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Andrew Gordon Wilson
Tytuł
Cytowane przez
Cytowane przez
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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
17542018
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
13582018
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
10672016
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
998*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
9272019
Gaussian process kernels for pattern discovery and extrapolation
AG Wilson, RP Adams
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
8302013
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
7822018
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
7532020
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning (ICML), 2019
6522019
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
AG Wilson, H Nickisch
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
6382015
What Are Bayesian Neural Network Posteriors Really Like?
P Izmailov, S Vikram, MD Hoffman, AG Wilson
International Conference on Machine Learning, 2021
4242021
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
3402020
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
3312016
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
3212019
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
306*2019
Why normalizing flows fail to detect out-of-distribution data
P Kirichenko, P Izmailov, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
2862020
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
2842017
Student-t processes as alternatives to Gaussian processes
A Shah, AG Wilson, Z Ghahramani
Artificial Intelligence and Statistics, 877-885, 2014
2842014
Exact Gaussian processes on a million data points
KA Wang, G Pleiss, JR Gardner, S Tyree, KQ Weinberger, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2019
2832019
Last layer re-training is sufficient for robustness to spurious correlations
P Kirichenko, P Izmailov, AG Wilson
arXiv preprint arXiv:2204.02937, 2022
2782022
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