Sonali Parbhoo

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Finale Doshi-VelezProfessor, HarvardAdresă de e-mail confirmată pe seas.harvard.edu
Volker RothProfessor of Computer Science, University of BaselAdresă de e-mail confirmată pe unibas.ch
Maurizio ZazziProfessor of Microbiology, University of SienaAdresă de e-mail confirmată pe unisi.it
Mike WuStanford UniversityAdresă de e-mail confirmată pe cs.stanford.edu
Mario WieserGenedata AGAdresă de e-mail confirmată pe genedata.com
Michael C. HughesAssistant Professor of Computer Science, Tufts UniversityAdresă de e-mail confirmată pe michaelchughes.com
Aleksander WieczorekUniversity of BaselAdresă de e-mail confirmată pe unibas.ch
Omer GottesmanAmazonAdresă de e-mail confirmată pe amazon.com
Thomas VetterEmeritus Professor in Computer Science, University of BaselAdresă de e-mail confirmată pe unibas.ch
Adam KortylewskiResearch Group Leader, University of Freiburg and Max Planck Institute for InformaticsAdresă de e-mail confirmată pe cs.uni-freiburg.de
Andreas Morel-ForsterPostdoctoral Researcher, Department for Mathematics and Computer Science, University of BaselAdresă de e-mail confirmată pe unibas.ch
David AdametzData ScientistAdresă de e-mail confirmată pe ubs.com
Andrew RossPhD student, Harvard UniversityAdresă de e-mail confirmată pe g.harvard.edu
Matthieu KomorowskiMD, PhD, Clinical Senior Lecturer at Imperial College London ; Visiting Scholar at MITAdresă de e-mail confirmată pe imperial.ac.uk
A. Aldo FaisalProfessor in AI & Neuroscience, Imperial College; Chair in Digital Health, University of BayreuthAdresă de e-mail confirmată pe imperial.ac.uk

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Sonali Parbhoo

Assistant Professor, Imperial College London

Adresă de e-mail confirmată pe imperial.ac.uk - Pagina de pornire

Bayesian Inference Causality Reinforcement Learning Interpretability Healthcare


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Beyond sparsity: Tree regularization of deep models for interpretability M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez 32nd AAAI Conference on Artificial Intelligence, 2017	263	2017
Determinants of HIV-1 reservoir size and long-term dynamics during suppressive ART N Bachmann, C Von Siebenthal, V Vongrad, T Turk, K Neumann, ... Nature communications 10 (1), 3193, 2019	131	2019
Combining kernel and model based learning for HIV therapy selection S Parbhoo, J Bogojeska, M Zazzi, V Roth, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2017, 239, 2017	72	2017
Real-time prediction of COVID-19 related mortality using electronic health records P Schwab, A Mehrjou, S Parbhoo, LA Celi, J Hetzel, M Hofer, B Schölkopf, ... Nature communications 12 (1), 1058, 2021	51	2021
Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions O Gottesman, J Futoma, Y Liu, S Parbhoo, L Celi, E Brunskill, ... International Conference on Machine Learning, 3658-3667, 2020	41	2020
Regional tree regularization for interpretability in black box models M Wu, S Parbhoo, M Hughes, R Kindle, L Celi, M Zazzi, V Roth, ... arXiv preprint arXiv:1908.04494, 2019	36*	2019
Information bottleneck for estimating treatment effects with systematically missing covariates S Parbhoo, M Wieser, A Wieczorek, V Roth Entropy 22 (4), 389, 2020	28*	2020
Optimizing for interpretability in deep neural networks with tree regularization M Wu, S Parbhoo, MC Hughes, V Roth, F Doshi-Velez Journal of Artificial Intelligence Research 72, 1-37, 2021	16	2021
Preferential mixture-of-experts: Interpretable models that rely on human expertise as much as possible MF Pradier, J Zazo, S Parbhoo, RH Perlis, M Zazzi, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2021, 525, 2021	13	2021
Informed mcmc with bayesian neural networks for facial image analysis A Kortylewski, M Wieser, A Morel-Forster, A Wieczorek, S Parbhoo, ... arXiv preprint arXiv:1811.07969, 2018	13	2018
A reinforcement learning design for HIV clinical trials S Parbhoo University of the Witwatersrand, Faculty of Science, School of Computer Science, 2014	13	2014
Ncore: Neural counterfactual representation learning for combinations of treatments S Parbhoo, S Bauer, P Schwab arXiv preprint arXiv:2103.11175, 2021	12	2021
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV S Parbhoo, M Wieser, V Roth, F Doshi-Velez Proceedings of Machine Learning Research 126, 1-19, 2020	11	2020
Addressing leakage in concept bottleneck models M Havasi, S Parbhoo, F Doshi-Velez Advances in Neural Information Processing Systems 35, 23386-23397, 2022	10	2022
Greedy structure learning of hierarchical compositional models A Kortylewski, A Wieczorek, M Wieser, C Blumer, S Parbhoo, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	10	2019
Improving counterfactual reasoning with kernelised dynamic mixing models S Parbhoo, O Gottesman, AS Ross, M Komorowski, A Faisal, I Bon, ... PloS one 13 (11), e0205839, 2018	10	2018
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty S Joshi, S Parbhoo, F Doshi-Velez arXiv preprint arXiv 2109, 2021	7	2021
Bayesian markov blanket estimation D Kaufmann, S Parbhoo, A Wieczorek, S Keller, D Adametz, V Roth Artificial Intelligence and Statistics, 333-341, 2016	7	2016
Host genomics of the HIV-1 reservoir size and its decay rate during suppressive antiretroviral treatment CW Thorball, A Borghesi, N Bachmann, C Von Siebenthal, V Vongrad, ... JAIDS Journal of Acquired Immune Deficiency Syndromes 85 (4), 517-524, 2020	6	2020
Inverse Learning of Symmetries M Wieser, S Parbhoo, A Wieczorek, V Roth Advances in Neural Information Processing Systems 33, 2020	6	2020

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