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03/09/2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Neural kernels have drastically increased performance on diverse and non...
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07/07/2022
Pre-training helps Bayesian optimization too
Bayesian optimization (BO) has become a popular strategy for global opti...
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05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
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12/15/2021
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Black box optimization requires specifying a search space to explore for...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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09/16/2021
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers
The performance of deep neural networks can be highly sensitive to the c...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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04/23/2021
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Bayesian optimization (BO) is a popular paradigm for global optimization...
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10/19/2020
Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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10/14/2020
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Modern deep learning models have achieved great success in predictive ac...
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10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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10/06/2020
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Uncertainty quantification for complex deep learning models is increasin...
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08/03/2020
A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem t...
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07/31/2020
Cold Posteriors and Aleatoric Uncertainty
Recent work has observed that one can outperform exact inference in Baye...
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07/10/2020
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Accurate estimation of predictive uncertainty in modern neural networks ...
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06/24/2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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06/19/2020
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
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05/14/2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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02/23/2020
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Many contemporary machine learning models require extensive tuning of hy...
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02/07/2020
The k-tied 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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02/06/2020
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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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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06/07/2019
Likelihood Ratios for Out-of-Distribution Detection
Discriminative neural networks offer little or no performance guarantees...
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06/06/2019
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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01/07/2019
DPPNet: Approximating Determinantal Point Processes with Deep Networks
Determinantal Point Processes (DPPs) provide an elegant and versatile wa...
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12/18/2018
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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02/26/2018
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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02/23/2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Permutations and matchings are core building blocks in a variety of late...
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06/11/2015
Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
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02/19/2015
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
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03/22/2014
Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in glob...
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02/05/2014
Input Warping for Bayesian Optimization of Non-stationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
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06/13/2012
Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model h...
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02/08/2011
