Ensuring the quality of modern methods of machine learning with regard to predictive quality, robustness, impartiality and interpretability is often a challenge in practical applications. Our research focuses on:
- Comprehensive generation and selection of features for complex learning procedures (e.g. for use in online scenarios and taking into account different uncertainty factors).
- Robust predictive and explanatory mechanisms, taking into account various fairness definitions.
- Techniques for quantifying and improving the quality of (training) data.
Paper @ AAAI 2021 Workshop on Explainable Agency in AI
"On Baselines for Local Feature Attributions" by Johannes Haug, Stefan Zürn, Peter El-Jiz and Gjergji Kasneci was accepted at the workshop on "Explainable Agency in AI" of the AAAI 2021 conference. This paper is based on a project from our EFML seminar in the summer semester 2020.
1st Prize in the Cognitive Load Monitoring Machine Learning Competition @ UbiComp 2020
We are happy to announce that our team has been awarded 1st Prize in the Cognitive Load Monitoring machine learning challenge at UniComp 2020 conference!
Paper @ ICPR 2020
"Aggregating Dependent Gaussian Experts in Local Approximation" by Hamed Jalali and Gjergji Kasneci has been accepted at ICPR 2020.
Paper @ ICPR 2020
"Learning Parameter Distributions to Detect Concept Drift in Data Streams" by Johannes Haug and Gjergji Kasneci has been accepted at ICPR 2020.
Open PhD Position
If you are interested in explainable Machine Learning & Data Science consider to apply to our group by sending your CV, your most recent transcript, and a writing sample to: firstname.lastname@example.org. We are looking forward to hearing from you. More information follows soon.
Paper @ KDD 2020
"Leveraging Model Inherent Variable Importance for Stable Online Feature Selection" by Johannes Haug, Martin Pawelczyk, Klaus Broelemann and Gjergji Kasneci has been accepted at KDD 2020.
Paper @ UAI 2020
On Learning Invariant Counterfactual Explanations under Predictive Multiplicity by Martin Pawelczyk, Klaus Broelemann and Gjergji Kasneci was accepted at UAI 2020.
Best-Paper Award @ Symposium on Eye Tracking Research and Applications (ETRA), 2020
A MinHash approach for fast scanpath classification by David Geisler, Nora Castner, Gjergji Kasneci and Enkelejda Kasneci was accepcted at ETRA (2020)
Paper @ Archives of Data Science, Series A
PLAY: A Profiled Linear Weighting Scheme for Understanding the Influence of Input Variables on the Output of a Deep Artificial Neural Network by Torsten Dietl, Gjergji Kasneci, Johannes Fürnkranz and Eneldo Loza Mencía was accepted at Archives of Data Science, Series A.
Paper @ WWW 2020
"Learning model agnostic actionable counterfactual explanations for tabular data" by Martin Pawelczyk, Klaus Broelemann und Gjergji Kasneci was accepted at WWW 2020. The paper presents a new method to generate counterfactual explanations.
Paper @WIRE's Data Mining and Knowledge Discovery Journal accepcted
"Bias in Data-driven AI Systems - An Introductory Survey" was accepted at WIRE's Data Mining and Knowledge Discovery Journal. This is our first multidisciplinary joint publication in the context of the EU project "NoBias": https://nobias-project.eu/index.php/partners/.