3 citations · 4 across the 5 of their papers we have counts for
5 papers
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize
Chris Junchi Li
In this paper, we revisit \textsf{ROOT-SGD}, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The prop…
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
Chris Junchi Li
Federated learning (FL) for minimax optimization has emerged as a powerful paradigm for training models across distributed nodes/clients while preserving data privacy and model rob…
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization
Chris Junchi Li
We present in this paper novel accelerated fully first-order methods in \emph{Bilevel Optimization} (BLO). Firstly, for BLO under the assumption that the lower-level functions admi…
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
Chris Junchi Li
Stochastic versions of the alternating direction method of multiplier (ADMM) and its variants play a key role in many modern large-scale machine learning problems. In this work, we…
Modeling of the Latent Embedding of Music using Deep Neural Network
Zhou Xing, Eddy Baik, Yan Jiao +8
While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to…