5 papers
Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
Alexander Ryabchenko, Jian Qian, Wenlong Mou
We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient directi…
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…
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…
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…
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.…