| 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 | 189 | 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 | 122 | 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 | 38 | 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 | 31 | 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 | 23 | 2022 |
| 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 |
| 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 | 17 | 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 | 16 | 2022 |
| 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 |
| 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 | 13 | 2020 |
| 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 |
| General latent feature modeling for data exploration tasks I Valera, MF Pradier, Z Ghahramani arXiv preprint arXiv:1707.08352, 2017 | 6 | 2017 |
| 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 | 5 | 2019 |
Finale Doshi-VelezProfessor, Harvard在 seas.harvard.edu 的电子邮件经过验证
