Melanie F. Pradier

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Finale Doshi-VelezProfessor, HarvardZweryfikowany adres z seas.harvard.edu
Roy PerlisMassachusetts General HospitalZweryfikowany adres z mgh.harvard.edu
Thomas H McCoyMassachusetts General Hospital / Harvard Medical SchoolZweryfikowany adres z mgh.harvard.edu
Fernando Perez-CruzSr Adviser, Innovation at Bank for International SettlementsZweryfikowany adres z bis.org
Krzysztof GajosProfessor of Computer Science, Harvard UniversityZweryfikowany adres z eecs.harvard.edu
Maia JacobsNorthwestern UniversityZweryfikowany adres z northwestern.edu
Michael C. HughesAssistant Professor of Computer Science, Tufts UniversityZweryfikowany adres z michaelchughes.com
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsZweryfikowany adres z cs.uni-saarland.de
Weiwei PanMachine Learning Researcher, Harvard UniversityZweryfikowany adres z g.harvard.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleZweryfikowany adres z eng.cam.ac.uk
Viktor StojkoskiFaculty of Economics - Skopje, UKIM and Center for Collective Learning, ANITIZweryfikowany adres z eccf.ukim.edu.mk
Zoran UtkovskiFraunhofer Heinrich Hertz Institute; University American College Skopje; Macedonian Academy ofZweryfikowany adres z hhi.fraunhofer.de
Yingzhen LiImperial College LondonZweryfikowany adres z imperial.ac.uk
Soumya GhoshMIT-IBM Watson AI Lab, IBM ResearchZweryfikowany adres z cs.brown.edu
Javier ZazoMicrosoft ResearchZweryfikowany adres z microsoft.com
Maria LomeliMeta, FAIRZweryfikowany adres z meta.com
Andrew RossPhD student, Harvard UniversityZweryfikowany adres z g.harvard.edu
Ljupco KocarevMacedonian Academy of Sciences and ArtsZweryfikowany adres z manu.edu.mk
Francisco J. R. RuizResearch Scientist at DeepmindZweryfikowany adres z google.com
Ryutaro TannoSenior Research Scientist, Google DeepMindZweryfikowany adres z google.com

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Melanie F. Pradier

Microsoft Research

Zweryfikowany adres z microsoft.com - Strona główna

Generative modeling Healthcare Bayesian modeling Interpretable machine learning


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection M Jacobs, MF Pradier, TH McCoy Jr, RH Perlis, F Doshi-Velez, KZ Gajos Translational psychiatry 11 (1), 108, 2021	213	2021
Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens M Jacobs, J He, M F. Pradier, B Lam, AC Ahn, TH McCoy, RH Perlis, ... Proceedings of the 2021 chi conference on human factors in computing systems …, 2021	130	2021
Economic complexity unfolded: Interpretable model for the productive structure of economies Z Utkovski, MF Pradier, V Stojkoski, F Perez-Cruz, L Kocarev PloS one 13 (8), e0200822, 2018	39	2018
Predicting treatment dropout after antidepressant initiation MF Pradier, TH McCoy Jr, M Hughes, RH Perlis, F Doshi-Velez Translational psychiatry 10 (1), 60, 2020	35	2020
Repairing neural networks by leaving the right past behind R Tanno, M F Pradier, A Nori, Y Li Advances in Neural Information Processing Systems 35, 13132-13145, 2022	25	2022
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	23	2021
Output-constrained Bayesian neural networks W Yang, L Lorch, MA Graule, S Srinivasan, A Suresh, J Yao, MF Pradier, ... arXiv preprint arXiv:1905.06287, 2019	23	2019
Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation MF Pradier, MC Hughes, TH McCoy Jr, SA Barroilhet, F Doshi-Velez, ... Neuropsychopharmacology 46 (2), 455-461, 2021	18	2021
Evaluating approximate inference in Bayesian deep learning AG Wilson, P Izmailov, MD Hoffman, Y Gal, Y Li, MF Pradier, S Vikram, ... NeurIPS 2021 Competitions and Demonstrations Track, 113-124, 2022	17	2022
Assessment of a prediction model for antidepressant treatment stability using supervised topic models MC Hughes, MF Pradier, AS Ross, TH McCoy, RH Perlis, F Doshi-Velez JAMA network open 3 (5), e205308-e205308, 2020	16	2020
General latent feature models for heterogeneous datasets I Valera, MF Pradier, M Lomeli, Z Ghahramani Journal of Machine Learning Research 21 (100), 1-49, 2020	15	2020
Emotion recognition from speech signals and perception of music MF Pradier Universität Stuttgart Institut für Systemtheorie und Bildschirmtechnik …, 2011	15	2011
General latent feature models for heterogeneous datasets I Valera, MF Pradier, M Lomeli, Z Ghahramani arXiv preprint arXiv:1706.03779, 2017	12	2017
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez arXiv preprint arXiv:1811.07006, 2018	11	2018
Latent projection bnns: Avoiding weight-space pathologies by learning latent representations of neural network weights MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez Workshop on Bayesian Deep Learning, NIPS, 2018	11	2018
Prior design for dependent Dirichlet processes: An application to marathon modeling M F. Pradier, F JR Ruiz, F Perez-Cruz PloS one 11 (1), e0147402, 2016	9	2016
Case-control Indian buffet process identifies biomarkers of response to Codrituzumab MF Pradier, B Reis, L Jukofsky, F Milletti, T Ohtomo, F Perez-Cruz, O Puig BMC cancer 19, 1-7, 2019	7	2019
Challenges in computing and optimizing upper bounds of marginal likelihood based on chi-square divergences MF Pradier, MC Hughes, F Doshi-Velez Second Symposium on Advances in Approximate Bayesian Inference, 2019	7	2019
AIRIVA: a deep generative model of adaptive immune repertoires MF Pradier, N Prasad, P Chapfuwa, S Ghalebikesabi, M Ilse, ... Machine Learning for Healthcare Conference, 588-611, 2023	6	2023
General latent feature modeling for data exploration tasks I Valera, MF Pradier, Z Ghahramani arXiv preprint arXiv:1707.08352, 2017	6	2017

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