1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Turing InstituteGB178 papers
- University of OxfordGB114 papers
- University of WarwickGB102 papers
- Queen Mary University of LondonGB91 papers
- University of CambridgeGB89 papers
- University College LondonGB81 papers
- Imperial College LondonGB60 papers
- University of EdinburghGB54 papers
- University of ManchesterGB30 papers
- University of ExeterGB25 papers
- University of SouthamptonGB20 papers
- Centre National de la Recherche ScientifiqueFR19 papers
6 papers · 2 filters
A note on regularised NTK dynamics with an application to PAC-Bayesian training
Eugenio Clerico, Benjamin Guedj
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keep…
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche, Benjamin Guedj
PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation boun…
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum +1
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learnin…
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
Ahmed Salih, Zahra Raisi-Estabragh, Ilaria Boscolo Galazzo +4
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communica…
Characterizing the contribution of dependent features in XAI methods
Ahmed Salih, Ilaria Boscolo Galazzo, Zahra Raisi-Estabragh +3
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpret…
Generalization Bounds with Data-dependent Fractal Dimensions
Benjamin Dupuis, George Deligiannidis, Umut Şimşekli
Providing generalization guarantees for modern neural networks has been a crucial task in statistical learning. Recently, several studies have attempted to analyze the generalizati…