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Repairing Neural Networks by Leaving the Right Past Behind


Jul 11, 2022
Ryutaro Tanno, Melanie F. Pradier, Aditya Nori, Yingzhen Li

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* 19 pages, 9 figures 

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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

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* 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

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* 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

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* 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

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* Software library available at https://github.com/ivaleraM/GLFM 

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General Latent Feature Modeling for Data Exploration Tasks


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

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

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