← Back to all news
arXiv Machine Learning By Youngmin Oh, Jinje Park, Taejin Paik

Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration

Read the original on arXiv Machine Learning →

arXiv:2506. 01250v3 Announce Type: replace Abstract: We introduce the first variance-aware algorithms for contextual dueling bandits that leverage shallow exploration strategies with neural networks for nonlinear utility approximation.

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 Machine Learning.

Related stories

arXiv Machine Learning
Sep 16

Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits

Meta-LinEXP3 is an online-within-online algorithm designed for adversarial linear contextual bandits with random action sets. It builds a task-level prior from completed tasks to guide an inner LinEXP3 learner, achieving an σO(√n) per‑task regret when context distributions are known and an σO(n^{2/3}) regret with a past‑only regularized moment estimator when they are unknown. The paper also links prior accuracy to transfer regret, showing that better priors yield sublinear, transfer‑dependent regret across tasks, and demonstrates the method on structured hyperspectral tensor sampling.

By Hao Li, Jie Xu, Zheng Xie
reinforcement-learningsafety
More like this →
arXiv Machine Learning
Jun 30

Randomized Exploration for Linear Bandits via Absolute Perturbations

arXiv:2606. 28616v1 Announce Type: new Abstract: In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson Sampling (TS) is computationally attractive yet typically harder to analyze due to its non-optimistic nature.

By Toshinori Kitamura, Shuai Liu, Csaba Szepesv\'ari
More like this →
arXiv Machine Learning
Aug 18

Generalized Linear Bandits with Memory

arXiv:2608. 15848v1 Announce Type: cross Abstract: We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory matrix.

By Heesang Ann, Hyunjun Choi, Taehyun Hwang, Younghoon Shin, Haeju Cheong, Min-hwan Oh
More like this →
arXiv Machine Learning
Jun 29

Self-Concordant Perturbations for Linear Bandits

arXiv:2510. 24187v3 Announce Type: replace-cross Abstract: We consider the adversarial linear bandits setting and present a unified algorithmic framework that bridges Follow-the-Regularized-Leader (FTRL) and Follow-the-Perturbed-Leader (FTPL) methods, extending the known connection between them from the full-information setting.

By Lucas L\'evy, Jean-Lou Valeau, Arya Akhavan, Patrick Rebeschini
safety
More like this →