Research Interests

My research interests are as follows.

2021 papers

Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu
Conference on Machine Learning and Systems (MLSys), 2021 [arxiv]

2020 papers

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
International Conference on Knowledge Discovery and Data Mining (KDD), 2020 [arxiv]

Towards Automated Neural Architecture Discovery for Click-Through Rate Prediction
Qingquan Song, Dehua Cheng, Eric Zhou, Jiyan Yang, Yuandong Tian, Xia Hu
International Conference on Knowledge Discovery and Data Mining (KDD), 2020 [arxiv]

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Maxim Naumov et al.
arXiv preprint, 2020 [arxiv]

ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Qinqing Zheng, Bor-Yiing Su, Jiyan Yang, Alisson Azzolini, Qiang Wu, Ou Jin, Shri Karandikar, Hagay Lupesko, Liang Xiong, Eric Zhou
arXiv preprint, 2020 [arxiv]

2019 Papers

Post-Training 4-bit Quantization on Embedding Tables
Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, Hector Yuen
Workshop on Systems for ML and Open Source Software at NeurIPS, 2019 [arxiv]

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, James Zou
arXiv preprint, 2019 [arxiv]

A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar et al.
arXiv preprint, 2019 [arxiv]

2018 Papers

Training with Low-precision Embedding Tables
Jian Zhang, Jiyan Yang, Hector Yuen
Workshop on Systems for ML and Open Source Software at NeurIPS, 2018 [paper]

Weighted SGD for Lp Regression with Randomized Preconditioning
Jiyan Yang, Yin-Lam Chow, Christopher Ré, and Michael W. Mahoney
J. Machine Learning Research, 18(211), 1-43, 2018. [paper] [arXiv] [slides]

2016 Papers

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]

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]

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]

2015 Papers

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]

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

2014 Papers

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
SIAM J. Scientific Computing, 36(5), S78-S110, 2014. [paper] [arXiv] [codes] [slides]

2013 Papers

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]

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]