2 papers
q-fin.CP2025
Dynamic Inverse Optimization under Drift and Shocks: Theory, Regret Bounds, and Applications
JINHO CHA
The growing prevalence of drift and shocks in modern decision environments exposes a gap between classical optimization theory and real-world practice. Standard models assume fixed…
cs.LG2025
FOSSIL: Regret-minimizing weighting for robust learning under imbalance and small data
J. Cha, J. Lee, J. Cho +1
Imbalanced and small data regimes are pervasive in domains such as rare disease imaging, genomics, and disaster response, where labeled samples are scarce and naive augmentation of…