2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
Akshay Murthy, Shawn Sebastian, Manil Shangle +3
Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff b…
P2C: Path to Counterfactuals
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key…
MC3G: Model Agnostic Causally Constrained Counterfactual Generation
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However,…
Generating Causally Compliant Counterfactual Explanations using ASP
Sopam Dasgupta
This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS ap…