arXiv:2610. 32339v1 Announce Type: cross Abstract: We determine the optimal regret in agnostic multiclass online learning with arbitrary, possibly infinite, label spaces.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.
arXiv:2609.21445v1 Announce Type: new Abstract: We prove that the optimal expected mistake bound of online learning a function class $\mathcal{H}$ by a randomized proper learning algorithm is $O(\mat...
arXiv:2609.38375v1 Announce Type: new Abstract: Can a constant number of linear minimizations per round improve on the $T^{3/4}$ regret rate of online Frank-Wolfe on general convex sets? Weibel et al...
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
arXiv:2610. 11074v1 Announce Type: new Abstract: We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial setting.
arXiv:2609. 29694v2 Announce Type: replace Abstract: How much does high confidence cost when a multiclass learner sees only whether its prediction is correct?
The paper introduces the concept of no‑regret language generation, requiring a generator to make a sublinear number of mistakes across all demonstration sequences and compatible target languages. It presents both a realizable and an agnostic variant, using tree‑based mistake‑forcing dimensions to characterize finite‑horizon minimax complexity and establish necessary and sufficient conditions for no‑regret generation. The authors also connect generation complexity to online prediction, providing optimal mistake bounds for language classes with finite Littlestone dimension.