3 citations · 4 across the 3 of their papers we have counts for
5 papers
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
Shubham Sharma, Alan H. Gee, Jette Henderson +1
Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions.…
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Shubham Sharma, Jette Henderson, Joydeep Ghosh
Three key properties that are desired of trustworthy machine learning models deployed in high-stakes environments are fairness, explainability, and an ability to account for variou…
Federating Recommendations Using Differentially Private Prototypes
Mónica Ribero, Jette Henderson, Sinead Williamson +1
Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users'…
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Shubham Sharma, Jette Henderson, Joydeep Ghosh
As artificial intelligence plays an increasingly important role in our society, there are ethical and moral obligations for both businesses and researchers to ensure that their mac…
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
Jette Henderson, Bradley A. Malin, Joyce C. Ho +1
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational pheno…