| Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, J Dillon, ... Advances in neural information processing systems 32, 2019 | 1339 | 2019 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... The Journal of Machine Learning Research 23 (1), 10237-10297, 2022 | 535 | 2022 |
| On empirical comparisons of optimizers for deep learning D Choi, CJ Shallue, Z Nado, J Lee, CJ Maddison, GE Dahl arXiv preprint arXiv:1910.05446, 2019 | 275 | 2019 |
| Evaluating prediction-time batch normalization for robustness under covariate shift Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek arXiv preprint arXiv:2006.10963, 2020 | 122 | 2020 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 115 | 2023 |
| Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model G Zhang, L Li, Z Nado, J Martens, S Sachdeva, G Dahl, C Shallue, ... Advances in neural information processing systems 32, 2019 | 104 | 2019 |
| 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 | 67 | 2021 |
| Plex: Towards reliability using pretrained large model extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... arXiv preprint arXiv:2207.07411, 2022 | 49 | 2022 |
| AG: Imperative-style Coding with Graph-based Performance D Moldovan, J Decker, F Wang, A Johnson, B Lee, Z Nado, D Sculley, ... Proceedings of Machine Learning and Systems 1, 389-405, 2019 | 39 | 2019 |
| A loss curvature perspective on training instabilities of deep learning models J Gilmer, B Ghorbani, A Garg, S Kudugunta, B Neyshabur, D Cardoze, ... International Conference on Learning Representations, 2021 | 34* | 2021 |
| Revisiting one-vs-all classifiers for predictive uncertainty and out-of-distribution detection in neural networks S Padhy, Z Nado, J Ren, J Liu, J Snoek, B Lakshminarayanan arXiv preprint arXiv:2007.05134, 2020 | 33 | 2020 |
| A large batch optimizer reality check: Traditional, generic optimizers suffice across batch sizes Z Nado, JM Gilmer, CJ Shallue, R Anil, GE Dahl arXiv preprint arXiv:2102.06356, 2021 | 32 | 2021 |
| 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 | 20 | 2022 |
| Adaptive gradient methods at the edge of stability JM Cohen, B Ghorbani, S Krishnan, N Agarwal, S Medapati, M Badura, ... arXiv preprint arXiv:2207.14484, 2022 | 19 | 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, ... J. Mach. Learn. Res. 24, 42:1-42:63, 2023 | 18 | 2023 |
| Stochastic gradient langevin dynamics that exploit neural network structure Z Nado, J Snoek, R Grosse, D Duvenaud, B Xu, J Martens | 13 | 2018 |
| Pre-trained Gaussian processes for Bayesian optimization Z Wang, GE Dahl, K Swersky, C Lee, Z Mariet, Z Nado, J Gilmer, J Snoek, ... arXiv preprint arXiv:2109.08215, 2021 | 10 | 2021 |
| Tensorforest: scalable random forests on tensorflow T Colthurst, D Sculley, G Hendry, Z Nado Machine learning systems workshop at NIPS, 2016 | 7 | 2016 |
| Pre-training helps Bayesian optimization too Z Wang, GE Dahl, K Swersky, C Lee, Z Mariet, Z Nado, J Gilmer, J Snoek, ... arXiv preprint arXiv:2207.03084, 2022 | 4 | 2022 |
| Kernel Regression with Infinite-Width Neural Networks on Millions of Examples B Adlam, J Lee, S Padhy, Z Nado, J Snoek arXiv preprint arXiv:2303.05420, 2023 | 3 | 2023 |
