Zachary Nado

According to our database1, Zachary Nado authored at least 22 papers between 2018 and 2024.

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Bibliography

2024
Pre-trained Gaussian Processes for Bayesian Optimization.
J. Mach. Learn. Res., 2024

2023
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness.
J. Mach. Learn. Res., 2023

Benchmarking Neural Network Training Algorithms.
CoRR, 2023

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples.
CoRR, 2023

2022
Underspecification Presents Challenges for Credibility in Modern Machine Learning.
J. Mach. Learn. Res., 2022

Adaptive Gradient Methods at the Edge of Stability.
CoRR, 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.
CoRR, 2022

Pre-training helps Bayesian optimization too.
CoRR, 2022

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
A Loss Curvature Perspective on Training Instability in Deep Learning.
CoRR, 2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.
CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.
CoRR, 2021

A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes.
CoRR, 2021

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.
CoRR, 2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.
CoRR, 2020

2019
On Empirical Comparisons of Optimizers for Deep Learning.
CoRR, 2019

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

AutoGraph: Imperative-style Coding with Graph-based Performance.
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

2018
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure.
Proceedings of the 6th International Conference on Learning Representations, 2018


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