Allgemein

Upcoming events:

• The lecture series Ringvorlesung "Transparenz, Datensicherheit & Co.: Umsetzbare Anforderungen an KI-Systeme?" will be closed on 18th of June with a lecture by Prof. Stefan Kramer with a conclusion and an outlook, followed by a presentation of the practical perspective by Prof. Steffen Staab, University of Stuttgart.
• On the 20th of June the summer party of institute of computer science will take place on the meadows in front of the institute building (Staudingerweg 9, 55128 Mainz).

Current activities:

• We warmly welcome Federico Peiretti from the University of Turin, Italy, as a visting PhD student in the data mining group from 1st of May until 31st of August.

Current Presentations and Publications

• The Ringvorlesung "Transparenz, Datensicherheit & Co.: Umsetzbare Anforderungen an KI-Systeme?" started this semester, organised by the TOPML project, and was opened on the 30th of April by a talk from Prof. Stefan Kramer. He highlighted the necessity of non-functional requirements for AI systems and the practical perspective as well as the research approach of TOPML. On the 14th of May Dr. Mattia Cerrato gave a lecture on various practical and theoretical examples to illustrate fairness in AI.
  In addition, the following lectures were presented::
  • 30th of April: Introduction from the research perspective of Prof. Stefan Kramer and the practical perspective of Prof. Nina Hubig, Clemson University.
  • 7th of May: Privacy from the research perspective of Prof. Kristian Kerting, TU Darmstadt, and the practical perspective of Dr. Torsten Panholzer, Universitätsmedizin Mainz.
  • 14th of May: Fairness from the research perspective of Dr. Mattia Cerrato, Johannes Gutenberg University Mainz, and the practical perspective of Prof. Christian Wolff, University Regensburg.
  • 21st of May: Resource efficiency from the research perspective of Prof. Sarah Neuwirth, Johannes Gutenberg University Mainz, and the practical perspective of Enver Solan, GEMESYS.
  • 4th of June: Transparency from the research perspective of Dr. Patrick Schramowski, TU Darmstadt, and the practical perspective of René Heinitz, Deloitte Consulting GmbH.
  • 11th of June: German perspektive from the research perspective of Dr. Nicolas Flores-Herr, Frauenhofer-Institut, and the practical perspective of Vivien Bender, Springer Nature.
  • 18th of June: Conclusion and outlook from the research perspective of Prof. Stefan Kramer and the practical perspective of Prof. Steffen Staab, University of Stuttgart.
• Our paper Differentially Private Sum-Product Networks, which is a joint work of Xenia Heilmann, Mattia Cerrato and Ernst Althaus has been accepted at the 41st International Conference On Machine Learning (ICML), Vienna, Austria.

We enjoyed participating in the following past events:

• On the 23rd of April, Prof. Stefan Kramer and Prof. Katharina von der Wense were the AI experts at the round table in the Kakadu bar, Mainz, following the science fiction opera Humanoid in the state theatre of Mainz, Germany. Humanoid narrates the relationship between humans and technology and the discussion afterwards went beyond questions such as "Can artificial intelligence develop consciousness?", "Where is the boundary between human and machine?" and "Do only humans have the right to a self-determined existence?".
• Just like every year, we happily greeted curious and interested young girls for another exciting Girls' Day to the "Abenteuer Informatik: Informatik wörtlich begreifen" on the 25th of April. With organizational support from the Ada Lovelace project, we were able to offer an interesting insight into the world of computer science.
• On the 29th and 30th of April the workshop "KI als Chance oder Risiko: von Grundlagen und Anwendungen bis hin zu Herausforderungen" was successfully held by PhD students from the TOPML project and organized with Q+. In this hands-on workshop practical examples were used to teach the basics of AI and discuss current chances and risks associated with its application.

Other highlights:

• On the 3rd of May we welcomed Blake VanBerlo from the University of Waterloo, Canada, for great discussions.
• We were pleased to welcome Dr. Stefan Kuhn, University of Tartu, Estonia, from June 3rd to 9th.

Our paper Differentially Private Sum-Product Networks, which is a joint work of Xenia Heilmann, Mattia Cerrato and Ernst Althaus has been accepted at the 41st International Conference On Machine Learning (ICML), Vienna, Austria.

Abstract

Differentially private ML approaches seek to learn models which may be publicly released while guaranteeing that the input data is kept private. One issue with this construction is that further model releases based on the same training data (e.g. for a new task) incur a further privacy budget cost. Privacy-preserving synthetic data generation is one possible solution to this conundrum. However, models trained on synthetic private data struggle to approach the performance of private, ad-hoc models. In this paper, we present a novel method based on sum-product networks that is able to perform both privacy-preserving classification and privacy-preserving data generation with a single model. To the best of our knowledge, ours is the first approach that provides both discriminative and generative capabilities to differentially private ML. We show that our approach outperforms the state of the art in terms of stability (i.e. number of training runs required for convergence) and utility of the generated data.

