FEAMOE: Fair, Explainable and Adaptive Mixture of Experts

10 October 2022

Papers citing "FEAMOE: Fair, Explainable and Adaptive Mixture of Experts"

5 / 5 papers shown

Title
Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Peiran Wang Linjie Tong Jiaxiang Liu Zuozhu Liu VLM MoE 39 0 0 10 Feb 2025
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values Shubham Sharma Sanghamitra Dutta Emanuele Albini Freddy Lecue Daniele Magazzeni Manuela Veloso 27 1 0 13 Mar 2024
Fair Wasserstein Coresets Zikai Xiong Niccolò Dalmasso Shubham Sharma Freddy Lecue Daniele Magazzeni Vamsi K. Potluru T. Balch Manuela Veloso 16 2 0 09 Nov 2023
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning Danial Dervovic Nicolas Marchesotti Freddy Lecue Daniele Magazzeni 21 0 0 11 Nov 2022
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,143 0 23 Aug 2019