Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set EstimationReport as inadecuate




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Presented at: Conference on Neural Information Processing Systems (NIPS), Barcelona, December 5-10, 2016 Publication date: 2016

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which is updated based on confidence bounds. TruVaR is effective in several important settings that are typically non-trivial to incorporate into myopic algorithms, including pointwise costs and heteroscedastic noise. We provide a general theoretical guarantee for TruVaR covering these aspects, and use it to recover and strengthen existing results on BO and LSE. Moreover, we provide a new result for a setting where one can select from a number of noise levels having associated costs. We demonstrate the effectiveness of the algorithm on both synthetic and real-world data sets.

Keywords: Bayesian optimization ; level-set estimation ; Gaussian processes Reference EPFL-CONF-221055





Author: Bogunovic, Ilija; Scarlett, Jonathan; Krause, Andreas; Cevher, Volkan

Source: https://infoscience.epfl.ch/record/221055?ln=en



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