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Learning under Model Misspecification: Applications to Variational and Ensemble methods

. (2019)cite arxiv:1912.08335Comment: Camera-Ready Version. NeurIPS 2020. Minor changes.

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An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data., , and . IPMU, volume 6178 of Lecture Notes in Computer Science, page 685-695. Springer, (2010)A Bayesian Random Split to Build Ensembles of Classification Trees., , and . ECSQARU, volume 5590 of Lecture Notes in Computer Science, page 469-480. Springer, (2009)Locally Averaged Bayesian Dirichlet Metrics., , , and . ECSQARU, volume 6717 of Lecture Notes in Computer Science, page 217-228. Springer, (2011)Varying Parameter in Classification Based on Imprecise Probabilities., , , and . SMPS, volume 37 of Advances in Soft Computing, page 231-239. Springer, (2006)Rejoinder on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks"., and . Int. J. Approx. Reason., 55 (7): 1618-1622 (2014)Understanding Generalization in the Interpolation Regime using the Rate Function., and . CoRR, (2023)Analyzing concept drift: A case study in the financial sector., , , , , and . Intell. Data Anal., 24 (3): 665-688 (2020)Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers., , , and . ECSQARU, volume 3571 of Lecture Notes in Computer Science, page 932-943. Springer, (2005)PAC-Bayes-Chernoff bounds for unbounded losses., , , and . CoRR, (2024)InferPy: Probabilistic modeling with Tensorflow made easy., , and . Knowl. Based Syst., (2019)