delayed-feedback

This paper presents a diagnostic protocol for selecting rewards and policies in delayed-feedback contextual bandits, arguing that standard offline evaluation can mislead and validating the approach on benchmarks and a deployed push system.

This paper identifies 'staleness amplification' in bilevel optimization under delayed feedback and proposes IGT-OMD, which uses Implicit Gradient Transport to achieve sublinear regret and improve decision loss on benchmarks like Warcraft shortest-path and LQR.