36 citations · 130 across the 21 of their papers we have counts for
23 papers
Harmful Partial Interventions and Benign Leakage in Concept-based Models
Gregory Verghese, Christopher J. Soelistyo, Mateo Espinosa Zarlenga +2
Artificial intelligence systems deployed in high-risk settings increasingly require models that support meaningful human oversight. Concept-based models (CMs) offer a promising fra…
Concept frustration: Aligning human concepts and machine representations
Enrico Parisini, Christopher J. Soelistyo, Ahab Isaac +2
Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a…
Deep Learning as Ricci Flow
Anthony Baptista, Alessandro Barp, Tapabrata Chakraborti +3
Deep neural networks (DNNs) are powerful tools for approximating the distribution of complex data. It is known that data passing through a trained DNN classifier undergoes a series…
Improving embedding of graphs with missing data by soft manifolds
Andrea Marinoni, Pietro Lio', Alessandro Barp +2
Embedding graphs in continous spaces is a key factor in designing and developing algorithms for automatic information extraction to be applied in diverse tasks (e.g., learning, inf…
Warped geometric information on the optimisation of Euclidean functions
Marcelo Hartmann, Bernardo Williams, Hanlin Yu +3
We consider the fundamental task of optimising a real-valued function defined in a potentially high-dimensional Euclidean space, such as the loss function in many machine-learning…
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa, Alessandro Barp, Arthur Gretton +1
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. No…