18 citations · 54 across the 14 of their papers we have counts for
5 papers · 1 filter
Model-based Offline Reinforcement Learning with Count-based Conservatism
Byeongchan Kim, Min-hwan Oh
In this paper, we propose a model-based offline reinforcement learning method that integrates count-based conservatism, named . Our method utilizes the count e…
Personalized Federated Learning with Server-Side Information
Jaehun Song, Min-hwan Oh, Hyung-Sin Kim
Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients. However,…
Sequential Anomaly Detection using Inverse Reinforcement Learning
Min-hwan Oh, Garud Iyengar
One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an…
Directed Exploration in PAC Model-Free Reinforcement Learning
Min-hwan Oh, Garud Iyengar
We study an exploration method for model-free RL that generalizes the counter-based exploration bonus methods and takes into account long term exploratory value of actions rather t…
Can GAN Learn Topological Features of a Graph?
Weiyi Liu, Pin-Yu Chen, Hal Cooper +3
This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generativ…