Our paper "fair pairwise learning to rank" has been accepted at IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Our paper "fair pairwise learning to rank", which was a joint work of Mattia Cerrato, Marius Köppel, Alexander Segner, Roberto Esposito, and Stefan Kramer, was accepted at IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Abstract: Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why
a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.
In this work we present a family of fair pairwise learning to rank approaches based on Neural Networks, which are able to produce balanced outcomes for underprivileged groups and, at the same time, build fair representations of data, i.e. new vectors having no correlation with regard to a sensitive attribute. We
compare our approaches to recent work dealing with fair ranking and evaluate them using both relevance and fairness metrics. Our results show that the introduced fair pairwise ranking methods compare favorably to other methods when considering the fairness/relevance trade-off.

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Talk by Dr. Claudia Schon

Stereotypes in Artificial Intelligence

Today, Artificial Intelligence systems are used in many areas and support among other things decision-making processes (e.g. in checking creditworthiness or assessing the probability of a criminal returning to crime). It is precisely these systems that one wishes to be free of prejudice. Unfortunately, however, this does not always correspond to reality. But to what extent do AI systems show prejudice in general and gender bias in particular? The talk focuses on the area of Commonsense Reasoning, which finds an application in the field of automatic assistants such as Siri and Alexa, and considers the question of how prejudice can be measured in this area.


Dr. Claudia Schon is a Klara-Marie-Faßbinder-Gastprofessor at TH Bingen.

The talk will take place on the 31.01.20 at 14:00 in room 05-514.

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Activities of the Data Mining Group at ECML/PKDD 2019

The Data Mining Group actively participated in a number of activities at the ECML/PKDD 2019 conference.

The group members presented research papers, posters and also organized two workshops. The workshop programs are available at the following links.

  • DeCoDeML (Deep Continuous-Discrete Machine Learning)
  • AIMLAI & XKDD (Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data mining)

The research paper "Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance: Marius Köppel*, Alexander Segner*, Martin Wagener*, Lukas Pensel*, Andreas Karwath^, Stefan Kramer*" was presented at the Ranking session.

The following papers were presented at the DeCoDeML and SoGood workshops.

DeCoDeML Spotlight Talk 1: Zahra Ahmadi, Sina Malakouti and Stefan Kramer. Deep Tree Networks: A New Symbolic Deep Architecture

DeCoDeML Spotlight Talk 3: Sophie Burkhardt, Nicolas Wagner, Johannes Fürnkranz and Stefan Kramer. Extracting Rules with Adaptable Complexity from Neural Networks using K-Term DNF Optimization

DeCoDeML Spotlight Talk 4: Nicolas Wagner, Sophie Burkhardt, Stefan Kramer. A Deep Convolutional DNF Learner

SoGood Talk: Lukas Pensel and Stefan Kramer. Forecast of Study Success in the STEM Disciplines Based Solely on Academic Record

The DeCoDeML workshop was organized for the first time and participation was well beyond the expectations of the organizing committee.

* = Johannes Gutenberg-Universität Mainz
^ = University of Birmingham

Paper accepted at Pacific Symposium for Biocomputing (PSB)

Our paper "Identifying drug side effects from social media using active learning and crowd sourcing", which was a joint work of Sophie Burkhardt, Julia Siekiera, Josua Glodde, Miguel A. Andrade-Navarro, and Stefan Kramer was accepted at the Pacific Symposium for Biocomputing (PSB) conference as an oral presentation.

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Paper accepted at JMLR

Our paper "Decoupling Sparsity and Smoothness in the DirichletVariational Autoencoder Topic Model" was accepted at the journal for machine learning research (JMLR) pdf code

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Talk by Prof. Dr. Anke Meyer-Baese

With the increasing amount of available medical data, computing power and network speed, modern medical
imaging is facing an unprecedented amount of data to analyze and interpret. Phenomena such as Big Data-omics stemming from several diagnostic procedures and novel multi-parametric imaging modalities tend to produce almost unmanageable quantities of data. The paper addresses the aforementioned context by assuming that a novel paradigm in massive data processing and automation becomes necessary in order to improve diagnostics and facilitate personalized and precision medicine for each patient. Traditional machine learning concepts have demonstrated many shortcomings when it comes to correctly diagnose fatal diseases. At the same time static graph networks are unable to capture the fluctuations in brain processing and monitor disease evolution. Therefore, artificial intelligence and deep learning are increasingly applied in oncologic medical imaging because they excel at providing quantitative assessments of biomedical imaging characteristics. On the other hand, novel concepts borrowed from modern control have paved the path for a dynamic graph theory that can predict neurodegenerative disease evolution and replace longitudinal studies. We chose two important topics, brain data processing and oncologic imaging to show the relevance of these concepts. We believe that these novel paradigms will impact multiple facets of radiology but are convinced that it is unlikely that they will replace radiologists any time in the near future since there are still many challenges in the clinical implementation.

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