Snoek, Jasper

35 publications

ICLR 2025 Bayesian Optimization via Continual Variational Last Layer Training Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

TMLR 2024 Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T Parisi, Abhishek Kumar, Alexander A Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura A Culp, Lechao Xiao, Maxwell Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

JMLR 2024 Pre-Trained Gaussian Processes for Bayesian Optimization Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

ICLR 2024 Variational Bayesian Last Layers James Harrison, John Willes, Jasper Snoek

NeurIPSW 2024 Variational Last Layers for Bayesian Optimization Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

JMLR 2023 A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

AISTATS 2022 Predicting the Utility of Search Spaces for Black-Box Optimization: A Simple, Budget-Aware Approach Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George Dahl

TMLR 2022 Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim, Peter Y Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

ICMLW 2022 Plex: Towards Reliability Using Pretrained Large Model Extensions Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

TMLR 2022 Sparse MoEs Meet Efficient Ensembles James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

AISTATS 2021 Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling Setareh Ariafar, Zelda Mariet, Dana Brooks, Jennifer Dy, Jasper Snoek

ICLR 2021 Combining Ensembles and Data Augmentation Can Harm Your Calibration Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

ICLR 2021 Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

ICLR 2021 Training Independent Subnetworks for Robust Prediction Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

NeurIPS 2020 A Spectral Energy Distance for Parallel Speech Synthesis Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

ICML 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

ICML 2020 How Good Is the Bayes Posterior in Deep Neural Networks Really? Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

NeurIPS 2020 Hyperparameter Ensembles for Robustness and Uncertainty Quantification Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

ICML 2020 The K-Tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

NeurIPS 2019 Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, Jasper Snoek

NeurIPS 2019 DppNet: Approximating Determinantal Point Processes with Deep Networks Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek

NeurIPS 2019 Likelihood Ratios for Out-of-Distribution Detection Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan

ICLRW 2019 On the Relationship Between Normalising Flows and Variational- and Denoising Autoencoders Alexey A. Gritsenko, Jasper Snoek, Tim Salimans

ICLR 2018 Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling Carlos Riquelme, George Tucker, Jasper Snoek

ICLR 2018 Learning Latent Permutations with Gumbel-Sinkhorn Networks Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek

ICML 2015 Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams

NeurIPS 2015 Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams

UAI 2014 Bayesian Optimization with Unknown Constraints Michael A. Gelbart, Jasper Snoek, Ryan P. Adams

ICML 2014 Input Warping for Bayesian Optimization of Non-Stationary Functions Jasper Snoek, Kevin Swersky, Rich Zemel, Ryan Adams

NeurIPS 2013 A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams

NeurIPS 2013 Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams

JMLR 2012 Nonparametric Guidance of Autoencoder Representations Using Label Information Jasper Snoek, Ryan P. Adams, Hugo Larochelle

AISTATS 2012 On Nonparametric Guidance for Learning Autoencoder Representations Jasper Snoek, Ryan Adams, Hugo Larochelle

NeurIPS 2012 Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams

CVPRW 2010 Automatic Segmentation of Video to Aid the Study of Faucet Usability for Older Adults Jasper Snoek, Babak Taati, Yulia Eskin, Alex Mihailidis