1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomp…
Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr +3
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows unce…
Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer +2
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…
Probabilistic Self-supervised Learning via Scoring Rules Minimization
Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer +4
In this paper, we propose a novel probabilistic self-supervised learning via Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic models to enhance repre…