69 citations · 259 across the 24 of their papers we have counts for
17 papers · 1 filter
Sampling Decisions: Exact Path-Space Control for Physics-Informed Generative Sampling
Michael Chertkov, Hamidreza Behjoo, Sungsoo Ahn
Scientific generative models must turn tractable local decisions into globally correlated samples that respect physical constraints. We introduce Sampling Decisions, a finite-horiz…
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn +1
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structu…
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Hyeonah Kim, Minsu Kim, Sungsoo Ahn +1
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number…
Graph Generation with -trees
Yunhui Jang, Dongwoo Kim, Sungsoo Ahn
Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis. In this work, we introduce a nove…
Imitating Graph-Based Planning with Goal-Conditioned Policies
Junsu Kim, Younggyo Seo, Sungsoo Ahn +2
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the t…
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Sungbin Shin, Yohan Jo, Sungsoo Ahn +1
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts. Unlike the s…