Search Results for author: Farhad Shakerin

Found 13 papers, 3 papers with code

Counterfactual Generation with Answer Set Programming

no code implementations6 Feb 2024 Sopam Dasgupta, Farhad Shakerin, Joaquín Arias, Elmer Salazar, Gopal Gupta

In our framework, we show how counterfactual explanations are computed and justified by imagining worlds where some or all factual assumptions are altered/changed.

Attribute counterfactual +2

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

2 code implementations14 Feb 2022 Huaduo Wang, Farhad Shakerin, Gopal Gupta

FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data.

A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories

no code implementations26 Sep 2021 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.

Clustering Inductive logic programming

SQuARE: Semantics-based Question Answering and Reasoning Engine

no code implementations22 Sep 2020 Kinjal Basu, Sarat Chandra Varanasi, Farhad Shakerin, Gopal Gupta

We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework.

Natural Language Understanding Question Answering

White-box Induction From SVM Models: Explainable AI with Logic Programming

no code implementations9 Aug 2020 Farhad Shakerin, Gopal Gupta

In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influential data points in the model, and induces a clause that would cover the support vector and points that are most similar to that support vector.

Inductive logic programming

Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models

no code implementations18 Sep 2019 Farhad Shakerin

We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.

Inductive logic programming

Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

no code implementations2 Aug 2018 Farhad Shakerin, Gopal Gupta

We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers.

General Classification Inductive logic programming

Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems

no code implementations18 Feb 2018 Farhad Shakerin, Gopal Gupta

To the best of our knowledge, this is the first heuristic-based ILP algorithm to induce answer set programs with multiple stable models.

Logic in Computer Science

A New Algorithm to Automate Inductive Learning of Default Theories

1 code implementation10 Jul 2017 Farhad Shakerin, Elmer Salazar, Gopal Gupta

An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories.

Logic in Computer Science

Cannot find the paper you are looking for? You can Submit a new open access paper.