Ghassen Jerfel
Ghassen Jerfel
Research @Waymo. Ex-Google Brain, UC Berkeley, Duke, Princeton.
Verified email at - Homepage
Cited by
Cited by
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
Journal of Machine Learning Research 23 (226), 1-61, 2022
Measuring Calibration in Deep Learning.
J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran
CVPR workshops 2 (7), 2019
Efficient and scalable bayesian neural nets with rank-1 factors
M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ...
International conference on machine learning, 2782-2792, 2020
Reconciling meta-learning and continual learning with online mixtures of tasks
G Jerfel, E Grant, T Griffiths, KA Heller
Advances in neural information processing systems 32, 2019
Analyzing the role of model uncertainty for electronic health records
MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, ...
Proceedings of the ACM Conference on Health, Inference, and Learning, 204-213, 2020
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
Combining ensembles and data augmentation can harm your calibration
Y Wen, G Jerfel, R Muller, MW Dusenberry, J Snoek, ...
arXiv preprint arXiv:2010.09875, 2020
Benchmarking bayesian deep learning on diabetic retinopathy detection tasks
N Band, TGJ Rudner, Q Feng, A Filos, Z Nado, MW Dusenberry, G Jerfel, ...
arXiv preprint arXiv:2211.12717, 2022
A simple approach to improve single-model deep uncertainty via distance-awareness
JZ Liu, S Padhy, J Ren, Z Lin, Y Wen, G Jerfel, Z Nado, J Snoek, D Tran, ...
Journal of Machine Learning Research 24 (42), 1-63, 2023
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
G Jerfel, SL Wang, C Fannjiang, KA Heller, Y Ma, M Jordan
Uncertainty in Artificial Intelligence, 2020
Adanet: A scalable and flexible framework for automatically learning ensembles
C Weill, J Gonzalvo, V Kuznetsov, S Yang, S Yak, H Mazzawi, E Hotaj, ...
arXiv preprint arXiv:1905.00080, 2019
Sparse MoEs meet efficient ensembles
JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ...
arXiv preprint arXiv:2110.03360, 2021
Dynamic Collaborative Filtering with Compound Poisson Factorization
G Jerfel, ME Basbug, BE Engelhardt
Artificial Intelligence and Statistics (AISTATS 2017), 2016
Deep uncertainty and the search for proteins
Z Mariet, G Jerfel, Z Wang, C Angermüller, D Belanger, S Vora, M Bileschi, ...
Workshop: Machine Learning for Molecules 196, 201, 2020
Boosted Stochastic Backpropagation for Variational Inference
G Jerfel
Princeton University, 2017
Multimodal Probabilistic Inference for Robust Uncertainty Quantification
G Jerfel
Duke University, 2021
Ensembling mixture-of-experts neural networks
R Jenatton, CR Ruiz, D Tran, JU Allingham, F Wenzel, ZE Mariet, ...
US Patent App. 17/960,780, 2023
Non-asymptotic Analysis of Langevin Monte Carlo Algorithms: A Review of Three Influential Papers
G Jerfel
An Information Theoretic Interpretation of Variational Inference based on the MDL Principle and the Bits-Back Coding Scheme
G Jerfel
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