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-CruzChief Data Scientist Swiss Data Science Center and Professor T. at ETHZ--CSZweryfikowany adres z sdsc.ethz.ch
Michael C. HughesAssistant Professor of Computer Science, Tufts UniversityZweryfikowany adres z michaelchughes.com
Krzysztof GajosProfessor of Computer Science, Harvard UniversityZweryfikowany adres z eecs.harvard.edu
Maia JacobsNorthwestern UniversityZweryfikowany adres z northwestern.edu
Jiayu YaoSchool of Engineering and Applied Sciences, Harvard UniversityZweryfikowany adres z g.harvard.edu
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsZweryfikowany adres z cs.uni-saarland.de
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleZweryfikowany adres z eng.cam.ac.uk
Weiwei PanMachine Learning Researcher, Harvard UniversityZweryfikowany adres z g.harvard.edu
Viktor StojkoskiCenter for Collective Learning, ANITI and Faculty of Economics - Skopje, UKIMZweryfikowany adres z eccf.ukim.edu.mk
Zoran UtkovskiFraunhofer Heinrich Hertz Institute; University Goce Delcev Stip; Macedonian Academy of Sciences andZweryfikowany adres z ugd.edu.mk
Ljupco KocarevMacedonian Academy of Sciences and ArtsZweryfikowany adres z cs.manu.edu.mk
Soumya GhoshMIT-IBM Watson AI Lab, IBM ResearchZweryfikowany adres z cs.brown.edu
Maria LomeliResearch engineer, Meta AI FAIR labsZweryfikowany adres z fb.com
Francisco J. R. RuizResearch Scientist at DeepmindZweryfikowany adres z google.com
Gunnar RätschProfessor, ETH ZürichZweryfikowany adres z inf.ethz.ch
Stephanie L HylandMicrosoft Research CambridgeZweryfikowany adres z microsoft.com
Julia E VogtETH ZurichZweryfikowany adres z inf.ethz.ch

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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	77	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	50	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	33	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	18	2020
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	18	2019
Emotion recognition from speech signals and perception of music MF Pradier Universität Stuttgart Institut für Systemtheorie und Bildschirmtechnik …, 2011	13	2011
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	11	2021
General latent feature models for heterogeneous datasets I Valera, MF Pradier, M Lomeli, Z Ghahramani arXiv preprint arXiv:1706.03779, 2017	11	2017
General latent feature models for heterogeneous datasets I Valera, MF Pradier, M Lomeli, Z Ghahramani The Journal of Machine Learning Research 21 (1), 4027-4075, 2020	10	2020
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	10	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	10	2016
General latent feature modeling for data exploration tasks I Valera, MF Pradier, Z Ghahramani arXiv preprint arXiv:1707.08352, 2017	8	2017
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	6	2020
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	6	2018
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	5	2021
Sparse three-parameter restricted indian buffet process for understanding international trade MF Pradier, V Stojkoski, Z Utkovski, L Kocorev, F Perez-Cruz 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018	5	2018
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	4	2022
Towards expressive priors for Bayesian neural networks: Poisson process radial basis function networks B Coker, MF Pradier, F Doshi-Velez arXiv preprint arXiv:1912.05779, 2019	4	2019
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	4	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	4	2019

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