5 papers
Towards Causal Representation Learning with Observable Sources as Auxiliaries
Kwonho Kim, Heejeong Nam, Inwoo Hwang +1
Causal representation learning seeks to recover latent factors that generate observational data through a mixing function. Needing assumptions on latent structures or relationships…
Locality-aware Concept Bottleneck Model
Sujin Jeon, Hyundo Lee, Eungseo Kim +3
Concept bottleneck models (CBMs) are inherently interpretable models that make predictions based on human-understandable visual cues, referred to as concepts. As obtaining dense co…
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization
Dong Kyu Cho, Inwoo Hwang, Sanghack Lee
Data augmentation is a popular tool for single source domain generalization, which expands the source domain by generating simulated ones, improving generalization on unseen target…
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi +2
Causal dynamics learning has recently emerged as a promising approach to enhancing robustness in reinforcement learning (RL). Typically, the goal is to build a dynamics model that…
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim +2
Monte Carlo Tree Search (MCTS) has showcased its efficacy across a broad spectrum of decision-making problems. However, its performance often degrades under vast combinatorial acti…