Work

Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning

Year: 2024

Type: conference-paper

Abstract: Machine learning (ML)-based models are often black box, making it challenging to understand and interpret their decision-making processes. Surrogate models are constructed to approximate the behavior of a target model and are an essential tool for analyzing black-box models. The construction of a surrogate model typically includes querying the target model with carefully selected data points and using the responses from the target model to infer information about its structure and parameters.

Source: IEEE/ACM International Conference on Automated Software Engineering (ASE)

DOI: 10.1145/3691620.3695532

Authors: Sunny Shree, Krishna Khadka, Yu Lei, Raghu N. Kacker, D. Richard Kuhn

Institutions: The University of Texas at Arlington, National Institute of Standards and Technology

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Institute of Standards and Technology

Award: EXPLAINING AND DEBUGGING MACHINE LEARNING MODELS BY NEIGHBORHOOD EXPLORATION