16 citations · 27 across the 3 of their papers we have counts for
4 papers
D2PO: Optimizing Diffusion Samplers via Dynamic Preference
Jinkyu Kim, Jinyoung Choi, Bohyung Han
We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing diffusion sampling policies with respect to timestep schedules and classifier-free g…
Relaxed Contrastive Learning for Federated Learning
Seonguk Seo, Jinkyu Kim, Geeho Kim +1
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient…
Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim, Geeho Kim, Bohyung Han
A critical challenge of federated learning is data heterogeneity and imbalance across clients, which leads to inconsistency between local networks and unstable convergence of globa…
Communication-Efficient Federated Learning with Accelerated Client Gradient
Geeho Kim, Jinkyu Kim, Bohyung Han
Federated learning often suffers from slow and unstable convergence due to the heterogeneous characteristics of participating client datasets. Such a tendency is aggravated when th…