Research Interests

My research interests are as follows.

Journal Articles

Weighted SGD for Lp Regression with Randomized Preconditioning
Jiyan Yang, Yin-Lam Chow, Christopher Ré, and Michael W. Mahoney
To appear in J. Machine Learning Research. [paper] [arXiv] [slides]

Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
Jiyan Yang, Xiangrui Meng, and Michael W. Mahoney
Proceedings of the IEEE, 104(1), 58-92, 2016. [paper] [arXiv] [codes] [slides]

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Haim Avron*, Vikas Sindhwani*, Jiyan Yang*, and Michael W. Mahoney
*alphabetical authorship order.
J. Machine Learning Research, 17(120), 1-38, 2016. [paper] [arXiv] [codes] [slides]

Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty
Junjie Qin, Yin-Lam Chow, Jiyan Yang, and Ram Rajagopal
IEEE Transactions on Smart Grid, 7(2), 1106-1118, 2016. [paper] [arXiv]

Online Modified Greedy Algorithm for Storage Control under Uncertainty
Junjie Qin, Yin-Lam Chow, Jiyan Yang, and Ram Rajagopal
IEEE Transactions on Power Systems, 31(3), 1729-1743, 2016. [paper] [arXiv]

Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions
Jiyan Yang, Oliver Rübel, Prabhat, Michael W. Mahoney, and Ben P. Bowen
Analytical Chemistry, 87(9), 4658-4666, 2015. [paper] [codes]

Quantile Regression for Large-scale Applications
Jiyan Yang, Xiangrui Meng, and Michael W. Mahoney
SIAM J. Scientific Computing, 36(5), S78-S110, 2014. [paper] [arXiv] [codes] [slides]

Conference Proceedings

Feature-distributed Sparse Regression: A Screen-and-clean Approach
Jiyan Yang, Michael W. Mahoney, Michael Saunders, and Yuekai Sun
Neural Information Processing Systems (NIPS), 2016. [paper]

Sub-sampled Newton Methods with Non-uniform Sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W. Mahoney
Neural Information Processing Systems (NIPS), 2016. [paper] [arXiv (long version)] [slides]

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
Alex Gittens et al.
IEEE International Conference on Big Data (IEEE BigData), 2016. [paper] [arXiv] [codes]

Weighted SGD for Lp Regression with Randomized Preconditioning
Jiyan Yang, Yin-Lam Chow, Christopher Ré, and Michael W. Mahoney
ACM-SIAM Symposium on Discrete Algorithms (SODA), 2016. [paper] [arXiv (long version)] [slides]

Modeling and Online Control of Generalized Energy Storage Networks
Junjie Qin, Yin-Lam Chow, Jiyan Yang, and Ram Rajagopal
International Conference on Future Energy Systems (ACM e-Energy), 2014. [paper] [arXiv]

Random Laplace Feature Maps for Semigroup Kernels on Histograms
Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, and Michael W. Mahoney
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [paper]

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Jiyan Yang*, Vikas Sindhwani*, Haim Avron*, and Michael W. Mahoney
* indicates equal contribution.
International Conference on Machine Learning (ICML), 2014. [paper] [extended version] [arXiv (long version)] [codes] [slides]

Quantile Regression for Large-scale Applications
Jiyan Yang, Xiangrui Meng, and Michael W. Mahoney
International Conference on Machine Learning (ICML), 2013. [paper] [arXiv (long version)] [codes] [slides]

Other Publications

A Multi-platform Evaluation of the Randomized CX Low-rank Matrix Factorization in Spark
Alex Gittens et al.
International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning
and Big Data Analytics (ParLearning), at IPDPS, 2016. [paper]

Tensor Machines for Learning Target-specific Polynomial Features
Jiyan Yang and Alex Gittens
arXiv preprint, 2015. [arXiv] [codes]

Dissertation

Randomized Linear Algebra For Large-scale Data Applications [thesis]

Talks

Sub-sampled Newton Methods with Non-uniform Sampling. PCMI, 2016. [slides]

Weighted SGD for Lp Regression with Randomized Preconditioning. SODA, 2016. [slides]

Implementing Randomized Matrix Algorithms in Parallel and Distributed Environmentsi. INFORMS, 2015. [slides]

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML, 2014. [slides]

Quantile Regression for Large-scale Applications. ICML, 2013. [slides]