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cs.LG2026
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…
cs.LG2026
Detoxifying LLMs via Representation Erasure-Based Preference Optimization
Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle +3
Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based on applications of DPO, NPO, and simila…
cs.LG2026
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…