no code implementations • 6 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.
no code implementations • 23 Oct 2023 • Sopam Dasgupta, Farhad Shakerin, Joaquín Arias, Elmer Salazar, Gopal Gupta
Our approach utilizes answer set programming and the s(CASP) goal-directed ASP system.
2 code implementations • 14 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.
no code implementations • 21 Dec 2021 • Dhruva Pendharkar, Kinjal Basu, Farhad Shakerin, Gopal Gupta
The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system.
no code implementations • 26 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.
no code implementations • 27 Jan 2021 • Kinjal Basu, Sarat Varanasi, Farhad Shakerin, Joaquin Arias, Gopal Gupta
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research.
no code implementations • 22 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.
no code implementations • 9 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.
no code implementations • 18 Sep 2019 • Farhad Shakerin
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.
1 code implementation • 24 May 2019 • Farhad Shakerin, Gopal Gupta
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.
no code implementations • 2 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.
no code implementations • 18 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
1 code implementation • 10 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