3 citations · 6 across the 4 of their papers we have counts for
9 papers
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems
Yinchong Yang, Florian Buettner
Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many com…
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization
Xudong Sun, Florian Buettner
We address the task of domain generalization, where the goal is to train a predictive model such that it is able to generalize to a new, previously unseen domain. We choose a hiera…
Post-hoc Uncertainty Calibration for Domain Drift Scenarios
Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem +2
We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representativ…
Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration
Christian Tomani, Florian Buettner
To facilitate a wide-spread acceptance of AI systems guiding decision making in real-world applications, trustworthiness of deployed models is key. That is, it is crucial for predi…
TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
Yushan Liu, Markus M. Geipel, Christoph Tietz +1
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cas…
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
Florian Buettner, John Piorkowski, Ian McCulloh +1
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems…