Jakob Drachmann Havtorn — accepted AI-conference papers

2024

An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records

Joakim Edin, Maria Maistro, Lars Maaløe, Lasse Borgholt, Jakob Drachmann Havtorn, Tuukka Ruotsalo

EMNLP 2024main

Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such records, streamlining workflows and reducing manual data entry, thereby saving healthc…

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2022

Model-agnostic out-of-distribution detection using combined statistical tests

Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe +2

AISTATS 2022poster

We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model. The idea is to combine a classical parametric test (R…

Cited by 26 Source ↗PDF ↗code ↗Scholar ↗