arXiv Machine Learning June 4, 2026 By Marc Abeille, David Janz, Ciara Pike-Burke

When and why randomised exploration works (in linear bandits)

Read the original on arXiv Machine Learning →

arXiv:2502. 08870v2 Announce Type: replace Abstract: We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation.

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reinforcement-learning

arXiv Machine Learning

Jun 2

Variance-sensitive Thompson sampling for generalised linear bandits, revisited

arXiv:2606. 00431v1 Announce Type: new Abstract: We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits.

By Tom Perneczky, Marc Abeille, David Janz

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

arXiv Machine Learning

Jun 16

Sharp analysis of linear ensemble sampling

arXiv:2602. 08026v2 Announce Type: replace Abstract: We analyse linear ensemble sampling (ES) with standard Gaussian perturbations in stochastic linear bandits.

By David Janz, Arya Akhavan, Csaba Szepesv\'ari

benchmarks

arXiv Machine Learning

Sep 15

When Greedy Sampling Explores: KL-Regularized Contextual Bandits without Eluder-Dimension Dependence

arXiv:2609.13564v1 Announce Type: new Abstract: We study KL-regularized contextual bandits under both reward and preference feedback. We show that greedy sampling can achieve logarithmic regret witho...

By Zichen Wang, Haoyang Hong, Huazheng Wang

arXiv Machine Learning

Jun 5

Exploration via linearly perturbed loss minimisation

arXiv:2311. 07565v3 Announce Type: replace Abstract: We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of a linearly perturbed regularised negative log-likelihood function.

By David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesv\'ari

reinforcement-learning

arXiv Machine Learning

Sep 3

Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling

arXiv:2609. 01999v1 Announce Type: cross Abstract: We study a variant of the Thompson Sampling (TS) algorithm, called $\alpha$-TS, for solving stochastic generalized linear bandit problems.

By Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, Bani K. Mallick

reinforcement-learning

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