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Andrew Gordon Wilson
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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
8792018
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
6982016
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
6662018
Gaussian process kernels for pattern discovery and extrapolation
AG Wilson, RP Adams
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
6552013
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
5082019
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
AG Wilson, H Nickisch
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
4632015
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
4222018
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
388*2020
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
3352020
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning (ICML), 2019
3192019
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
2422016
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
230*2019
Student-t processes as alternatives to Gaussian processes
A Shah, AG Wilson, Z Ghahramani
Artificial Intelligence and Statistics, 877-885, 2014
2172014
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
1902017
Gaussian process regression networks
AG Wilson, DA Knowles, Z Ghahramani
Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012
1842012
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
1812019
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
1792019
Fast kernel learning for multidimensional pattern extrapolation
AG Wilson, E Gilboa, JP Cunningham, A Nehorai
Advances in Neural Information Processing Systems (NIPS), 3626-3634, 2014
173*2014
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
1602020
What Are Bayesian Neural Network Posteriors Really Like?
P Izmailov, S Vikram, MD Hoffman, AG Wilson
International Conference on Machine Learning, 2021
1492021
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