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.
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.
Related stories
arXiv Machine Learning
Jun 30
Optimal Regret for Single Index Bandits
arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.
arXiv Machine Learning
Jun 4
When and why randomised exploration works (in linear bandits)
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.
arXiv Machine Learning
Jul 16
On the Sublinear Regret of Continuous K-Max Bandits
arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.
arXiv Machine Learning
Sep 23
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
arXiv:2510.10730v3 Announce Type: replace Abstract: We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for two mo...
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.
arXiv Machine Learning
Jun 2
Finite-Time Regret Analysis of Retry-Aware Bandits
arXiv:2605. 20854v2 Announce Type: replace Abstract: We study a stochastic bandit algorithm motivated by retry-aware objectives that value the best outcome among multiple attempts, such as pass@$k$ and max@$k$.
arXiv AI
5d ago
On the Complexity of Preference-Based Bandits
The paper investigates preference-based bandits where a learner selects pairs of arms and receives binary preference feedback modeled by Bradley–Terry. It introduces the locally sensitive eluder dimension, a new complexity measure for logistic preference feedback, and proposes the GINOP algorithm that uses log-loss confidence sets to balance optimism and exploration. The authors prove a first-order regret bound showing that learning with preference feedback can be as statistically efficient as learning from direct rewards, and they validate their theory with empirical experiments.
arXiv Machine Learning
Sep 23
Sharp Characterization of Bias in Post-Bandit Inference
arXiv:2608.01069v2 Announce Type: replace Abstract: Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable in...
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.
arXiv Statistics ML
4d ago
Block Optimism for Nonstationary Bandits with Latent Linear Dynamics
The paper introduces a new algorithm for a nonstationary bandit setting where actions influence both immediate rewards and the evolution of an unobserved latent linear state. By approximating the infinite‑memory reward process with a finite‑memory block‑level proxy and applying a UCB‑based block algorithm, the authors achieve a regret bound of “~O(√T)”, improving upon the previous “~O(T^{2/3})” guarantee. This represents the first such “~O(√T)” result for latent linear‑dynamics bandits with bilinear rewards and an open‑loop action‑sequence benchmark.
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.