| 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 32, 2019 | 842 | 2019 |
| Subspace Inference for Bayesian Deep Learning P Izmailov, WJ Maddox, P Kirichenko, T Garipov, D Vetrov, AG Wilson Uncertainty in Artificial Intelligence 35, 2019 | 171 | 2019 |
| The uncommercial traveler and Reprinted pieces C Dickens, W Maddox, F Walker (No Title), 1958 | 74 | 1958 |
| Accelerating bayesian optimization for biological sequence design with denoising autoencoders S Stanton, W Maddox, N Gruver, P Maffettone, E Delaney, P Greenside, ... International Conference on Machine Learning, 20459-20478, 2022 | 69 | 2022 |
| Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling GW Benton, WJ Maddox, S Lotfi, AG Wilson Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 63 | 2021 |
| Heteroplasmic shifts in tumor mitochondrial genomes reveal tissue-specific signals of relaxed and positive selection S Grandhi, C Bosworth, W Maddox, C Sensiba, S Akhavanfard, Y Ni, ... Human molecular genetics 26 (15), 2912-2922, 2017 | 60 | 2017 |
| Rethinking parameter counting in deep models: Effective dimensionality revisited WJ Maddox, G Benton, AG Wilson arXiv preprint arXiv:2003.02139, 2020 | 59 | 2020 |
| Bayesian optimization with high-dimensional outputs WJ Maddox, M Balandat, AG Wilson, E Bakshy Advances in neural information processing systems 34, 19274-19287, 2021 | 47 | 2021 |
| Function-space distributions over kernels GW Benton, WJ Maddox, JP Salkey, J Albinati, AG Wilson Advances in Neural Information Processing Systems 32, 2019 | 41 | 2019 |
| Fast adaptation with linearized neural networks W Maddox, S Tang, P Moreno, AG Wilson, A Damianou International Conference on Artificial Intelligence and Statistics, 2737-2745, 2021 | 40 | 2021 |
| On uncertainty, tempering, and data augmentation in bayesian classification S Kapoor, WJ Maddox, P Izmailov, AG Wilson Advances in Neural Information Processing Systems 35, 18211-18225, 2022 | 30 | 2022 |
| Kernel interpolation for scalable online Gaussian processes S Stanton, W Maddox, I Delbridge, AG Wilson International Conference on Artificial Intelligence and Statistics, 3133-3141, 2021 | 28 | 2021 |
| Conditioning sparse variational Gaussian processes for online decision-making WJ Maddox, S Stanton, AG Wilson Advances in Neural Information Processing Systems 34, 6365-6379, 2021 | 26 | 2021 |
| Invertible convolutional networks M Finzi, P Izmailov, W Maddox, P Kirichenko, AG Wilson Workshop on Invertible Neural Nets and Normalizing Flows, International …, 2019 | 20 | 2019 |
| Bayesian optimization with conformal prediction sets S Stanton, W Maddox, AG Wilson International Conference on Artificial Intelligence and Statistics, 959-986, 2023 | 16 | 2023 |
| Similarity of neural networks with gradients S Tang, WJ Maddox, C Dickens, T Diethe, A Damianou arXiv preprint arXiv:2003.11498, 2020 | 16 | 2020 |
| Low-precision arithmetic for fast Gaussian processes WJ Maddox, A Potapcynski, AG Wilson Uncertainty in Artificial Intelligence, 1306-1316, 2022 | 10 | 2022 |
| When are Iterative Gaussian Processes Reliably Accurate? WJ Maddox, S Kapoor, AG Wilson arXiv preprint arXiv:2112.15246, 2021 | 10 | 2021 |
| Optimizing high-dimensional physics simulations via composite bayesian optimization W Maddox, Q Feng, M Balandat arXiv preprint arXiv:2111.14911, 2021 | 10 | 2021 |
| Fast uncertainty estimates and bayesian model averaging of dnns W Maddox, T Garipov, P Izmailov, D Vetrov, AG Wilson Uncertainty in Deep Learning Workshop at UAI 8, 2018 | 8 | 2018 |
