activity
20122023
most citedInterpretability and Explainability: A Machine Learning Zoo Mini-tour

68 citations · 213 across the 20 of their papers we have counts for

collaborators

25 papers

eess.IV2023

M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms

Ece Ozkan, Thomas M. Sutter, Yurong Hu +2

Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ej…

cs.LG2023★ 4 cited

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility

Kacper Sokol, Julia E. Vogt

Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a wide…

stat.ML2023★ 15 cited

Beyond Normal: On the Evaluation of Mutual Information Estimators

Paweł Czyż, Frederic Grabowski, Julia E. Vogt +2

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.…

cs.LG2023★ 3 cited

Tree Variational Autoencoders

Laura Manduchi, Moritz Vandenhirtz, Alain Ryser +1

We propose Tree Variational Autoencoder (TreeVAE), a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. Tr…

cs.LG2023

Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss

Moritz Vandenhirtz, Laura Manduchi, Ričards Marcinkevičs +1

Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of th…

cs.LG2023

Differentiable Random Partition Models

Thomas M. Sutter, Alain Ryser, Joram Liebeskind +1

Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems. However, assigning elements, such as samples in…