| An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in deep neural networks G Ughi, V Abrol, J Tanner Optimization and Engineering, 1-28, 2021 | 5 | 2021 |
| A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQA G Ughi, V Abrol, J Tanner NeurIPS 2019 Workshop Beyond First Order Methods in ML, 2020 | 2 | 2020 |
| Limits on simultaneous transmit and receive D Allwright, GA Antonucci, T Babb, A Evans, E Bowley, R Dakin, C Please, ... | | 2022 |
| Federated causal inference for out-of-distribution generalization in predicting physiological effects of radiation exposure L Sanders, P Duckworth, O O'Donoghue, L Scheibenreif, G Ughi, ... AGU Fall Meeting 2021, 2021 | | 2021 |
| Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery O O'Donoghue, P Duckworth, G Ughi, L Scheibenreif, K Khezeli, ... NeurIPS 2021 Workshop Machine Learning for Health, 2021 | | 2021 |
| Mutual Information of Neural Network Initialisations: Mean Field Approximations J Tanner, G Ughi International Seminar in Information Theory 2021, 2021 | | 2021 |
Jared TannerProfessor of Mathematics of Information, University of OxfordDirección de correo verificada de maths.ox.ac.uk
