Follow
Andrew Gordon Wilson
Title
Cited by
Cited by
Year
Averaging weights leads to wider optima and better generalization
P Izmailov, D Podoprikhin, T Garipov, D Vetrov, AG Wilson
Uncertainty in Artificial Intelligence (UAI), 2018
18302018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
JR Gardner, G Pleiss, D Bindel, KQ Weinberger, AG Wilson
Advances in Neural Information Processing Systems (NIPS), 2018
14342018
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
11062016
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy
Advances in neural information processing systems 33, 21524-21538, 2020
1092*2020
A simple baseline for Bayesian uncertainty in deep learning
W Maddox, T Garipov, P Izmailov, D Vetrov, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2019
9672019
Gaussian process kernels for pattern discovery and extrapolation
AG Wilson, RP Adams
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
8492013
Loss surfaces, mode connectivity, and fast ensembling of DNNs
T Garipov, P Izmailov, D Podoprikhin, DP Vetrov, AG Wilson
Advances in Neural Information Processing Systems (NIPS), 2018
8062018
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
7922020
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning (ICML), 2019
6892019
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
AG Wilson, H Nickisch
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
6572015
What Are Bayesian Neural Network Posteriors Really Like?
P Izmailov, S Vikram, MD Hoffman, AG Wilson
International Conference on Machine Learning, 2021
4532021
Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
M Finzi, S Stanton, P Izmailov, AG Wilson
International Conference on Machine Learning (ICML), 2020
3522020
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
3442016
Cyclical stochastic gradient MCMC for Bayesian deep learning
R Zhang, C Li, J Zhang, C Chen, AG Wilson
International Conference on Learning Representations (ICLR), 2019
3352019
Large language models are zero-shot time series forecasters
N Gruver, M Finzi, S Qiu, AG Wilson
Advances in Neural Information Processing Systems 36, 19622-19635, 2023
3342023
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
B Athiwaratkun, M Finzi, P Izmailov, AG Wilson
International Conference on Learning Representations (ICLR), 2019
3122019
Last layer re-training is sufficient for robustness to spurious correlations
P Kirichenko, P Izmailov, AG Wilson
arXiv preprint arXiv:2204.02937, 2022
3062022
Why normalizing flows fail to detect out-of-distribution data
P Kirichenko, P Izmailov, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
3052020
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
2922017
Student-t processes as alternatives to Gaussian processes
A Shah, AG Wilson, Z Ghahramani
Artificial Intelligence and Statistics, 877-885, 2014
2922014
The system can't perform the operation now. Try again later.
Articles 1–20