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(* indicates equal contributions # indicates alphabetical author order)
2019
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations.
Jialei Wang, and Tong Zhang.
Journal of Machine Learning Research (JMLR), 2019.
Stochastic Canonical Correlation Analysis.
Chao Gao#, Dan Garber#, Nathan Srebro#, Jialei Wang# and Weiran Wang#.
Journal of Machine Learning Research (JMLR), 2019.
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates.
Yining Wang, Jialei Wang, Sivaraman Balakrishnan, and Aarti Singh.
Journal of Multivariate Analysis (JMVA), 2019.
2018
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.
Blake Woodworth, Jialei Wang, Brendan McMahan, and Nathan Srebro.
Conference on Neural Information Processing Systems (NeurIPS), 2018.
Gradient Sparsification for Communication-Efficient Distributed Optimization.
Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang.
Conference on Neural Information Processing Systems (NeurIPS), 2018.
Distributed Stochastic Multi-Task Learning with Graph Regularization.
Weiran Wang, Jialei Wang, Mladen Kolar, and Nathan Srebro.
Efficient coordinate-wise leading eigenvector computation.
Jialei Wang*, Weiran Wang*, Dan Garber, and Nathan Srebro.
International Conference on Algorithmic Learning Theory (ALT), 2018.
2017
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization.
Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, and Tong Zhang.
Journal of Machine Learning Research (JMLR), 2017.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms.
Jialei Wang, and Lin Xiao.
International Conference on Machine Learning (ICML), 2017.
Efficient Distributed Learning with Sparsity.
Jialei Wang, Mladen Kolar, Nathan Srebro, and Tong Zhang.
International Conference on Machine Learning (ICML), 2017.
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox.
Jialei Wang*, Weiran Wang*, and Nathan Srebro.
Conference on Learning Theory (COLT), 2017.
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data.
Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, and Nathan Srebro.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
Electronic Journals of Statistics (EJS), 2017.
2016
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis.
Weiran Wang*, Jialei Wang*, Dan Garber, and Nathan Srebro.
Conference on Neural Information Processing Systems (NIPS), 2016.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion.
Jialei Wang, Pedre A. Olsen, Andrew R. Conn and Aurelie C. Lozano.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Distributed Multi-Task Learning.
Jialei Wang, Mladen Kolar, and Nathan Srebro.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
Inference for High-dimensional Exponential Family Graphical Models.
Jialei Wang, and Mladen Kolar.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
Transaction Costs Optimization for Online Portfolio Selection.
Bin Li, Jialei Wang, Steven C.H. Hoi, and Dingjiang Huang.
Quantitative Finance, 2016.
2015
Trading Interpretability for Accuracy: Oblique Treed Sparse Additive Models.
Jialei Wang, Ryohei Fujimaki, and Yosuke Motohashi.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.
SOLAR: Scalable Online Learning Algorithms for Ranking.
Jialei Wang, Ji Wan, Yongdong Zhang, and Steven C.H. Hoi.
Annual Meeting of the Association for Computational Linguistics (ACL), 2015.
Large Scale Online Kernel Learning.
Jing Lu, Steven C.H. Hoi, Jialei Wang, Peilin Zhao, and Zhiyong Liu.
Journal of Machine Learning Research (JMLR), 2015.
2014
Active Collaborative Permutation Learning.
Jialei Wang, Nathan Srebro, and James Evans.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014.
A Consistent Estimator of the Expected Gradient Outerproduct.
Shubhendu Trived*, Jialei Wang*, Samory Kpotufe, and Gregory Shakhnarovich.
Conference on Uncertainty in Artificial Intelligence (UAI), 2014.
LIBOL: A Library for Online Learning Algorithms.
Steven C.H. Hoi, Jialei Wang, and Peilin Zhao.
Journal of Machine Learning Research (JMLR), 2014.
Online Transfer Learning.
Peilin Zhao, Steven C.H. Hoi, Jialei Wang, and Bin Li.
Artificial Intelligence (AIJ), 2014.
2013 and before
Cost-Sensitive Online Classification.
Jialei Wang, Peilin Zhao, Steven C.H. Hoi.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013.
Online Feature Selection and Its Applications.
Jialei Wang, Peilin Zhao, Steven C.H. Hoi and Rong Jin.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013.
Online multi-task collaborative filtering for on-the-fly recommender systems.
Jialei Wang, Steven C.H. Hoi, Peilin Zhao, and Zhiyong Liu.
The ACM Recommender System conference (Recsys), 2013.
Large-scale online kernel classification.
Jialei Wang, Steven C.H. Hoi, Peilin Zhao, Jinfeng Zhuang and Zhiyong Liu.
International Joint Conferences on Artificial Intelligence (IJCAI), 2013.
Exact Soft Confidence-Weight Learning.
Jialei Wang, Peilin Zhao, and Steven C.H. Hoi.
International Conference on Machine Learning (ICML), 2012.
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning.
Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, and Steven C.H. Hoi.
International Conference on Machine Learning (ICML), 2012.
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