Andrew Jesson
Andrew Jesson
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
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
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
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
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
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
Brain tumor segmentation using a 3D FCN with multi-scale loss
A Jesson, T Arbel
International MICCAI Brainlesion Workshop, 392-402, 2017
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
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
Attentive task-agnostic meta-learning for few-shot text classification
X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ...
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
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
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
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
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
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ...
arXiv preprint arXiv:2106.12059, 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
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
M Oprescu, J Dorn, M Ghoummaid, A Jesson, N Kallus, U Shalit
40th International Conference on Machine Learning (ICML), 2023
Partial identification of dose responses with hidden confounders
MG Marmarelis, E Haddad, A Jesson, N Jahanshad, A Galstyan, ...
Uncertainty in Artificial Intelligence, 1368-1379, 2023
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
Differentiable Multi-Target Causal Bayesian Experimental Design
Y Annadani, P Tigas, DR Ivanova, A Jesson, Y Gal, A Foster, S Bauer
40th International Conference on Machine Learning (ICML), 2023
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Artikkelit 1–20