14 citations · 50 across the 12 of their papers we have counts for
3 papers · 1 filter
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
Rachana Balasubramanian, Samuel Sharpe, Brian Barr +2
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance. High quality expl…
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together
Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato +2
With the increasing sophistication of machine learning models, there are growing trends of developing model explanation techniques that focus on only one instance (local explanatio…
Towards Ground Truth Explainability on Tabular Data
Brian Barr, Ke Xu, Claudio Silva +4
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes fro…