| 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 | 1857 | 2018 |
| 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 | 1444 | 2018 |
| Deep kernel learning AG Wilson, Z Hu, R Salakhutdinov, EP Xing Artificial Intelligence and Statistics (AISTATS), 2016 | 1119 | 2016 |
| 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 | 1107* | 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 | 969 | 2019 |
| Gaussian process kernels for pattern discovery and extrapolation AG Wilson, RP Adams Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013 | 849 | 2013 |
| 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 | 817 | 2018 |
| Bayesian deep learning and a probabilistic perspective of generalization AG Wilson, P Izmailov Advances in Neural Information Processing Systems (NeurIPS), 2020 | 803 | 2020 |
| Simple black-box adversarial attacks C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger International Conference on Machine Learning (ICML), 2019 | 684 | 2019 |
| Kernel interpolation for scalable structured Gaussian processes (KISS-GP) AG Wilson, H Nickisch Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015 | 665 | 2015 |
| What Are Bayesian Neural Network Posteriors Really Like? P Izmailov, S Vikram, MD Hoffman, AG Wilson International Conference on Machine Learning, 2021 | 465 | 2021 |
| A Cookbook of Self-Supervised Learning R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 2023 | 371 | 2023 |
| 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 | 361 | 2020 |
| 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 | 346 | 2023 |
| Stochastic variational deep kernel learning AG Wilson, Z Hu, RR Salakhutdinov, EP Xing Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016 | 341 | 2016 |
| 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 | 337 | 2019 |
| Last layer re-training is sufficient for robustness to spurious correlations P Kirichenko, P Izmailov, AG Wilson arXiv preprint arXiv:2204.02937, 2022 | 327 | 2022 |
| 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 | 313 | 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 | 310 | 2020 |
| Bayesian optimization with gradients J Wu, M Poloczek, AG Wilson, PI Frazier Advances in Neural Information Processing Systems (NIPS) 30, 2017 | 295 | 2017 |
