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. 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 invo
The paper addresses a common challenge in stochastic linear bandits, specifically the trade-off between computational efficiency and analytical tractability in exploration algorithms.
Improved bandit algorithms can enhance decision-making under uncertainty in various AI applications, leading to more efficient resource allocation and faster learning in complex systems.
This research introduces a novel modification to Thompson Sampling, potentially making it both computationally attractive and analytically robust for real-world applications.
- · AI/ML researchers
- · Reinforcement learning applications
- · Tech companies developing AI
- · Machine learning platforms
- · Algorithms with high computational demands
More efficient and reliable online learning systems could be developed across various industries.
Faster convergence to optimal strategies in dynamic environments, improving automated decision-making processes.
Reduced operational costs and enhanced performance for businesses heavily reliant on bandit-like optimization problems.
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