
Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the InfiniteWidth Limit
Modern deep learning models have achieved great success in predictive ac...
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Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Uncertainty quantification for complex deep learning models is increasin...
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A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem t...
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Cold Posteriors and Aleatoric Uncertainty
Recent work has observed that one can outperform exact inference in Baye...
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Revisiting OnevsAll Classifiers for Predictive Uncertainty and OutofDistribution Detection in Neural Networks
Accurate estimation of predictive uncertainty in modern neural networks ...
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Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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Evaluating PredictionTime Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
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Efficient and Scalable Bayesian Neural Nets with Rank1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Many contemporary machine learning models require extensive tuning of hy...
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The ktied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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Likelihood Ratios for OutofDistribution Detection
Discriminative neural networks offer little or no performance guarantees...
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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved gr...
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DPPNet: Approximating Determinantal Point Processes with Deep Networks
Determinantal Point Processes (DPPs) provide an elegant and versatile wa...
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Avoiding a Tragedy of the Commons in the Peer Review Process
Peer review is the foundation of scientific publication, and the task of...
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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Recent advances in deep reinforcement learning have made significant str...
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Learning Latent Permutations with GumbelSinkhorn Networks
Permutations and matchings are core building blocks in a variety of late...
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Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
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Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
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Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in glob...
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Input Warping for Bayesian Optimization of Nonstationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
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Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model h...
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On Nonparametric Guidance for Learning Autoencoder Representations
Unsupervised discovery of latent representations, in addition to being u...
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Jasper Snoek
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