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Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible

Jan 13, 2021
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez

* 10 pages, 5 figures, 4 tables, AMIA 2021 Virtual Informatics Summit 

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Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks

Dec 12, 2019
Beau Coker, Melanie F. Pradier, Finale Doshi-Velez

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Output-Constrained Bayesian Neural Networks

May 15, 2019
Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez

* Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning and Workshop on Understanding and Improving Generalization in Deep Learning. Long Beach, CA, 2019 

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Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies

May 03, 2019
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier, Kjong Lehmann, Andreas Wicki, Fernando Perez Cruz, Julia E. Vogt, Gunnar Rätsch

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Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

Dec 03, 2018
Melanie F. Pradier, Weiwei Pan, Jiayu Yao, Soumya Ghosh, Finale Doshi-velez

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Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade

Jun 29, 2018
Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski, Ljupco Kocarev, Fernando Perez-Cruz

* To appear in the proceedings of ICASSP 2018 

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General Latent Feature Models for Heterogeneous Datasets

Mar 08, 2018
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

* Software library available at 

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General Latent Feature Modeling for Data Exploration Tasks

Jul 26, 2017
Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani

* presented at 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), Sydney, NSW, Australia 

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