Jasper Snoek

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

Achievements

+12 more ↓

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (11) 🌍 Conference Polyglot (5)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏃 Academic Marathon (13) 🌟 Keyword Trendsetter Combo (3) 🔬 Deep Specialist (11) 🔥 Unstoppable (8) 🚀 Conference Pioneer ⚡ Prolific Year (5) 📈 Trend Setter 🗃️ Keyword Collector (92) 💎 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