Jasper Snoek
I completed my PhD in machine learning at the University of Toronto in 2013. Subsequently, I held postdoctoral fellowships at the University of Toronto, under Geoffrey Hinton and Ruslan Salakhutdinov, and at the Harvard Center for Research on Computation and Society, under Ryan Adams. While at Harvard I co-founded the machine learning startup Whetlab, which was acquired by Twitter in 2015. Currently, I am a research scientist at Google Brain in Cambridge, MA.
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A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Shreyas Padhy
Zi Lin
Yeming Wen
Ghassen Jerfel
Journal of Machine Learning Research (2022)
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
Training independent subnetworks for robust prediction
Marton Havasi
Rodolphe Jenatton
Stanislav Fort
International Conference on Learning Representations (2021)
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit
Ben Adlam
Jaehoon Lee
Jeffrey Pennington
International Conference on Learning Representations, 2021, International Conference on Learning Representations, 2021, 27 pages
Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen
Ghassen Jerfel
Rafael Rios Müller
International Conference on Learning Representations (2021)
A Spectral Energy Distance for Parallel Speech Synthesis
Nal Kalchbrenner
Rianne van den Berg
(2020)
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel
Rodolphe Jenatton
Neural Information Processing Systems (NeurIPS) (2020)
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Ghassen Jerfel
Yeming Wen
Yian Ma
International Conference on Machine Learning (ICML) (2020)
Cold Posteriors and Aleatoric Uncertainty
Ben Adlam
Sam Smith
ICML workshop on Uncertainty and Robustness in Deep Learning (2020)
