4 papers · 1 filter
Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning
Michael Lanier, Luise Ge, Sastry Kompella +1
Many decentralized distillation methods are designed around training-time coordination, yet deploy each node in isolation even when more capable neighbors remain available at infer…
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
Ben Rachmut, Luise Ge, William Yeoh +2
Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution. While recent approaches att…
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
Luise Ge, Michael Lanier, Anindya Sarkar +3
Many dynamic decision problems, such as robotic control, involve a series of tasks, many of which are unknown at training time. Typical approaches for these problems, such as multi…
Learning Linear Utility Functions From Pairwise Comparison Queries
Luise Ge, Brendan Juba, Yevgeniy Vorobeychik
We study learnability of linear utility functions from pairwise comparison queries. In particular, we consider two learning objectives. The first objective is to predict out-of-sam…