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Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon.. CoRR, (2019)Deep Classifiers with Label Noise Modeling and Distance Awareness., , , , , , , , , and . Trans. Mach. Learn. Res., (2022)Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations., , , , and . IEEE Big Data, page 55-64. IEEE, (2022)Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process., , , and . CoRR, (2018)A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models., , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 547-568. PMLR, (2023)Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees.. CoRR, (2019)On Compositional Uncertainty Quantification for Seq2seq Graph Parsing., , , , and . ICLR, OpenReview.net, (2023)Training independent subnetworks for robust prediction., , , , , , , and . CoRR, (2020)A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness., , , , , , , , , and . J. Mach. Learn. Res., (2023)Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees., , , and . CoRR, (2022)