Ghassen Jerfel

According to our database, Ghassen Jerfel authored at least 15 papers between 2017 and 2023.

Collaborative distances:

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

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

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

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

J. Mach. Learn. Res., 2022

2021

Multimodal Probabilistic Inference for Robust Uncertainty Quantification.

Ghassen Jerfel

PhD thesis, 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

Variational refinement for importance sampling using the forward Kullback-Leibler divergence.

Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks.

Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 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

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

Analyzing the role of model uncertainty for electronic health records.

Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

2019

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles.

Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

CoRR, 2019

Reconciling meta-learning and continual learning with online mixtures of tasks.

Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller

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

Measuring Calibration in Deep Learning.

Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018

Online gradient-based mixtures for transfer modulation in meta-learning.

Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller

CoRR, 2018

2017

Dynamic Collaborative Filtering With Compound Poisson Factorization.

Ghassen Jerfel, Mehmet Emin Basbug, Barbara E. Engelhardt

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017