3 papers
cs.AI2026
Multi-Adapter Representation Interventions via Energy Calibration
Manjiang Yu, Hongji Li, Junwei Chen +4
Representation intervention has emerged as a promising paradigm for aligning large language models toward desired behaviors without modifying model weights. Existing methods typica…
cs.LG2025
Representation Learning on Out of Distribution in Tabular Data
Achmad Ginanjar, Xue Li, Priyanka Singh +1
The open-world assumption in model development suggests that a model might lack sufficient information to adequately handle data that is entirely distinct or out of distribution (O…
cs.LG2025
Continual Contrastive Learning on Tabular Data with Out of Distribution
Achmad Ginanjar, Xue Li, Priyanka Singh +1
Out-of-distribution (OOD) prediction remains a significant challenge in machine learning, particularly for tabular data where traditional methods often fail to generalize beyond th…