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ORCIDHao-Jun Michael Shi
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2020 – today
- 2023
[j4]Hao-Jun Michael Shi, Melody Qiming Xuan, Figen Öztoprak, Jorge Nocedal:
On the numerical performance of finite-difference-based methods for derivative-free optimization. Optim. Methods Softw. 38(2): 289-311 (2023)- [i7]Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat:
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale. CoRR abs/2309.06497 (2023) - 2022
- [j3]Hao-Jun Michael Shi, Yuchen Xie, Richard H. Byrd, Jorge Nocedal:
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization. SIAM J. Optim. 32(1): 29-55 (2022) - [j2]Hao-Jun Michael Shi, Yuchen Xie, Melody Qiming Xuan, Jorge Nocedal:
Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization. SIAM J. Sci. Comput. 44(4): 2302- (2022) - 2020
- [c3]Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang:
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. KDD 2020: 165-175
2010 – 2019
- 2019
- [i6]Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy:
Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR abs/1906.00091 (2019) - [i5]Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang:
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. CoRR abs/1909.02107 (2019) - 2018
- [j1]Xiaoyi Gu
, Shenyinying Tu, Hao-Jun Michael Shi, Mindy Case, Deanna Needell, Yaniv Plan:
Optimizing Quantization for Lasso Recovery. IEEE Signal Process. Lett. 25(1): 45-49 (2018) - [c2]Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang:
A Progressive Batching L-BFGS Method for Machine Learning. ICML 2018: 619-628 - [i4]Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang:
A Progressive Batching L-BFGS Method for Machine Learning. CoRR abs/1802.05374 (2018) - 2016
- [c1]Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell:
Methods for quantized compressed sensing. ITA 2016: 1-9 - [i3]Jerry Luo, Kayla Shapiro, Hao-Jun Michael Shi, Qi Yang, Kan Zhu:
Practical Algorithms for Learning Near-Isometric Linear Embeddings. CoRR abs/1601.00062 (2016) - [i2]Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi, Mindy Case, Deanna Needell, Yaniv Plan:
Optimizing quantization for Lasso recovery. CoRR abs/1606.03055 (2016) - 2015
- [i1]Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell:
Methods for Quantized Compressed Sensing. CoRR abs/1512.09184 (2015)
Coauthor Index
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