Hao-Jun Michael Shi

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 13

Top Topics

Interval Estimation

Recommendation Systems

Top Venues

IEEE Signal Process. Lett.

Publications

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 (2023)
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) (2023)
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) (2022)
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) (2022)
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. KDD (2020)
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. CoRR (2019)
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 (2019)
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) (2018)
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang
A Progressive Batching L-BFGS Method for Machine Learning. CoRR (2018)
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)
Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi, Mindy Case, Deanna Needell, Yaniv Plan
Optimizing quantization for Lasso recovery. CoRR (2016)
Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell
Methods for quantized compressed sensing. ITA (2016)
Jerry Luo, Kayla Shapiro, Hao-Jun Michael Shi, Qi Yang, Kan Zhu
Practical Algorithms for Learning Near-Isometric Linear Embeddings. CoRR (2016)
Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell
Methods for Quantized Compressed Sensing. CoRR (2015)