12 citations · 37 across the 7 of their papers we have counts for
9 papers
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal
Jiaeli Shi, Najah Ghalyan, Kostis Gourgoulias +2
Machine learning models trained on sensitive or private data can inadvertently memorize and leak that information. Machine unlearning seeks to retroactively remove such details fro…
Estimating class separability of text embeddings with persistent homology
Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne +3
This paper introduces an unsupervised method to estimate the class separability of text datasets from a topological point of view. Using persistent homology, we demonstrate how tra…
Learning medical triage from clinicians using Deep Q-Learning
Albert Buchard, Baptiste Bouvier, Giulia Prando +10
Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately…
Masking schemes for universal marginalisers
Divya Gautam, Maria Lomeli, Kostis Gourgoulias +2
We consider the effect of structure-agnostic and structure-dependent masking schemes when training a universal marginaliser (arXiv:1711.00695) in order to learn conditional distrib…
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Yura Perov, Logan Graham, Kostis Gourgoulias +4
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…