8 papers
How smoothing the affinity matrix affects neighborhood preservation in t-SNE
Shirin Mohebi, Guillaume Bied, Jefrey Lijffijt
Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborh…
How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
Mathilde Perez, Raphaël Romero, Jefrey Lijffijt +1
Link prediction models are increasingly used to recommend interactions in evolving networks, yet their impact on network structure is typically assessed from static snapshots. In p…
Are LLMs ready to help non-expert users to make charts of official statistics data?
Gadir Suleymanli, Alexander Rogiers, Lucas Lageweg +1
In this time when biased information, deep fakes, and propaganda proliferate, the accessibility of reliable data sources is more important than ever. National statistical institute…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
InfoClus: Informative Clustering of High-dimensional Data Embeddings
Fuyin Lai, Edith Heiter, Guillaume Bied +1
Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often d…
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
Sander Noels, Guillaume Bied, Maarten Buyl +4
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to…