Jasper Snoek

According to our database, Jasper Snoek authored at least 66 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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 Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

Trans. Mach. Learn. Res., 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

J. Mach. Learn. Res., 2024

Bayesian Optimization via Continual Variational Last Layer Training.

Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

CoRR, 2024

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability.

Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith

CoRR, 2024

Variational Bayesian Last Layers.

James Harrison, John Willes, Jasper Snoek

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

J. Mach. Learn. Res., 2023

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

Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek

CoRR, 2023

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

Trans. Mach. Learn. Res., 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

Trans. Mach. Learn. Res., 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2022

Pre-training helps Bayesian optimization too.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 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 E. Dahl

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Second opinion needed: communicating uncertainty in medical machine learning.

Benjamin Kompa, Jasper Snoek, Andrew L. Beam

npj Digit. Medicine, 2021

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures.

Benjamin Kompa, Jasper Snoek, Andrew L. Beam

Entropy, 2021

Sampling the Variational Posterior with Local Refinement.

Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato

Entropy, 2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran

CoRR, 2021

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems.

Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

CoRR, 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

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling.

Setareh Ariafar, Zelda Mariet, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Cold Posteriors and Aleatoric Uncertainty.

Ben Adlam, Jasper Snoek, Samuel L. Smith

CoRR, 2020

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.

Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek

CoRR, 2020

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling.

Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek

CoRR, 2020

Hydra: Preserving Ensemble Diversity for Model Distillation.

Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton

CoRR, 2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification.

Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Spectral Energy Distance for Parallel Speech Synthesis.

Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

How Good is the Bayes Posterior in Deep Neural Networks Really?

Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Proceedings of the 37th International Conference on Machine Learning, 2020

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.

Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.

Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Avoiding a Tragedy of the Commons in the Peer Review Process.

D. Sculley, Jasper Snoek, Alexander B. Wiltschko

CoRR, 2019

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.

Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Likelihood Ratios for Out-of-Distribution Detection.

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DppNet: Approximating Determinantal Point Processes with Deep Networks.

Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the relationship between Normalising Flows and Variational- and Denoising Autoencoders.

Alexey A. Gritsenko, Jasper Snoek, Tim Salimans

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2018

Winner's Curse? On Pace, Progress, and Empirical Rigor.

D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling.

Carlos Riquelme, George Tucker, Jasper Snoek

Proceedings of the 6th International Conference on Learning Representations, 2018

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure.

Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Latent Permutations with Gumbel-Sinkhorn Networks.

Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring.

Ahmad Akl, Jasper Snoek, Alex Mihailidis

IEEE J. Biomed. Health Informatics, 2017

2015

Spectral Representations for Convolutional Neural Networks.

Oren Rippel, Jasper Snoek, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Scalable Bayesian Optimization Using Deep Neural Networks.

Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams

Proceedings of the 32nd International Conference on Machine Learning, 2015

Patient Prognosis from Vital Sign Time Series: Combining Convolutional Neural Networks with a Dynamical Systems Approach.

Li-Wei H. Lehman, Mohammad M. Ghassemi, Jasper Snoek, Shamim Nemati

Proceedings of the Computing in Cardiology, 2015

2014

Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology.

Jasper Snoek

PhD thesis, 2014

Data Mining in Bone Marrow Transplant Records to Identify Patients With High Odds of Survival.

Babak Taati, Jasper Snoek, Dionne M. Aleman, Ardeshir Ghavamzadeh

IEEE J. Biomed. Health Informatics, 2014

Freeze-Thaw Bayesian Optimization.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

CoRR, 2014

Bayesian Optimization with Unknown Constraints.

Michael A. Gelbart, Jasper Snoek, Ryan P. Adams

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Input Warping for Bayesian Optimization of Non-Stationary Functions.

Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams

Proceedings of the 31th International Conference on Machine Learning, 2014

Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults.

Ahmad Akl, Jasper Snoek, Alex Mihailidis

Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

Global Optimization Approaches for Parameter Tuning in Biomedical Signal Processing: A Focus of Multi-scale Entropy.

Mohammad M. Ghassemi, Li-Wei H. Lehman, Jasper Snoek, Shamim Nemati

Proceedings of the Computing in Cardiology, CinC 2014, 2014

2013

Video analysis for identifying human operation difficulties and faucet usability assessment.

Babak Taati, Jasper Snoek, Alex Mihailidis

Neurocomputing, 2013

Multi-Task Bayesian Optimization.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data.

Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Nonparametric guidance of autoencoder representations using label information.

Jasper Snoek, Ryan P. Adams, Hugo Larochelle

J. Mach. Learn. Res., 2012

On Nonparametric Guidance for Learning Autoencoder Representations.

Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Practical Bayesian Optimization of Machine Learning Algorithms.

Jasper Snoek, Hugo Larochelle, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation.

Jasper Snoek, Babak Taati, Alex Mihailidis

Proceedings of the Artificial Intelligence for Gerontechnology, 2012

2011

From Videos to Places: Geolocating the World's Videos.

Jasper Snoek, Luciano Sbaiz, Hrishikesh B. Aradhye

Proceedings of the Data Mining Workshops (ICDMW), 2011

Towards Aging-in-Place: Automatic Assessment of Product Usability for Older Adults with Dementia.

Babak Taati, Jasper Snoek, Alex Mihailidis

Proceedings of the 2011 IEEE International Conference on Healthcare Informatics, 2011

Towards a single sensor passive solution for automated fall detection.

Michael Belshaw, Babak Taati, Jasper Snoek, Alex Mihailidis

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

2010

Automatic segmentation of video to aid the study of faucet usability for older adults.

Jasper Snoek, Babak Taati, Yulia Eskin, Alex Mihailidis

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010

Water Flow Detection in a Handwashing Task.

Babak Taati, Jasper Snoek, David Giesbrecht, Alex Mihailidis

Proceedings of the Canadian Conference on Computer and Robot Vision, 2010

2009

Automated detection of unusual events on stairs.

Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel, Alex Mihailidis

Image Vis. Comput., 2009

2006

Automated Detection of Unusual Events on Stairs.

Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel

Proceedings of the Third Canadian Conference on Computer and Robot Vision (CRV 2006), 2006