31 citations · 78 across the 11 of their papers we have counts for
14 papers
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
Nishtha Madaan, Adithya Manjunatha, Hrithik Nambiar +4
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based syste…
Explainable Data Imputation using Constraints
Sandeep Hans, Diptikalyan Saha, Aniya Aggarwal
Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analys…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…
Data Synthesis for Testing Black-Box Machine Learning Models
Diptikalyan Saha, Aniya Aggarwal, Sandeep Hans
The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In…
Automated Testing of AI Models
Swagatam Haldar, Deepak Vijaykeerthy, Diptikalyan Saha
The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we too…