.

• Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang, “Generalized Second Price Auction with Probabilistic Broad Match”, Proceedings of the fifteenth ACM conference on Economics and computation, 2014.

• Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang, “Efficient Algorithm for Privately Releasing Smooth Queries”, Advances in Neural Information Processing Systems (NeurIPS), 2013.

• Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, Tie-Yan Liu, “A Theoretical Analysis of NDCG Type Ranking Measures”, Annual Conference on Learning Theory (COLT), 2013.

• Di He, Wei Chen, Liwei Wang, Tie-Yan Liu, “A Game-Theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search”, International Joint Conference on Artificial Intelligence (IJCAI), 2013.

• Kai Fan, Hongyi Zhang, Yu Zang, Liwei Wang, “Estimation based on RBM from label proportions in large group case", Intelligent Science and Intelligent Data Engineering, 2013.

• Chi Jin, Liwei Wang, “Dimensionality Dependent PAC-Bayes Margin Bounds”, Advances in Neural Information Processing Systems (NeurIPS), 2012.

• Mingyang Jiang, Chunxiao Li, Zirui Deng, Jufu Feng, Liwei Wang, “Learning from error: A two-level combined model for image classification”, Asian Conference on Pattern Recognition, 2011.

• Mingyang Jiang, Chunxiao Li, Jufu Feng, Liwei Wang, “Towards Hypothesis Testing and Lossy Minimum Description Length: A Unified Segmentation Framework”, Asian Conference on Computer Vision, 2010.

• Mingyang Jiang, Chunxiao Li, Jufu Feng, Liwei Wang, “Segmentation via NCuts and Lossy Minimum Description Length: A Unified Approach”, Asian Conference on Computer Vision, 2010.

• Liwei Wang, “Sufficient Conditions for Agnostic Active Learnable”, Advances in Neural Information Processing Systems (NeurIPS), 2009.

• Bing Sun, Jufu Feng, Liwei Wang, “Learning IMED via shift-invariant transformation”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009.

• Cheng Yang, Liwei Wang, Jufu Feng, “A Comparative Study of Two Kernel Ideas for Nonlinear Feature Extraction”, SPIE The Sixth International Symposium on Multispectral Image Processing & Pattern Recognition, 2009.

• Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng, “On the Margin Explanation of Boosting Algorithms”, Annual Conference on Learning Theory (COLT), 2008.

• Liwei Wang, Cheng Yang, Jufu Feng, “On learning with dissimilarity functions”, International Conference on Machine Learning (ICML), 2007.

• Liwei Wang, Jufu Feng, “Rademacher margin complexity”, Annual Conference on Learning Theory (COLT), 2007.

• Liwei Wang, Jufu Feng, “Learning Gaussian mixture models by structural risk minimization”, International Conference on Machine Learning and Cybernetics, 2005.

• Liwei Wang, Jufu Feng, “MDL transduction”, International Conference on Machine Learning and Cybernetics, 2005.

• Liwei Wang, Jufu Feng, “Maximum a posteriori transduction”, International Conference on Machine Learning and Cybernetics, 2004.

• Liwei Wang, Zuoying Wang, “Testing the hypothesis of multi-variate normality in Bayesian approaches to speaker adaptation”, International Symposium on Speech and language Processing, 2002.

   

Journal Articles:

• Aoxue Li, Zhiwu Lu, Liwei Wang, Tao Xiang, Ji-Rong Wen, “Zero shot scene classification for high spatial resolution remote sensing images”, IEEE Transactions on Geoscience and remote sensing, to appear, 2017.

• Aoxue Li, Zhiwu Lu, Liwei Wang, Peng Han, Ji-Rong Wen, “Large Scale Sparse Learning from Noisy Tags for Semantic Segmentation”, IEEE Transactions on Cybernetics, to appear, 2017

• Zhiwu Lu, Zhenyong Fu, Tao Xiang, Peng Han, Liwei Wang, Xin Gao, “Learning from Weak and Noisy Labels for Semantic Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, issue. 3, pp. 486-500, 2017.

• Zhiwu Lu, Liwei Wang, Ji-Rong Wen, “Image Classification by Visual Bag-of-Words Refinement and Reduction”, Neurocomputing, vol. 173, pp. 373-384, 2016.

• Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang, “Differentially Private Data Releasing for Smooth Queries”, Journal of Machine Learning Research, vol. 17, no. 51, pp. 1-42, 2016.

• Wayne Xin Zhao, Sui Li, Yulan He, Liwei Wang, Ji-Rong Wen, Xiaoming Li, “Exploring Demographic Information in Social Media for Product Recommendation”, Knowledge and Information Systems, pp. 1-29, 2016.

• Liwei Wang, Jiapeng Zhang, “On the measurement complexity of differentially private query answering”, Science China Information Sciences, vol. 58, no. 9, pp. 1-11, 2015.

• Zhiwu Lu, Liwei Wang, “Noise-robust semi-supervised learning via fast sparse coding”, Pattern Recognition, vol. 48, no. 2, pp. 605-612, 2015.

