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Melanie F. Pradier
Melanie F. Pradier
Microsoft Research
Zweryfikowany adres z microsoft.com - Strona główna
Tytuł
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Cytowane przez
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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
1382021
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
872021
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
372018
Predicting treatment dropout after antidepressant initiation
MF Pradier, TH McCoy Jr, M Hughes, RH Perlis, F Doshi-Velez
Translational psychiatry 10 (1), 60, 2020
242020
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
222019
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
162021
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
152020
Emotion recognition from speech signals and perception of music
MF Pradier
Universität Stuttgart Institut für Systemtheorie und Bildschirmtechnik …, 2011
152011
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
132021
General latent feature models for heterogeneous datasets
I Valera, MF Pradier, M Lomeli, Z Ghahramani
arXiv preprint arXiv:1706.03779, 2017
132017
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
112020
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
112018
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
102022
General latent feature modeling for data exploration tasks
I Valera, MF Pradier, Z Ghahramani
arXiv preprint arXiv:1707.08352, 2017
102017
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
102016
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
82018
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
72019
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
62022
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
62019
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
62018
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