| Improving differential evolution through Bayesian hyperparameter optimization S Biswas, D Saha, S De, AD Cobb, S Das, BA Jalaian 2021 IEEE congress on evolutionary computation (CEC), 832-840, 2021 | 99 | 2021 |
| Bayesopt adversarial attack B Ru, A Cobb, A Blaas, Y Gal International conference on learning representations, 2019 | 90 | 2019 |
| Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting AD Cobb, B Jalaian Uncertainty in Artificial Intelligence (UAI), 2021, 2020 | 87 | 2020 |
| An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval AD Cobb, MD Himes, F Soboczenski, S Zorzan, MD O’Beirne, AG Baydin, ... The astronomical journal 158 (1), 33, 2019 | 74 | 2019 |
| Loss-calibrated approximate inference in Bayesian neural networks AD Cobb, SJ Roberts, Y Gal Theory of deep learning workshop, ICML, 2018, 2018 | 53 | 2018 |
| Introducing an explicit symplectic integration scheme for Riemannian manifold Hamiltonian Monte Carlo AD Cobb, AG Baydin, A Markham, SJ Roberts arXiv preprint arXiv:1910.06243, 2019 | 36 | 2019 |
| Accurate machine-learning atmospheric retrieval via a neural-network surrogate model for radiative transfer MD Himes, J Harrington, AD Cobb, AG Baydin, F Soboczenski, ... The Planetary Science Journal 3 (4), 91, 2022 | 30 | 2022 |
| HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset I Kiskin, AD Cobb, L Wang, S Roberts Machine Learning for the Developing World Workshop, NeurIPS, 2019, 2020 | 25 | 2020 |
| HumBugDB: A Large-scale Acoustic Mosquito Dataset I Kiskin, M Sinka, AD Cobb, W Rafique, L Wang, D Zilli, B Gutteridge, ... NeurIPS 2021, 2021 | 24 | 2021 |
| Principal component flows E Cunningham, AD Cobb, S Jha International Conference on Machine Learning, 4492-4519, 2022 | 23* | 2022 |
| URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks MP Vadera, AD Cobb, B Jalaian, BM Marlin Workshop on Uncertainty & Robustness in Deep Learning, ICML 2020, 2020 | 18 | 2020 |
| Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity W Fruehwirt, AD Cobb, M Mairhofer, L Weydemann, H Garn, R Schmidt, ... Machine Learning for Health (ML4H) Workshop, NeurIPS, 2018, 2018 | 16 | 2018 |
| Bayesian deep learning for exoplanet atmospheric retrieval F Soboczenski, MD Himes, MD O'Beirne, S Zorzan, AG Baydin, AD Cobb, ... Third workshop on Bayesian Deep Learning, NeurIPS, 2018, 2018 | 16* | 2018 |
| Robust decision-making in the internet of battlefield things using bayesian neural networks AD Cobb, BA Jalaian, ND Bastian, S Russell 2021 Winter Simulation Conference (WSC), 1-12, 2021 | 12 | 2021 |
| On uncertainty and robustness in large-scale intelligent data fusion systems BM Marlin, T Abdelzaher, G Ciocarlie, AD Cobb, M Dennison, B Jalaian, ... 2020 IEEE Second International Conference on Cognitive Machine Intelligence …, 2020 | 12 | 2020 |
| Runtime monitoring of deep neural networks using top-down context models inspired by predictive processing and dual process theory A Roy, A Cobb, N Bastian, B Jalaian, S Jha AAAI Spring Symposium 2022, 2022 | 11 | 2022 |
| Automatic acoustic mosquito tagging with bayesian neural networks I Kiskin, AD Cobb, M Sinka, K Willis, SJ Roberts Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 10 | 2021 |
| Better call surrogates: A hybrid evolutionary algorithm for hyperparameter optimization S Biswas, AD Cobb, A Sistrunk, N Ramakrishnan, B Jalaian arXiv preprint arXiv:2012.06453, 2020 | 9 | 2020 |
| The practicalities of scaling Bayesian neural networks to real-world applications AD Cobb University of Oxford, 2020 | 8 | 2020 |
| URSABench: A system for comprehensive benchmarking of Bayesian deep neural network models and inference methods M Vadera, J Li, A Cobb, B Jalaian, T Abdelzaher, B Marlin Proceedings of Machine Learning and Systems 4, 217-237, 2022 | 7 | 2022 |
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk