68 citations · 154 across the 8 of their papers we have counts for
10 papers
Stochastic Concept Bottleneck Models
Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs +1
Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw…
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz +1
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept v…
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…
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Ričards Marcinkevičs, Patricia Reis Wolfertstetter, Ugne Klimiene +9
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring,…
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine lea…
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiograph…
A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi +9
In this work, we study the problem of clustering survival data a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to clus…
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Ričards Marcinkevičs, Julia E. Vogt
Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in seq…
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Ričards Marcinkevičs, Julia E. Vogt
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learn…
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm
Ricards Marcinkevics, Steven Kelk, Carlo Galuzzi +1
The classification of time series data is a well-studied problem with numerous practical applications, such as medical diagnosis and speech recognition. A popular and effective app…