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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
6572018
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
5932016
Gaussian process kernels for pattern discovery and extrapolation
AG Wilson, RP Adams
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
5892013
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
4842018
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
AG Wilson, H Nickisch
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
3992015
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
3812019
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
3252018
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
255*2020
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning (ICML), 2019
2452019
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
2232020
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
2082016
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
197*2019
Student-t processes as alternatives to Gaussian processes
A Shah, AG Wilson, Z Ghahramani
Artificial Intelligence and Statistics, 877-885, 2014
1962014
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
1672017
Gaussian process regression networks
AG Wilson, DA Knowles, Z Ghahramani
Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012
1582012
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
157*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
1512019
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
1442019
A la carte-learning fast kernels
Z Yang, AJ Smola, L Song, AG Wilson
Artificial Intelligence and Statistics (AISTATS), 2015
1362015
Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes
AG Wilson
University of Cambridge, 2014
1302014
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Artiklar 1–20