• Zhiwu Lu, Liwei Wang, “Learning descriptive visual representation for image classification and annotation”, Pattern Recognition, vol. 48, no. 2, pp. 498-508, 2015.

• Zhiwu Lu, Peng Han, Liwei Wang, Ji-Rong Wen, “Semantic Sparse Recoding of Visual Content for Image Applications”, IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 176-188, 2015.

• Kai Fan, Hongyi Zhang, Songbai Yan, Liwei Wang, Wensheng Zhang, Jufu Feng, “Learning a generative classifier from label proportions”, Neurocomputing, vol. 139, pp. 47-55, 2014.

• Di He, Wei Chen, Liwei Wang, Tie-Yan Liu, “Online learning for auction mechanism in bandit setting”, Decision Support Systems, vol. 56, no. 12, pp. 379-386, 2013.

• Liwei Wang, Xiaocheng Deng, Zhaoxiang Jing, Jufu Feng, “Further Results on the Margin Explanation of Boosting: New Algorithms and Experiments”, Science China Information Sciences, vol. 55, pp. 7, pp. 1551-1562, 2012.

• Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng, “A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin”, Journal of Machine Learning Research, vol. 12, no. 6, pp. 1835-1863, 2011.

• Liwei Wang, “Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning”, Journal of Machine Learning Research, vol. 12, no. 7, pp. 2269-2292, 2011.

• Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, Jufu Feng, “Theory and Algorithm for Learning with Dissimilarity Functions”, Neural Computation, vol. 21, no. 5, pp. 1459-1484, 2009.

• Cheng Yang, Liwei Wang, Jufu Feng, “A Novel Margin Based Algorithm for Feature Extraction”, New Generation Computing, vol. 27, no. 4, pp. 285-305, 2009.

• Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang, “A density-ratio framework for statistical data processing”, IPSJ Transactions on Computer Vision and Applications, vol. 1, no. 3, pp. 183-208, 2009.

• Cheng Yang, Liwei Wang, Jufu Feng, “On feature extraction via kernels”, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 38, no. 2, pp. 553-557, 2008.

• Xichen Sun, Liwei Wang, Jufu Feng, “Further results on the subspace distance”, Pattern Recognition, vol. 40, no. 1, pp. 328-329, 2007.

• Liwei Wang, Xiao Wang, Jufu Feng, “On image matrix based feature extraction algorithms”, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 36, no.1, pp. 194-197, 2006.

• Liwei Wang, Xiao Wang, Jufu Feng, “Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition”, Pattern Recognition, vol. 39, no. 3, pp. 456-464, 2006.

• Liwei Wang, Jufu Feng, “Comments on “Fundamental limits of reconstruction-based superresolution algorithms under local translation””, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 846-846, 2006.

• Liwei Wang, Yan Zhang, Jufu Feng, “On the Euclidean distance of images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1334-1339, 2005.

• Liwei Wang, Xiao Wang, Jufu Feng, “Intrapersonal subspace analysis with application to adaptive Bayesian face recognition”, Pattern Recognition, vol. 38, no. 4, pp. 617-621, 2005.

• Liwei Wang, Xiao Wang, Xuerong Zhang, Jufu Feng, “The equivalence of two-dimensional PCA to line-based PCA”, Pattern Recognition Letters, vol. 26, no. 1, pp. 57-60, 2005.

• Liwei Wang, “Parallel and Sequential Support Vector Machines for Multi-Label Classification”, International Journal of Information Technology, vol. 11, no. 09, pp. 11-18, 2005.

• Liwei Wang, Xiao Wang, Ming Chang, Jufu Feng, “Is two-dimensional PCA a new technique”, Acta Automatica Sinica, vol. 31, no. 5, pp. 782-787, 2005.

• Hongyi Zhang, Liwei Wang, Yuxi Chen, “Research Progress of Probabilistic Graphical Models: A Survey”, Journal of Software, vol. 11, pp. 2476-2497, 2013.

• 张宏毅，王立威，陈瑜希，“概率图模型研究进展综述”，软件学报，vol. 11, pp. 2476-2497, 2013.

Refereed Workshop Papers:

• Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang, “Direct importance estimation—A new versatile tool for statistical pattern recognition”, Meeting on Image Recognition and Understanding (MIRU), Nagano, Japan, Jul. 29-31, 2008.

• Liwei Wang, Masashi Sugiyama, “Equilibrium Margin: A New Concept for Characterizing the Generalization Error of Voting Classifiers”, Workshop on Information Based Induction Sciences (IBIS), Tokyo, Japan, 2007.

   

Book Chapters:

• Liwei Wang, Kai Zheng, “Machine Learning in Differential Privacy”, in Machine Learning and Applications, X. Gao and J. Zhang eds., 2015.

• 王立威，郑凯，“差分隐私保护的机器学习”，机器学习及其应用2015，清华大学出版社，高新波，张军平 主编

• Liwei Wang, Zhaoxiang Jing, “Theoretical Explanations of Boosting Algorithms”, in Machine Learning and Applications, Z. Zhou and J. Wang eds., 2009.

• 王立威，景兆祥，“关于Boosting算法的Margin解释”，机器学习及其应用2009，清华大学出版社，周志华，王钰 主编