1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Semi-supervised counterfactual explanations
Shravan Kumar Sajja, Sumanta Mukherjee, Satyam Dwivedi
Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different outpu…
cs.LG2023★ 1 cited
TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
Vikas C. Raykar, Arindam Jati, Sumanta Mukherjee +4
A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While vario…