Zachary Nado
Zachary Nado
Google Brain
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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
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 2020
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
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
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
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
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
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
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
Tensorforest: scalable random forests on tensorflow
T Colthurst, D Sculley, G Hendry, Z Nado
Machine learning systems workshop at NIPS, 2016
Stochastic gradient Langevin dynamics that exploit neural network structure
Z Nado, J Snoek, R Grosse, D Duvenaud, B Xu, J Martens
Benchmarking bayesian deep learning on diabetic retinopathy detection tasks
N Band, TGJ Rudner, Q Feng, A Filos, Z Nado, MW Dusenberry, G Jerfel, ...
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
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
A Loss Curvature Perspective on Training Instability in Deep Learning
J Gilmer, B Ghorbani, A Garg, S Kudugunta, B Neyshabur, D Cardoze, ...
arXiv preprint arXiv:2110.04369, 2021
Deep recurrent and convolutional neural networks for automated behavior classification
Z Nado
Undergraduate Thesis, 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:2109.08215, 2022
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
S Ariafar, J Gilmer, Z Nado, J Snoek, R Jenatton, G Dahl
International Conference on Artificial Intelligence and Statistics, 11056-11071, 2022
Method and system for automated behavior classification of test subjects
T Serre, Y Barhomi, Z Nado, K Bath, S Eberhardt
US Patent 10,181,082, 2019
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Download PDF
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
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