4 citations · 6 across the 11 of their papers we have counts for
13 papers
CoBe: A Benchmark For Conversational Counterfactual Text Editing
Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar +6
Counterfactual text editing is the ability to revise a given text in light of a hypothetical change while preserving facts not causally affected by that change. When asked what wou…
From Black-box to Causal-box: Towards Building More Interpretable Models
Inwoo Hwang, Yushu Pan, Elias Bareinboim
Understanding the predictions made by deep learning models remains a central challenge, especially in high-stakes applications. A promising approach is to equip models with the abi…
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…
Towards Spatially Consistent Image Generation: On Incorporating Intrinsic Scene Properties into Diffusion Models
Hyundo Lee, Suhyung Choi, Inwoo Hwang +1
Image generation models trained on large datasets can synthesize high-quality images but often produce spatially inconsistent and distorted images due to limited information about…
SnapMoGen: Human Motion Generation from Expressive Texts
Chuan Guo, Inwoo Hwang, Jian Wang +1
Text-to-motion generation has experienced remarkable progress in recent years. However, current approaches remain limited to synthesizing motion from short or general text prompts,…
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…
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition
Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song +2
Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships…
Learning Geometry-aware Representations by Sketching
Hyundo Lee, Inwoo Hwang, Hyunsung Go +3
Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a v…
SelecMix: Debiased Learning by Contradicting-pair Sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak +4
Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labe…
On the Importance of Critical Period in Multi-stage Reinforcement Learning
Junseok Park, Inwoo Hwang, Min Whoo Lee +4
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plastic…