Summary of Counterfactual Explanations For Clustering Models, by Aurora Spagnol et al.
Counterfactual Explanations for Clustering Modelsby Aurora Spagnol, Kacper Sokol, Pietro Barbiero, Marc Langheinrich, Martin GjoreskiFirst…
Counterfactual Explanations for Clustering Modelsby Aurora Spagnol, Kacper Sokol, Pietro Barbiero, Marc Langheinrich, Martin GjoreskiFirst…
Deep generative models as an adversarial attack strategy for tabular machine learningby Salijona Dyrmishi, Mihaela…
(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makersby Manh Khoi Duong, Stefan ConradFirst submitted…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performanceby Jacobus G. M. van…
Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learningby Luca…
A Margin-Maximizing Fine-Grained Ensemble Methodby Jinghui Yuan, Hao Chen, Renwei Luo, Feiping NieFirst submitted to…
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillationby Bochao Liu, Jianghu Lu, Pengju Wang, Junjie…
How to predict on-road air pollution based on street view images and machine learning: a…
Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate…
Detecting LGBTQ+ Instances of Cyberbullyingby Muhammad Arslan, Manuel Sandoval Madrigal, Mohammed Abuhamad, Deborah L. Hall,…