000076962 001__ 76962 000076962 005__ 20190130113623.0 000076962 0248_ $$2sideral$$a108327 000076962 037__ $$aART-2018-108327 000076962 041__ $$aeng 000076962 100__ $$0(orcid)0000-0002-6741-844X$$aMartinez Cantin, Ruben 000076962 245__ $$aPractical Bayesian optimization in the presence of outliers 000076962 260__ $$c2018 000076962 5060_ $$aAccess copy available to the general public$$fUnrestricted 000076962 5203_ $$aInference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a disadvantage when the memory is populated with outliers, inducing bias in the estimation. In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers. The empirical evidence shows that Bayesian optimization with robust regression often produces suboptimal results. We then propose a new algorithm which combines robust regression (a Gaussian process with Student-t likelihood) with outlier diagnostics to classify data points as outliers or inliers. By using an scheduler for the classification of outliers, our method is more efficient and has better convergence over the standard robust regression. Furthermore, we show that even in controlled situations with no expected outliers, our method is able to produce better results. 000076962 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000076962 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion 000076962 700__ $$aTee, Kevin 000076962 700__ $$aMcCourt, Michael 000076962 773__ $$g84 (AISTATS) (2018), 1722-1731$$pProc. Mach. Learn. Res.$$tProceedings of Machine Learning Research$$x2640-3498 000076962 85641 $$uhttp://proceedings.mlr.press/v84/martinez-cantin18a.html$$zTexto completo de la revista 000076962 8564_ $$s604625$$uhttps://zaguan.unizar.es/record/76962/files/texto_completo.pdf$$yPreprint 000076962 8564_ $$s100703$$uhttps://zaguan.unizar.es/record/76962/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint 000076962 909CO $$ooai:zaguan.unizar.es:76962$$particulos$$pdriver 000076962 951__ $$a2019-01-30-10:09:42 000076962 980__ $$aARTICLE