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cs.LG2026
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
Heejin Jung, Gyeongdeok Seo, Hoyoon Byun +2
Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal effects from observational data.…
cs.LG2025
MIDUS: Memory-Infused Depth Up-Scaling
Taero Kim, Hoyoon Byun, Youngjun Choi +2
Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scaling (DUS) does so by duplicating Transfo…
cs.LG2024
Perturb-and-Compare Approach for Detecting Out-of-Distribution Samples in Constrained Access Environments
Heeyoung Lee, Hoyoon Byun, Changdae Oh +2
Accessing machine learning models through remote APIs has been gaining prevalence following the recent trend of scaling up model parameters for increased performance. Even though t…