464 citations · 3k across the 68 of their papers we have counts for
9 papers · 1 filter
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
Zhiwei Tang, Dmitry Rybin, Tsung-Hui Chang
In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle-a situation frequently encount…
Federated Stochastic Primal-dual Learning with Differential Privacy
Yiwei Li, Shuai Wang, Tsung-Hui Chang +1
Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the l…
Federated Semi-Supervised Learning with Class Distribution Mismatch
Zhiguo Wang, Xintong Wang, Ruoyu Sun +1
Many existing federated learning (FL) algorithms are designed for supervised learning tasks, assuming that the local data owned by the clients are well labeled. However, in many pr…
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
Lunchen Xie, Jiaqi Liu, Songtao Lu +2
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big d…
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Wenwen Min, Taosheng Xu, Xiang Wan +1
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. Unfortunately, the interpretation of the clustering results from NMF is diff…
Learning Structured Communication for Multi-agent Reinforcement Learning
Junjie Sheng, Xiangfeng Wang, Bo Jin +5
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology fo…