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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
6912018
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
6072016
Gaussian process kernels for pattern discovery and extrapolation
AG Wilson, RP Adams
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
6002013
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
5092018
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
AG Wilson, H Nickisch
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
4092015
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
4062019
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
3382018
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
280*2020
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning (ICML), 2019
2542019
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
2432020
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
2122016
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
199*2019
Student-t processes as alternatives to Gaussian processes
A Shah, AG Wilson, Z Ghahramani
Artificial Intelligence and Statistics, 877-885, 2014
1972014
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
1712017
Gaussian process regression networks
AG Wilson, DA Knowles, Z Ghahramani
Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012
1622012
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
161*2014
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
1552019
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
1492019
A la carte-learning fast kernels
Z Yang, AJ Smola, L Song, AG Wilson
Artificial Intelligence and Statistics (AISTATS), 2015
1372015
Bayesian GAN
Y Saatchi, AG Wilson
Advances in Neural Information Processing Systems (NIPS) 30, 2017
1312017
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