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State-of-the-art approaches to estimate neural uncertainties are often hybrid, combining parametric models with explicit or implicit (dropout-based) ensembling. We take another pathway and propose a novel approach to uncertainty quantification for regression tasks, <jats:italic>Wasserstein dropout<\/jats:italic>, that is purely non-parametric. Technically, it captures <jats:italic>aleatoric<\/jats:italic> uncertainty by means of dropout-based sub-network distributions. This is accomplished by a new objective which minimizes the Wasserstein distance between the label distribution and the model distribution. An extensive empirical analysis shows that Wasserstein dropout outperforms state-of-the-art methods, on vanilla test data as well as under distributional shift in terms of producing more accurate and stable uncertainty estimates.<\/jats:p>","DOI":"10.1007\/s10994-022-06230-8","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T19:36:45Z","timestamp":1662665805000},"page":"3161-3204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Wasserstein dropout"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1741-2338","authenticated-orcid":false,"given":"Joachim","family":"Sicking","sequence":"first","affiliation":[]},{"given":"Maram","family":"Akila","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Pintz","sequence":"additional","affiliation":[]},{"given":"Tim","family":"Wirtz","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Wrobel","sequence":"additional","affiliation":[]},{"given":"Asja","family":"Fischer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"6230_CR1","first-page":"14927","volume-title":"Advances in neural information processing systems","author":"A Amini","year":"2020","unstructured":"Amini, A., Schwarting, W., Soleimany, A., et al. 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