Contextual Linear Optimization under Full and Partial Feedback

This talk is about Contextual Linear Optimization (CLO) across two feedback regimes, where we study the traditional two-stage Estimate-Then-Optimize (ETO) approach and the new integrated Induced Empirical Risk Minimization (IERM) framework. In the full-feedback setting, we theoretically demonstrate that under model correct specification, ETO can surprisingly achieve faster regret convergence rates than IERM by leveraging problem-specific geometric properties. In partial-feedback settings (bandit and semi-bandit), we propose a unified offline IERM framework and establish novel fast-rate guarantees. Numerical experiments on shortest path problems validate our theoretical findings across different regimes.