10 citations · 26 across the 5 of their papers we have counts for
5 papers
Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
Longtao Xu, Jian Li
Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this setting as Federated Domain-Increme…
Critical Learning Periods in Federated Learning
Gang Yan, Hao Wang, Jian Li
Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; howe…
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, Rahul Singh +1
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the…
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
Shufan Wang, Jian Li, Shiqiang Wang
We study the problem of augmenting online algorithms with machine learned (ML) advice. In particular, we consider the \emph{multi-shop ski rental} (MSSR) problem, which is a genera…
Learning-Assisted Competitive Algorithms for Peak-Aware Energy Scheduling
Russell Lee, Mohammad H. Hajiesmaili, Jian Li
In this paper, we study the peak-aware energy scheduling problem using the competitive framework with machine learning prediction. With the uncertainty of energy demand as the fund…