5 citations · 8 across the 12 of their papers we have counts for
13 papers
Counterfactual Generation with Answer Set Programming
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail approval, hiring, and many more. Unfo…
Counterfactual Explanation Generation with s(CASP)
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail, hiring, and many more. Unfortunately…
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
Huaduo Wang, Farhad Shakerin, Gopal Gupta
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (AS…
An ASP-based Approach to Answering Natural Language Questions for Texts
Dhruva Pendharkar, Kinjal Basu, Farhad Shakerin +1
An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a…
A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories
Huaduo Wang, Farhad Shakerin, Gopal Gupta
We present a clustering- and demotion-based algorithm called Kmeans-FOLD to induce nonmonotonic logic programs from positive and negative examples. Our algorithm improves upon-and…
Knowledge-driven Natural Language Understanding of English Text and its Applications
Kinjal Basu, Sarat Varanasi, Farhad Shakerin +2
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not…