← Authors

Jasper Snoek

27 papers · 2012–2025 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (13) 🌟 Keyword Trendsetter Combo (3) πŸ”¬ Deep Specialist (11) πŸ”₯ Unstoppable (8) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (92) πŸ’Ž Century Club (27) ❓ The Questioner (2)

Conferences

NIPS (9) ICLR (7) ICML (5) AISTATS (3) JMLR (3)

Top co-authors

Balaji Lakshminarayanan (6) Rodolphe Jenatton (5) Dustin Tran (5) Ryan P. Adams (5) Kevin Swersky (4) Zachary Nado (4) Tim Salimans (3) Ryan Adams (3) Joshua Dillon (3) Hugo Larochelle (3)

Keywords

gaussian process (7) bayesian optimization (5) hyperparameter tuning (5) hyperparameter optimization (4) uncertainty quantification (4) generative model (3) multi-task learning (3) latent representation (2) bayesian neural network (2) out-of-distribution detection (2) variational inference (2) unsupervised learning (2) determinantal point process (2) neural network (2) population coding (1) feature learning (1) gaussian processes (1) latent dirichlet allocation (1) stochastic gradient (1) ensemble learning (1)

Papers

Bayesian Optimization via Continual Variational Last Layer Training ICLR 2025
Variational Bayesian Last Layers ICLR 2024
Pre-trained Gaussian Processes for Bayesian Optimization JMLR 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
Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling AISTATS 2021
Training independent subnetworks for robust prediction ICLR 2021
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit ICLR 2021
A Spectral Energy Distance for Parallel Speech Synthesis NIPS 2020
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification NIPS 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 NIPS 2019
Likelihood Ratios for Out-of-Distribution Detection NIPS 2019
DppNet: Approximating Determinantal Point Processes with Deep Networks NIPS 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
Spectral Representations for Convolutional Neural Networks NIPS 2015
Scalable Bayesian Optimization Using Deep Neural Networks ICML 2015
Input Warping for Bayesian Optimization of Non-Stationary Functions ICML 2014
Multi-Task Bayesian Optimization NIPS 2013
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data NIPS 2013
On Nonparametric Guidance for Learning Autoencoder Representations AISTATS 2012
Practical Bayesian Optimization of Machine Learning Algorithms NIPS 2012
Nonparametric Guidance of Autoencoder Representations using Label Information JMLR 2012