98 citations · 379 across the 33 of their papers we have counts for
4 papers · 1 filter
Conformal prediction under ambiguous ground truth
David Stutz, Abhijit Guha Roy, Tatiana Matejovicova +3
Conformal Prediction (CP) allows to perform rigorous uncertainty quantification by constructing a prediction set satisfying for a user-chose…
Evaluating AI systems under uncertain ground truth: a case study in dermatology
David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy +17
For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However…
Generative models improve fairness of medical classifiers under distribution shifts
Ira Ktena, Olivia Wiles, Isabela Albuquerque +9
A ubiquitous challenge in machine learning is the problem of domain generalisation. This can exacerbate bias against groups or labels that are underrepresented in the datasets used…
Transformers Meet Directed Graphs
Simon Geisler, Yujia Li, Daniel Mankowitz +3
Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected…