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 @ NeurIPS
We are proud to announce that our paper "CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms" by Martin Pawelczyk, Sascha Bielawski, Johan Van den Heuvel, Tobias Richter and Gjergji Kasneci was accepted at the NeurIPS 2021 Datasets and Benchmarks Track.
Paper @ ISMAR 2021
"TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types" by Wolfgang Fuhl, Gjergji Kasneci and Enkelejda Kasneci was accepted at the IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2021.
Article @ Computers in Human Behavior Reports
"Robust Cognitive Load Detection from Wrist-Band Sensors" by Vadim Borisov, Enkelejda Kasneci and Gjergji Kasneci has been accepted to Computers in Human Behavior Reports journal.
Article @ Nature Scientific Data
"TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information" was published in the latest issue of the Nature Scientific Data Journal. This article is the result of a collaboration between Enkelejda Kasneci, Gjergji Kasneci, Tobias Appel, Johannes Haug, Franz Wortha, Maike Tibus, Ulrich Trautwein & Peter Gerjets.
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.
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/.