We study the multi-armed dueling bandit problem in which feedback is provided in the form of relative comparisons between pairs of actions, with the goal of ...
Instance-dependent Regret Bounds for Dueling Bandits · Akshay Balsubramani, Zohar S. Karnin, +1 author. M. Zoghi · Published in Annual Conference… 6 June 2016 ...
We study the multi-armed dueling bandit problem in which feedback is provided in the form of relative comparisons between pairs of actions, with the goal of ...
Duration: 5:57
Posted: Jul 19, 2016
Posted: Jul 19, 2016
Apr 16, 2024 · Instance-dependent regret bounds for dueling bandits. In Conference on Learning Theory. PMLR. Bengs, V., Saha, A. and Hüllermeier, E. (2022) ...
Fingerprint. Dive into the research topics of 'Instance-dependent Regret Bounds for Dueling Bandits'. Together they form a unique fingerprint.
Feb 22, 2022 · We first perform pairwise comparisons amongst bandits in the seed set, and pick a candidate bandit. This candidate bandit is used to eliminate ...
Our use of upper confidence bounds in designing algorithms for the dueling bandits problem is prefigured by their use in the multi-armed bandit algorithms that ...
The theory of adversarial bandits guarantees that if we make use of an adversarial bandit algorithm A, then Sparring-. A will incur regret of the form O(. √. T) ...
TL;DR: We study the problem of dueling bandit and prove a variance-aware regret bound. Abstract: Dueling bandits is a prominent framework for decision-making ...