Upcoming Events: Third European Workshop on Algorithmic Fairness

• From July 1 to July 3, 2024, we will be hosting the Third European Workshop on Algorithmic Fairness (EWAF’24) in Mainz, organized by members of the chair and associated members. The Workshop creates a unique platform for researchers from academia and industry working on algorithmic fairness in the context of Europe’s legal and societal framework. The objective is to promote discussions within an interdisciplinary setting. Interdisciplinary submissions dealing with the fields of computer science, law, sociology, philosophy, and EU specific topics related to algorithmic fairness are welcomed. Submission deadline is on March 15, 2024 on EasyChair. The workshop will feature keynotes by, amongst others, Virginia Dignum and Isabel Valera.

Current Publications and Presentations

• The ‘Stockholm declaration on AI ethics', prepared by Ross D. King, Teresca Scassa, Stefan Kramer and Hiroaki Kitano, was published at Nature on February 20, 2024.
• Our paper 'Peer Learning: learning Complex Policies in Groups from Scratch via Action Recommendations', combining social learning theory with reinforcement learning, has been presented by Cedric Derstroff and Jannis Brugger at AAAI 2024 in Vancouver on March 1.
• On March 5, Prof. Stefan Kramer gave a talk on “AI for Scientific Discovery” at the University of Waikato, New Zealand, where is currently visiting due to his position as an honorary professor.

Recent Events: curAMet and Retreat of Data Mining Chair

• Focused on discussing latest progresses and future collaborations, all curATime partners, including members of the data mining group, met on February 6 for the first cluster conference at Rheingoldhalle Mainz. Posters were presented, and Prof. Kramer was invited to give a keynote.
• The data mining chair spent a day with interesting talks at the Gutenberg Digital Hub in Mainz to exchange current work and ideas in the research group on February 9, 2024.

Starting with this article, we are transitioning our public news reporting to a quarterly overview format. This editorial shift aims to provide a more extensive perspective on our activities. We continue referring to relevant articles for further details. Nevertheless, we encourage you to reach out to our group members for context, or your suggestions and in-depth-questions.

We are pleased to announce the following upcoming talk:

• On 18th of Dezember, Prof. Stefan Kramer will lecture on AI for sustainability: “KI für Nachhaltigkeit: Ein kleiner, technologischer Beitrag". The lecture in German language is part of the series Voices for Climate, where Luisa Neubauer gave the first talk.

We enjoyed participating in the following inspiring past events:

• Workshop on the Nobel Turing Grand Challenge: We discussed with the Nobel laureate Arieh Warshel and Turing prize winner Ed Feigenbaum and several other luminaries from AI and other fields how to advance the use of AI in research to tackle the Nobel Turing Grand Challenge: creating AI systems capable of making Nobel quality discoveries comparable to the best scientific breakthroughs.
• On 30th of November during a symposium of about AI at the University of Mainz, Prof. Stefan Kramer discussedwith Prof. Christoph Bläsi and Prof. Paul Czodrowski in a panel about AI in science and society. In the afternoon, he offered an introduction into AI in scientific discovery. The total symposium took place on 22nd and 30th of November in German language, organzed by the KI-Projektbüro Rheinland-Pfalz under the heading of "KI@JGU".
• Successful Ph.D. defense of Staffan Arvidsson McShane at Uppsala University with the title Confidence Predictions in Pharmaceutical Science. Supervisor Prof. Ola Spjuth summarizes: “The thesis applied machine learning and conformal prediction in various settings for prediction of drug metabolism, when transitioning between biological assays, and for monitoring disease progression”.
• Successful Ph.D. defense of Shuyi Yang at the University of Turin on inductive, fair and private semi-supervised learning.
• Talk JuGSo: Künstliche Intelligenz – Segen oder Fluch? by Prof. Stefan Kramer hosted by the Social Democratic Party Wiesbaden on November, 14th
• We organized the first interdisciplinary workshop on ML, law and society at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023) from 18th to 22nd of September.

