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Mattia Jacopo Villani

2 accepted papers

2025

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

NeurIPS 2025poster

Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is difficult, generally requiring an exponential (in the feature dimension) number of model evaluations. To address this, many mo…

Cited by 0SourceScholar
2025

Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks

AISTATS 2025poster

Kolmogorov-Arnold Networks are a new family of neural network architectures which holds promise for overcoming the curse of dimensionality and has interpretability benefits (Liu et al., 2024). In this paper, we explore the connection between Kolmogorov Arnold Networks (KANs) with piecewise linear (u…

Cited by 0SourceScholar