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Hao-Jun Michael Shi
Hao-Jun Michael Shi
Research Scientist, Meta
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Title
Cited by
Cited by
Year
Deep Learning Recommendation Model for Personalization and Recommendation Systems
M Naumov, D Mudigere, HJM Shi, J Huang, N Sundaraman, J Park, ...
arXiv preprint arXiv:1906.00091, 2019
3632019
A progressive batching L-BFGS method for machine learning
R Bollapragada, J Nocedal, D Mudigere, HJ Shi, PTP Tang
International Conference on Machine Learning, 620-629, 2018
1162018
A Primer on Coordinate Descent Algorithms
HJM Shi, S Tu, Y Xu, W Yin
arXiv preprint arXiv:1610.00040, 2016
852016
Compositional embeddings using complementary partitions for memory-efficient recommendation systems
HJM Shi, D Mudigere, M Naumov, J Yang
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
602020
Methods for Quantized Compressed Sensing
HJM Shi, M Case, X Gu, S Tu, D Needell
Information Theory and Applications, 2016
312016
Ground-Motion Prediction Equations for Arias Intensity Consistent with the NGA-West2 Ground-Motion Models
C Abrahamson, HJM Shi, B Yang
Pacific Earthquake Engineering Research Center, 2016
252016
On the numerical performance of finite-difference-based methods for derivative-free optimization
HJM Shi, M Qiming Xuan, F Oztoprak, J Nocedal
Optimization Methods and Software, 1-23, 2022
18*2022
A noise-tolerant quasi-Newton algorithm for unconstrained optimization
HJM Shi, Y Xie, R Byrd, J Nocedal
SIAM Journal on Optimization 32 (1), 29-55, 2022
92022
Adaptive finite-difference interval estimation for noisy derivative-free optimization
HJM Shi, Y Xie, MQ Xuan, J Nocedal
SIAM Journal on Scientific Computing 44 (4), A2302-A2321, 2022
72022
Optimizing Quantization for Lasso Recovery
X Gu, S Tu, HJM Shi, M Case, D Needell, Y Plan
arXiv preprint arXiv:1606.03055, 2016
62016
Practical Algorithms for Learning Near-Isometric Linear Embeddings
J Luo, K Shapiro, HJM Shi, Q Yang, K Zhu
SIAM Undergraduate Research Online 9, 2016
32016
PyTorch-LBFGS: A PyTorch implementation of L-BFGS
HJM Shi, D Mudigere
22017
Methods for Stochastic, Noisy, and Derivative-Free Optimization
HJM Shi
Northwestern University, 2021
2021
Additional Numerical Results for:“On the Numerical Performance of Finite-Difference Based Methods for Derivative-Free Optimization”
HJM Shia, MQ Xuana, F Oztoprakb, J Nocedala
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