b'Jasper Snoek'

research

∙ 03/09/2023

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples

Neural kernels have drastically increased performance on diverse and non...

0 Ben Adlam, et al. ∙

research

∙ 07/07/2022

Pre-training helps Bayesian optimization too

Bayesian optimization (BO) has become a popular strategy for global opti...

17 Zi Wang, et al. ∙

research

∙ 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...

54 Jeremiah Zhe Liu, et al. ∙

research

∙ 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...

0 Setareh Ariafar, et al. ∙

research

∙ 10/07/2021

Sparse MoEs meet Efficient Ensembles

Machine learning models based on the aggregated outputs of submodels, ei...

3 James Urquhart Allingham, et al. ∙

research

∙ 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...

9 Zi Wang, et al. ∙

research

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

research

∙ 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...

0 Samuel Kim, et al. ∙

research

∙ 10/19/2020

Combining Ensembles and Data Augmentation can Harm your Calibration

Ensemble methods which average over multiple neural network predictions ...

0 Yeming Wen, et al. ∙

research

∙ 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...

7 Ben Adlam, et al. ∙

research

∙ 10/13/2020

Training independent subnetworks for robust prediction

Recent approaches to efficiently ensemble neural networks have shown tha...

0 Marton Havasi, et al. ∙

research

∙ 10/06/2020

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Uncertainty quantification for complex deep learning models is increasin...

0 Benjamin Kompa, et al. ∙

research

∙ 08/03/2020

A Spectral Energy Distance for Parallel Speech Synthesis

Speech synthesis is an important practical generative modeling problem t...

3 Alexey A. Gritsenko, et al. ∙

research

∙ 07/31/2020

Cold Posteriors and Aleatoric Uncertainty

Recent work has observed that one can outperform exact inference in Baye...

0 Ben Adlam, et al. ∙

research

∙ 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 ...

20 Shreyas Padhy, et al. ∙

research

∙ 06/24/2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Ensembles over neural network weights trained from different random init...

13 Florian Wenzel, et al. ∙

research

∙ 06/19/2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Covariate shift has been shown to sharply degrade both predictive accura...

10 Zachary Nado, et al. ∙

research

∙ 05/14/2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Bayesian neural networks (BNNs) demonstrate promising success in improvi...

11 Michael W. Dusenberry, et al. ∙

research

∙ 02/23/2020

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling

Many contemporary machine learning models require extensive tuning of hy...

0 Setareh Ariafar, et al. ∙

research

∙ 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...

6 Jakub Świątkowski, et al. ∙

research

∙ 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...

27 Florian Wenzel, et al. ∙

research

∙ 01/14/2020

Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...

1 Linh Tran, et al. ∙

research

∙ 06/07/2019

Likelihood Ratios for Out-of-Distribution Detection

Discriminative neural networks offer little or no performance guarantees...

6 Jie Ren, et al. ∙

research

∙ 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...

9 Yaniv Ovadia, et al. ∙

research

∙ 01/07/2019

DPPNet: Approximating Determinantal Point Processes with Deep Networks

Determinantal Point Processes (DPPs) provide an elegant and versatile wa...

0 Zelda Mariet, et al. ∙

research

∙ 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...

0 D. Sculley, et al. ∙

research

∙ 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...

0 Carlos Riquelme, et al. ∙

research

∙ 02/23/2018

Learning Latent Permutations with Gumbel-Sinkhorn Networks

Permutations and matchings are core building blocks in a variety of late...

0 Gonzalo Mena, et al. ∙

research

∙ 06/11/2015

Spectral Representations for Convolutional Neural Networks

Discrete Fourier transforms provide a significant speedup in the computa...

0 Oren Rippel, et al. ∙

research

∙ 02/19/2015

Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian optimization is an effective methodology for the global optimiz...

0 Jasper Snoek, et al. ∙

research

∙ 03/22/2014

Bayesian Optimization with Unknown Constraints

Recent work on Bayesian optimization has shown its effectiveness in glob...

0 Michael A. Gelbart, et al. ∙

research

∙ 02/05/2014

Input Warping for Bayesian Optimization of Non-stationary Functions

Bayesian optimization has proven to be a highly effective methodology fo...

0 Jasper Snoek, et al. ∙

research

∙ 06/13/2012

Practical Bayesian Optimization of Machine Learning Algorithms

Machine learning algorithms frequently require careful tuning of model h...

0 Jasper Snoek, et al. ∙

research

∙ 02/08/2011

On Nonparametric Guidance for Learning Autoencoder Representations

Unsupervised discovery of latent representations, in addition to being u...

0 Jasper Snoek, et al. ∙

Jasper Snoek

Featured Co-authors