| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1263 | 2018 |
| Longitudinal multiple sclerosis lesion segmentation: resource and challenge A Carass, S Roy, A Jog, JL Cuzzocreo, E Magrath, A Gherman, J Button, ... NeuroImage 148, 77-102, 2017 | 247 | 2017 |
| Evaluating white matter lesion segmentations with refined Sørensen-Dice analysis A Carass, S Roy, A Gherman, JC Reinhold, A Jesson, T Arbel, O Maier, ... Scientific reports 10 (1), 8242, 2020 | 81 | 2020 |
| On feature collapse and deep kernel learning for single forward pass uncertainty J van Amersfoort, L Smith, A Jesson, O Key, Y Gal arXiv preprint arXiv:2102.11409, 2021 | 77* | 2021 |
| Brain tumor segmentation using a 3D FCN with multi-scale loss A Jesson, T Arbel Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 46 | 2018 |
| Identifying causal-effect inference failure with uncertainty-aware models A Jesson*, S Mindermann*, U Shalit, Y Gal Advances in Neural Information Processing Systems (NeurIPS) 34, 2020 | 45 | 2020 |
| Cased: Curriculum adaptive sampling for extreme data imbalance A Jesson, N Guizard, SH Ghalehjegh, D Goblot, F Soudan, N Chapados International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 39 | 2017 |
| Hierarchical MRF and random forest segmentation of MS lesions and healthy tissues in brain MRI A Jesson, T Arbel Proceedings of the 2015 longitudinal multiple sclerosis lesion segmentation …, 2015 | 37 | 2015 |
| Attentive task-agnostic meta-learning for few-shot text classification X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ... | 30* | 2018 |
| Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding A Jesson, S Mindermann, Y Gal, U Shalit 38th International Conference on Machine Learning (ICML) 139, 4829-4838, 2021 | 28 | 2021 |
| Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data A Jesson*, P Tigas*, J van Amersfoort, A Kirsch, U Shalit, Y Gal Advances in Neural Information Processing Systems (NeurIPS) 35, 2021 | 11 | 2021 |
| Task adaptive metric space for medium-shot medical image classification X Jiang, L Ding, M Havaei, A Jesson, S Matwin International Conference on Medical Image Computing and Computer-Assisted …, 2019 | 11 | 2019 |
| GeneDisco: A Benchmark for Experimental Design in Drug Discovery A Mehrjou, A Soleymani, A Jesson, P Notin, Y Gal, S Bauer, P Schwab International Conference on Learning Representations (ICLR), 2022 | 8 | 2022 |
| Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions A Jesson, A Douglas, P Manshausen, N Meinshausen, P Stier, Y Gal, ... Advances in Neural Information Processing Systems (NeurIPS) 36, 2022 | 4 | 2022 |
| Interventions, where and how? experimental design for causal models at scale P Tigas*, Y Annadani*, A Jesson, B Schölkopf, Y Gal, S Bauer Advances in Neural Information Processing Systems (NeurIPS) 36, 2022 | 4 | 2022 |
| Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific A Jesson*, P Manshausen*, A Douglas*, D Watson-Parris, Y Gal, P Stier Causal Inference & Machine Learning: Why now? (NeurIPS Wokshop), 2021 | 4 | 2021 |
| Stochastic Batch Acquisition for Deep Active Learning A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ... arXiv preprint arXiv:2106.12059, 2021 | 4 | 2021 |
| Adversarially learned mixture model A Jesson, C Low-Kam, T Nair, F Soudan, F Chandelier, N Chapados Theoretical Foundations and Applications of Deep Generative Models (ICML …, 2018 | 1 | 2018 |
| Differentiable Multi-Target Causal Bayesian Experimental Design Y Annadani, P Tigas, DR Ivanova, A Jesson, Y Gal, A Foster, S Bauer arXiv preprint arXiv:2302.10607, 2023 | | 2023 |
| Using uncertainty-aware machine learning models to study aerosol-cloud interactions M Solal, A Jesson, Y Gal, A Douglas | | 2022 |
Yarin GalAssociate Professor, University of OxfordVahvistettu sähköpostiosoite verkkotunnuksessa cs.ox.ac.uk
