Automated Detection of Unusual Events on Stairs. - Jasper Snoek; Jesse Hoey; Liam Stewart; Richard S. Zemel (CRV, 2006)

Automated detection of unusual events on stairs. - Jasper Snoek; Jesse Hoey; Liam Stewart; Richard S. Zemel; Alex Mihailidis (Image Vis. Comput., 2009)

Water Flow Detection in a Handwashing Task. - Babak Taati; Jasper Snoek; David Giesbrecht; Alex Mihailidis (CRV, 2010)

Automatic segmentation of video to aid the study of faucet usability for older adults. - Jasper Snoek; Babak Taati; Yulia Eskin; Alex Mihailidis (CVPR Workshops, 2010)

Towards a single sensor passive solution for automated fall detection. - Michael Belshaw; Babak Taati; Jasper Snoek; Alex Mihailidis (EMBC, 2011)

Towards Aging-in-Place: Automatic Assessment of Product Usability for Older Adults with Dementia. - Babak Taati; Jasper Snoek; Alex Mihailidis (HISB, 2011)

From Videos to Places: Geolocating the World's Videos. - Jasper Snoek; Luciano Sbaiz; Hrishikesh B. Aradhye (ICDM Workshops, 2011)

An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation. - Jasper Snoek; Babak Taati; Alex Mihailidis (AAAI Fall Symposium: Artificial Intelligence for Gerontechnology, 2012)

Practical Bayesian Optimization of Machine Learning Algorithms. - Jasper Snoek; Hugo Larochelle; Ryan P. Adams (NIPS, 2012)

On Nonparametric Guidance for Learning Autoencoder Representations. - Jasper Snoek; Ryan Prescott Adams; Hugo Larochelle (AISTATS, 2012)

Nonparametric guidance of autoencoder representations using label information. - Jasper Snoek; Ryan P. Adams; Hugo Larochelle (J. Mach. Learn. Res., 2012)

A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. - Jasper Snoek; Richard S. Zemel; Ryan Prescott Adams (NIPS, 2013)

Multi-Task Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (NIPS, 2013)

Video analysis for identifying human operation difficulties and faucet usability assessment. - Babak Taati; Jasper Snoek; Alex Mihailidis (Neurocomputing, 2013)

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 (CinC, 2014)

Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults. - Ahmad Akl; Jasper Snoek; Alex Mihailidis (EMBC, 2014)

Input Warping for Bayesian Optimization of Non-Stationary Functions. - Jasper Snoek; Kevin Swersky; Richard S. Zemel; Ryan P. Adams (ICML, 2014)

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

Freeze-Thaw Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (CoRR, 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)

Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology. - Jasper Snoek (2014)

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 (CinC, 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 (ICML, 2015)

Spectral Representations for Convolutional Neural Networks. - Oren Rippel; Jasper Snoek; Ryan P. Adams (NIPS, 2015)

Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring. - Ahmad Akl; Jasper Snoek; Alex Mihailidis (IEEE J. Biomed. Health Informatics, 2017)

Learning Latent Permutations with Gumbel-Sinkhorn Networks. - Gonzalo E. Mena; David Belanger 002; Scott W. Linderman; Jasper Snoek (ICLR (Poster), 2018)

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. - Zachary Nado; Jasper Snoek; Roger B. Grosse; David Duvenaud; Bowen Xu; James Martens (ICLR (Workshop), 2018)

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. - Carlos Riquelme; George Tucker; Jasper Snoek (ICLR (Poster), 2018)

Winner's Curse? On Pace, Progress, and Empirical Rigor. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko; Ali Rahimi (ICLR (Workshop), 2018)

On the relationship between Normalising Flows and Variational- and Denoising Autoencoders. - Alexey A. Gritsenko; Jasper Snoek; Tim Salimans (DGS@ICLR, 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 006; Peter J. Liu; Emily Fertig; Jasper Snoek; Ryan Poplin; Mark A. DePristo; Joshua V. Dillon; Balaji Lakshminarayanan (NeurIPS, 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 006; Zachary Nado (NeurIPS, 2019)

Avoiding a Tragedy of the Commons in the Peer Review Process. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko (CoRR, 2019)

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 (ICML, 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 (ICML, 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 (ICML, 2020)

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

Hyperparameter Ensembles for Robustness and Uncertainty Quantification. - Florian Wenzel; Jasper Snoek; Dustin Tran; Rodolphe Jenatton (NeurIPS, 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)

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)

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)

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. - Shreyas Padhy; Zachary Nado; Jie Ren 006; Jeremiah Z. Liu; Jasper Snoek; Balaji Lakshminarayanan (CoRR, 2020)

Cold Posteriors and Aleatoric Uncertainty. - Ben Adlam; Jasper Snoek; Samuel L. Smith (CoRR, 2020)

Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. - Setareh Ariafar; Zelda Mariet; Dana H. Brooks; Jennifer G. Dy; Jasper Snoek (AISTATS, 2021)

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. - Ben Adlam; Jaehoon Lee 001; 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 (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)

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)

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; et al. (CoRR, 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)

Sampling the Variational Posterior with Local Refinement. - Marton Havasi; Jasper Snoek; Dustin Tran; Jonathan Gordon 003; José Miguel Hernández-Lobato (Entropy, 2021)

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (Entropy, 2021)

Second opinion needed: communicating uncertainty in medical machine learning. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (npj Digit. Medicine, 2021)

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 (AISTATS, 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)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 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; et al. (Trans. Mach. Learn. Res., 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)

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. - Ben Adlam; Jaehoon Lee 001; Shreyas Padhy; Zachary Nado; Jasper Snoek (CoRR, 2023)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2023)

Variational Bayesian Last Layers. - James Harrison; John Willes; Jasper Snoek (ICLR, 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; et al. (CoRR, 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)

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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

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