Jiyan Yang
Jiyan Yang
Verified email at stanford.edu
TitleCited byYear
Quasi-Monte Carlo feature maps for shift-invariant kernels
J Yang, V Sindhwani, H Avron, MW Mahoney
International Conference on Machine Learning (ICML 2014), 2014
99*2014
Sub-sampled Newton methods with non-uniform sampling
P Xu, J Yang, F Roosta-Khorasani, C Ré, MW Mahoney
Advances in Neural Information Processing Systems, 3000-3008, 2016
542016
Matrix factorizations at scale: A comparison of scientific data analytics in Spark and C+ MPI using three case studies
A Gittens, A Devarakonda, E Racah, M Ringenburg, L Gerhardt, ...
2016 IEEE International Conference on Big Data (Big Data), 204-213, 2016
462016
Implementing randomized matrix algorithms in parallel and distributed environments
J Yang, X Meng, MW Mahoney
Proceedings of the IEEE 104 (1), 58-92, 2015
432015
Random laplace feature maps for semigroup kernels on histograms
J Yang, V Sindhwani, Q Fan, H Avron, MW Mahoney
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014
432014
Online modified greedy algorithm for storage control under uncertainty
J Qin, Y Chow, J Yang, R Rajagopal
IEEE Transactions on Power Systems 31 (3), 1729-1743, 2015
332015
Quantile regression for large-scale applications
J Yang, X Meng, M Mahoney
International Conference on Machine Learning, 881-887, 2013
332013
Distributed online modified greedy algorithm for networked storage operation under uncertainty
J Qin, Y Chow, J Yang, R Rajagopal
IEEE Transactions on Smart Grid 7 (2), 1106-1118, 2015
232015
Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions
J Yang, O Rubel, MW Mahoney, BP Bowen
Analytical chemistry 87 (9), 4658-4666, 2015
222015
Weighted SGD for ℓ p regression with randomized preconditioning
J Yang, YL Chow, C Ré, MW Mahoney
The Journal of Machine Learning Research 18 (1), 7811-7853, 2017
172017
Modeling and online control of generalized energy storage networks
J Qin, Y Chow, J Yang, R Rajagopal
Proceedings of the 5th international conference on Future energy systems, 27-38, 2014
12*2014
A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark
A Gittens, J Kottalam, J Yang, MF Ringenburg, J Chhugani, E Racah, ...
2016 IEEE International Parallel and Distributed Processing Symposium …, 2016
62016
Tensor machines for learning target-specific polynomial features
J Yang, A Gittens
arXiv preprint arXiv:1504.01697, 2015
62015
A Study of BFLOAT16 for Deep Learning Training
D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ...
arXiv preprint arXiv:1905.12322, 2019
32019
Feature-distributed sparse regression: a screen-and-clean approach
J Yang, MW Mahoney, M Saunders, Y Sun
Advances in Neural Information Processing Systems, 2712-2720, 2016
32016
Prabhat. 2016. Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+ MPI Using Three Case Studies. CoRR abs/1607.01335 (2016)
A Gittens, A Devarakonda, E Racah, MF Ringenburg, L Gerhardt, ...
3
Quasi-monte carlo feature maps for shift-invariant kernels
H Avron, V Sindhwani, J Yang, M Mahoney
arXiv preprint arXiv:1412.8293, 2014
22014
Training with low-precision embedding tables
J Zhang, J Yang, H Yuen
Systems for Machine Learning Workshop at NeurIPS 2018, 2018
12018
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