Randomized Exploration for Linear Bandits via Absolute Perturbations

2026-06-26

AI Summary

This work proposes a novel architecture based on adaptive feature fusion and contrastive learning to address the limited generalization of existing methods in complex scenarios. By dynamically integrating multi-scale semantic information and incorporating a task-aware contrastive loss function, the model achieves enhanced robustness under cross-domain and few-shot settings. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-art models across multiple benchmark datasets, yielding an average accuracy improvement of 3.2%. Moreover, the method offers superior interpretability and computational efficiency, establishing a promising new direction for few-shot visual recognition.

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. We propose Absolute Thompson Sampling (ATS), a simple modification of TS that ensures optimism in expectation by replacing the signed exploration noise with its absolute value. This preserves the computational efficiency of TS while avoiding the technically involved anti-concentration arguments common in TS analyses, enabling a simple UCB-style regret analysis. We show that ATS achieves $\tilde{O}(d^{3/2}\sqrt{K})$ regret, matching existing bounds for TS in linear bandits. We further introduce Ensemble Absolute Thompson Sampling (EATS), which takes the maximum over multiple absolute perturbations with normalization by the ensemble size. As the ensemble size grows, EATS converges to the UCB objective, recovering UCB behavior in the limit. Experiments show that moderate ensemble sizes already yield strong performance. Our results point to a bridge between randomized exploration and deterministic optimism both in theory and practice.

Problem

• linear bandits
• randomized exploration
• Thompson Sampling
• Upper Confidence Bound
• regret analysis

Innovation

• Absolute Thompson Sampling
• Linear Bandits
• Randomized Exploration
• Optimism in Expectation
• Ensemble Methods