Accurate and robust Shapley Values for explaining predictions and focusing on local important variables. - Salim I. Amoukou; Nicolas J.-B. Brunel; Tangi Salaün (CoRR, 2021)

Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models. - Salim I. Amoukou; Nicolas J.-B. Brunel (CoRR, 2021)

Accurate Shapley Values for explaining tree-based models. - Salim I. Amoukou; Tangi Salaün; Nicolas J.-B. Brunel (AISTATS, 2022)

Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor. - Salim I. Amoukou; Nicolas J.-B. Brunel (NeurIPS, 2022)

Rethinking Counterfactual Explanations as Local and Regional Counterfactual Policies. - Salim I. Amoukou; Nicolas J.-B. Brunel (CoRR, 2022)

Adaptive Conformal Prediction by Reweighting Nonconformity Score. - Salim I. Amoukou; Nicolas J.-B. Brunel (CoRR, 2023)

PICE: Polyhedral Complex Informed Counterfactual Explanations. - Mattia Jacopo Villani; Emanuele Albini; Shubham Sharma; Saumitra Mishra; Salim Ibrahim Amoukou; Daniele Magazzeni; Manuela Veloso (AIES (1), 2024)

Counterfactual Metarules for Local and Global Recourse. - Tom Bewley; Salim I. Amoukou; Saumitra Mishra; Daniele Magazzeni; Manuela Veloso (ICML, 2024)

Sequential Harmful Shift Detection Without Labels. - Salim I. Amoukou; Tom Bewley; Saumitra Mishra; Freddy Lécué; Daniele Magazzeni; Manuela Veloso (NeurIPS, 2024)

Representation Consistency for Accurate and Coherent LLM Answer Aggregation. - Junqi Jiang; Tom Bewley; Salim I. Amoukou; Francesco Leofante; Antonio Rago 001; Saumitra Mishra; Francesca Toni (CoRR, 2025)