| 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 | 699 | 2022 |
| Measuring calibration in deep learning. J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran CVPR workshops 2 (7), 2019 | 430 | 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 | 221 | 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 | 175* | 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 | 106 | 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 | 98 | 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 | 56* | 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 | 45 | 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 | 35 | 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 | 32 | 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 | 22 | 2019 |
| Sparse MoEs meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... arXiv preprint arXiv:2110.03360, 2021 | 18 | 2021 |
| Dynamic Collaborative Filtering with Compound Poisson Factorization G Jerfel, ME Basbug, BE Engelhardt Artificial Intelligence and Statistics (AISTATS 2017), 2016 | 15 | 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 | 3 | 2020 |
| Boosted Stochastic Backpropagation for Variational Inference G Jerfel Princeton University, 2017 | 2 | 2017 |
| Multimodal Probabilistic Inference for Robust Uncertainty Quantification G Jerfel Duke University, 2021 | 1 | 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 | | 2023 |
| Non-asymptotic Analysis of Langevin Monte Carlo Algorithms: A Review of Three Influential Papers G Jerfel | | 2019 |
| An Information Theoretic Interpretation of Variational Inference based on the MDL Principle and the Bits-Back Coding Scheme G Jerfel | | 2017 |
Dustin TranResearch Scientist, Google在 google.com 的电子邮件经过验证
