2 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Source Attribution in Retrieval-Augmented Generation
Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko +4
While attribution methods, such as Shapley values, are widely used to explain the importance of features or training data in traditional machine learning, their application to Larg…
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
The Susceptibility of Example-Based Explainability Methods to Class Outliers
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
This study explores the impact of class outliers on the effectiveness of example-based explainability methods for black-box machine learning models. We reformulate existing explain…
Hospitalization Length of Stay Prediction using Patient Event Sequences
Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad +3
Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a n…