arXiv Machine Learning
Self-Directed Apple Tasting
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arXiv Machine Learning
Oct 8
Optimal Regret for Online Market Making with Limit Order Book
arXiv:2610.09691v1 Announce Type: cross Abstract: We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the pr...
arXiv Machine Learning
Oct 8
m-Set Adversarial Bandits with Winner Feedback
The paper presents upper and lower bounds on the regret for m‑set adversarial bandits across various utilities—winner reward or sum of rewards—and feedback models, including winner index, winner reward, sum of rewards, and their combinations. By comparing these results to standard bounds for combinatorial and MNL bandits, the authors demonstrate that small changes in the setting can dramatically affect learning rates. The main technical contribution is the derivation of information‑theoretic lower bounds, and synthetic experiments validate the theoretical findings.
arXiv Machine Learning
Aug 26
An Efficient Minimax-Optimal Algorithm for Adversarial $m$-Set Bandits
arXiv:2608. 12231v2 Announce Type: replace Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.
arXiv Machine Learning
Aug 7
Optimal Rates for Learning with Monotone Adversaries
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
arXiv Machine Learning
Sep 15
Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary
arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...
arXiv Machine Learning
Jul 1
A Complete Characterization of Learnability for Adversarial Noisy Bandits
arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.