New analysis improves GP bandit optimization regret bounds

Researchers have developed a new method for analyzing the performance of parallel Gaussian Process (GP) bandit optimization. This paper focuses on improving the regret upper bounds for GP-BTS, a widely used algorithm. The new analysis demonstrates that the algorithm can achieve better regret bounds without requiring an initial phase of uncertainty sampling, which is often ineffective in practice. The findings also indicate significantly improved regret bounds in noiseless settings compared to noisy ones. AI

RANK_REASON Academic paper on a novel analysis method for an existing algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Gaussian process
• GP-BTS
• stat.ML
• Thompson sampling

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →