1 citations · 1 across the 4 of their papers we have counts for
4 papers
Are Logistic Models Really Interpretable?
Danial Dervovic, Freddy Lécué, Nicolás Marchesotti +1
The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the infor…
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
Sanjay Kariyappa, Freddy Lécué, Saumitra Mishra +3
This paper proposes Progressive Inference - a framework to compute input attributions to explain the predictions of decoder-only sequence classification models. Our work is based o…
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features
Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lécué +1
The SHAP framework provides a principled method to explain the predictions of a model by computing feature importance. Motivated by applications in finance, we introduce the Top-k…
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue +1
Similarity functions measure how comparable pairs of elements are, and play a key role in a wide variety of applications, e.g., notions of Individual Fairness abiding by the semina…