Furthermore, funding for two projects with our contributions has been granted by Carl Zeiss Stiftung recently:

• MAINCE: Medical AI combining Natural products and CEllular Imaging. MAINCE will use AI approaches to identify new and much-needed therapeutics in immunology. Clues to the effect of therapeutics through state-of-the-art imaging techniques will be linked by (amongst others) generative AI to laboratory experiments to accelerate drug development and make it more efficient. Linking natural products with cell painting holds the promise for the discovery of novel drugs, powered by cutting edge AI.
• Multi-dimensionAI: linking scales of information to improve care for patients with heart failure. The project team is researching the treatment of heart failure using a combination of innovative AI approaches and robotics. Comprehensive health data will be used to train an AI that will identify new causal factors for heart failure and incorporate them into the therapy.

Other highlights:

• The article Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates has been published in iScience. It has been written by Franziska Lang, Patrick Sorn, Barbara Schrörs, David Weber and Martin Löwer from TRON, Translational Oncology at our University Medical Center as well as Stefan Kramer and Ugur Sahin, co-founder of BioNTech SE.

Our paper Discriminative machine learning for maximal representative subsampling, which is a joint work of Tony Hauptmann, Sophie Fellenz, Laksan Nathan, Oliver Tüscher and Stefan Kramer has been accepted at Scientific Reports.

Abstract

Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS – Soft-MRS – that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.

Our paper "Classifying Aircraft Categories from Magnetometry Data Using a Hypothesis-based Multi-Task Framework", which is a joint work of Julian Vexler and Stefan Kramer, has been accepted at the 12th International Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 (https://ecai2023.eu/ECAI2023-3), as a sister conference to ECAI 2023, the 26th European Conference on Artificial Intelligence. The paper is a result of an on-going project about the air-side integration of magnetometers for object classification.

Abstract

Airport traffic surveillance requires reliable safety systems to prevent accidents in safety-critical areas. This paper examines airport aprons, where existing holding point protection systems have shown that they are sometimes not able to prevent accidents. One possible solution to this problem is the use of innovative sensor technology such as magnetometers. These sensors can be used to measure the distortion of the earth's magnetic field by metallic objects. The main objective is to identify the geometrical pattern of a passing object by fusing coherent events, and classify it into a category based on its size. We propose a hypotheses-based multi-task framework for the classification of aircraft by making use of the estimated motion behaviour of a passing object. The framework includes statistical components, domain knowledge, and artificial intelligence solutions to infer the geometrical pattern and motion vector of an object from a predefined set of possible hypotheses. In future work, we aim to optimize the framework using synthetic and real-world data to increase its robustness and generalization ability to other airports.

Derian Boer has joined the Data Mining group and will be working in the Cluster for Atherothrombosis and Individualized Medicine (curATime) a future cluster funded by the BMBF, where powerful players from science and industry have joined forces to develop tailored treatment and prevention concepts for cardiovascular diseases and their clinical application.

We are very happy about the successful realization of our first regional AI conference, MAInC 2023, with outstanding keynote speakers and a focus on trustworthy AI, i.e., also the legal and ethical implications of AI (Linkedin), which was also mentioned by the SWR.

Håkan Lane has joined the Data Mining group and will be working in the Cluster for Atherothrombosis and Individualized Medicine (curATime), a future cluster funded by the BMBF, where powerful players from science and industry have joined forces to develop tailored treatment and prevention concepts for cardiovascular diseases and their clinical application.

Our paper "Identifying Aircraft Motions and Patterns from Magnetometry Data Using a Knowledge-Based Multi-Fusion Approach", which is a joint work of Julian Vexler and Stefan Kramer, has been accepted at the international conference on information fusion (https://fusion2023.org/). The paper is a result of an on-going project about the air-side integration of magnetometers for object detection.

Abstract

In aviation there are many safety-critical domains where reliable safety systems are essential to prevent any kind of hazard. This paper focuses on airport aprons, where currently used holding point protection systems have shown to be not faultless, sometimes leading to avoidable accidents. One way to avoid such accidents is by means of innovative sensor technology, in our case, magnetometers, i.e. sensors measuring the distortion of the earth’s magnetic field by metallic objects. The main goal is to use the magnetometry data to detect passing aircraft and to capture their geometrical pattern as well as to estimate their motion vector. Therefore, we present a spatio-temporal cluster fusion and an event fusion algorithm. The cluster fusion can be applied as a post-processing step to any spatio-temporal clustering method and is able to more accurately represent aircraft patterns by integrating expert knowledge into the fusion process. In this context, we present a spatio-temporal cluster tree representation for a fast and accurate estimation of the motion vector. Finally, the data-driven event fusion is able to separate detected aircraft crossings into separate events by employing domain-knowledge. In future work, we aim to come up with a framework making use of the cluster results and estimated motion vector to classify and infer the position of an aircraft, before this is deployed as a real-time application.