ml.cs.aueb.gr

Next sessions

April 2025

12:00 - 13:00 EEST

Exploring the role and significance of latent variables.

March 2025

12:00 - 13:00 EEST

Machine learning when there is uncertainty, exploring fields such as conformal prediction.

February 2025

12:00 - 13:00 EEST

Machine learning with signals that are not unimodal, as for subjective tasks.

May 2025

12:00 - 13:00 EEST

Exploring diffusion processes and their applications in various fields.

June 2025

12:00 - 13:00 EEST

Retrieval augmented generation and classification, exploring clustering, and other fields.

July 2025

12:00 - 13:00 EEST

Explainability methods for ML in fields such as NLP and Vision, including self-explanations and counterfactuals.

Recent contributions from our research group in machine learning highlight significant advancements and insights:

Pavlopoulos et al. (2024): "Revisiting Silhouette Aggregation," awarded Best Paper runner up at the 27th International Conference on Discovery Science in Pisa, Italy. Available as a preprint: arXiv:2401.05831.

Pavlopoulos and Likas (2024): "Polarized Opinion Detection Improves the Detection of Toxic Language", published in the Proceedings of EACL in Malta. Related slides: https://aclanthology.org/2024.eacl-long.117/

These works reflect our group's commitment to advancing the field through empirical research and innovative methodologies.

Deepening understanding of ML principles by enhancing comprehension of fundamental machine learning concepts and their mathematical underpinnings.
Linking theory to applications by exploring how foundational ML theories are applied to solve complex problems in NLP, vision, and speech.
Facilitating scholarly discussion by encouraging critical discussions on recent research papers, fostering a critical analysis of methodologies, results, and implications.
Promoting academic collaboration by supporting networking and collaborative research opportunities among students, educators, and researchers.