394 citations · 402 across the 2 of their papers we have counts for
6 papers
On the Numerical Performance of Derivative-Free Optimization Methods Based on Finite-Difference Approximations
Hao-Jun Michael Shi, Melody Qiming Xuan, Figen Oztoprak +1
The goal of this paper is to investigate an approach for derivative-free optimization that has not received sufficient attention in the literature and is yet one of the simplest to…
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
Hao-Jun Michael Shi, Yuchen Xie, Richard Byrd +1
This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors. This work is motivated by applications that contain…
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov +1
Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal +2
The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and qua…
Optimizing quantization for Lasso recovery
Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi +3
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantizati…