4 papers
Capacity-Constrained Online Convex Optimization with Delayed Feedback
Alexander Ryabchenko, Idan Attias, Daniel M. Roy
Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, tracking resources are finite, and…
Reinforcement Learning with Action-Triggered Observations
Alexander Ryabchenko, Wenlong Mou
We introduce Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), a reinforcement learning framework for partial observability in which full state observa…
A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees
Alexander Ryabchenko, Idan Attias, Daniel M. Roy
We develop a reduction-based framework for online learning with delayed feedback that recovers and improves upon existing results for both first-order and bandit convex optimizatio…
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
Alexander Ryabchenko, Idan Attias, Daniel M. Roy
We study online learning with oblivious losses and delays under a novel ``capacity constraint'' that limits how many past rounds can be tracked simultaneously for delayed feedback.…