Jasper Snoek
27 papers · 2012–2025 · 5 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (54) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(15)
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Keyword Trendsetter Combo
(3)
π§
Keyword Pioneer
π
Century Club Β· Bronze
β‘
Prolific Year Β· Productive
ποΈ
Keyword Collector
(119)
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Trend Setter
β
The Questioner
(2)
π₯
Unstoppable Β· Bronze
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Conference Pioneer
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Renaissance Researcher
(6)
Top co-authors
Research topics
Machine Learning
(23)
Optimization & Theory
(16)
Artificial Intelligence
(14)
Deep Learning
(14)
Bayesian & Probabilistic Β· Artificial Intelligence
(10)
Models
(8)
Architectures
(7)
Core Methods
(7)
Learning Types
(7)
Bayesian & Probabilistic Β· Machine Learning
(4)
Techniques
(4)
Mathematics & Optimization
(3)
Core AI
(3)
Application Areas
(2)
Learning Paradigms
(2)
Keywords
gaussian process
(8)
bayesian optimization
(6)
hyperparameter tuning
(5)
hyperparameter optimization
(5)
bayesian neural network
(4)
uncertainty quantification
(4)
variational inference
(4)
model calibration
(4)
neural network
(4)
uncertainty estimation
(3)
out-of-distribution detection
(3)
multi-task learning
(3)
generative model
(3)
surrogate model
(2)
determinantal point process
(2)
latent variable model
(2)
unsupervised learning
(2)
latent representation
(2)
population coding
(1)
gaussian processes
(1)
Papers
Bayesian Optimization via Continual Variational Last Layer Training
ICLR 2025
Pre-trained Gaussian Processes for Bayesian Optimization
JMLR 2024
Variational Bayesian Last Layers
ICLR 2024
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
JMLR 2023
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
AISTATS 2022
Combining Ensembles and Data Augmentation Can Harm Your Calibration
ICLR 2021
Exploring the Uncertainty Properties of Neural Networksβ Implicit Priors in the Infinite-Width Limit
ICLR 2021
Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling
AISTATS 2021
Training independent subnetworks for robust prediction
ICLR 2021
A Spectral Energy Distance for Parallel Speech Synthesis
NeurIPS 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
ICML 2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
ICML 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
NeurIPS 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
ICML 2020
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
NeurIPS 2019
DppNet: Approximating Determinantal Point Processes with Deep Networks
NeurIPS 2019
Likelihood Ratios for Out-of-Distribution Detection
NeurIPS 2019
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
ICLR 2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks
ICLR 2018
Scalable Bayesian Optimization Using Deep Neural Networks
ICML 2015
Spectral Representations for Convolutional Neural Networks
NeurIPS 2015
Input Warping for Bayesian Optimization of Non-Stationary Functions
ICML 2014
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
NeurIPS 2013
Multi-Task Bayesian Optimization
NeurIPS 2013
Nonparametric Guidance of Autoencoder Representations using Label Information
JMLR 2012
On Nonparametric Guidance for Learning Autoencoder Representations
AISTATS 2012
Practical Bayesian Optimization of Machine Learning Algorithms
NeurIPS 2012