32 citations · 59 across the 6 of their papers we have counts for
6 papers
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
Jie Hu, Lingyun Chen, Geeho Kim +3
History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on f…
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…
Open-Set Representation Learning through Combinatorial Embedding
Geeho Kim, Junoh Kang, Bohyung Han
Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in ident…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…