Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
arXiv:2602.01682v2 Announce Type: replace-cross
Abstract: We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent’s hidden objective vector from observed optimal actions over f…