Lernen. Wissen. Daten. Analysen. - Learning. Knowledge. Data. Analytics.
LWDA 2021 hosted by the Munich Center for Machine Learning (MCML), Munich, 01.09–03.09.2021.
KDML is a workshop series that aims at bringing together the German Machine Learning and Data Mining community. The KDML 2021 Workshop is co-located with the annual LWDA 2021 – Learning, Knowledge, Data, Analytics – conference that will take place as an online conference organized by the Munich Center for Machine Learning (MCML) from September 01, 2021 to September 03, 2021.
We invite submissions on all aspects of data mining, knowledge discovery, and machine learning. In addition to original research, we also invite resubmissions of recently published articles at major conference venues related to KDML. Moreover, KDML explicitly invites student submissions.
We encourage submissions addressing Fairness in Machine Learning as our focus topic of KDML 2021. As a cross-cutting topic throughout our community we are looking forward to submissions covering formal foundations of fairness in machine learning and a variety of applications of all areas of fair and transparent data analytics.
Wednesday 2021-09-01: 13:30-16:00
11:30-12:00
Bettina Finzel, René Kollmann, Ines Rieger, Jaspar Pahl and Ute Schmid: Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification (Joint Session)
12:00-12:30
Michael Steininger, Konstantin Kobs, Padraig Davidson, Anna Krause and Andreas Hotho: Density-based weighting for imbalanced regression (Joint Session)
13:30-13:55
Leonid Schwenke and Martin Atzmueller: Abstracting Local Transformer Attention for Enhancing Interpretability on Time Series Data
13:55-14:20
Dominik Dürrschnabel, Maren Koyda and Gerd Stumme: Attribute Selection using Contranominal Scales
14:20-14:45
Pascal Welke, Fouad Alkhoury, Christian Bauckhage and Stefan Wrobel: Decision Snippet Features
14:45-15:10
Felix Stamm, Martin Becker, Markus Strohmaier and Florian Lemmerich: Redescription Model Mining
15:10-15:35
Bastian Schäfermeier, Gerd Stumme and Tom Hanika: Topic Space Trajectories
15:35-16:00
Deniz Neufeld: Visualization Methods for Periodic Time Series Data
Thursday 2021-09-02: 11:00-12:30
11:00-11:15
Christopher Hagedorn and Johannes Huegle: Constraint-Based Causal Structure Learning in Multi-GPU Environments
11:15-11:30
Max Luebbering, Michael Gebauer, Rajkumar Ramamurthy, Maren Pielka, Christian Bauckhage and Rafet Sifa: Utilizing Representation Learning for Robust Text Classification Under Datasetshift
11:30-11:45
Tobias Rohrer, Ludwig Samuel, Adriatik Gashi, Gunter Grieser and Elke Hergenröther: Foosball table goalkeeper automation using reinforcement learning
11:45-12:00
Lars Schmarje and Reinhard Koch: Life is not black and white - Combining Semi-Supervised Learning with fuzzy labels
12:00-12:15
Felix Gonsior, Sascha Mücke and Katharina Morik: Structure Search for Normalizing Flows
12:15-12:30
Philipp Doebler, Anna Doebler, Philip Buczak and Andreas Groll: Interactions of Scores Derived from Two Groups of Variables: Alternating Lasso Regularization Avoids Overfitting and Finds Interpretable Scores
Thursday 2021-09-02: 13:30-16:00
13:30-13:55
Simon Omlor and Alexander Munteanu: Oblivious sketching for logistic regression
13:55-14:20
Daniel Neider, Jean-Raphaël Gaglione, Ivan Gavran, Ufuk Topcu, Bo Wo and Zhe Xu AdvisoRL: Advice-Guided Reinforcement Learning in a non-Markovian Environment
14:20-14:45
Xuan Xie: Property-Directed Verification and Robustness Certification of Recurrent Neural Networks
14:45-15:10
Mirko Bunse and Katharina Morik: A PAC Learning Theory for Active Class Selection
15:10-15:35
Erich Schubert: HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations
Friday 2021-09-03: 11:00-12:30
11:00-11:15
Erik Thordsen and Erich Schubert: CANDLE: Classification And Noise Detection With Local Embedding Approximations
11:15-11:30
Andreas Lohrer, Anna Beer, Maximilian Archimedes Xaver Hünemörder, Jenny Lauterbach, Thomas Seidl and Peer Kröger: AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval
11:30-11:45
Nil Ayday and Debarghya Ghoshdastidar: Improvement on Incremental Spectral Clustering
11:45-12:10
Eike Stadtländer, Tamás Horváth and Stefan Wrobel: Learning Weakly Convex Sets in Metric Spaces
Friday 2021-09-03: 13:30-15:00
13:30-13:55
Mirko Lenz, Premtim Sahitaj, Sean Kallenberg, Christopher Coors, Lorik Dumani, Ralf Schenkel and Ralph Bergmann: Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs
13:55-14:20
Philip Hausner and Michael Gertz: News Article Extraction Using Graph Embeddings
14:20-14:45
Patrick Kolpaczki, Viktor Bengs and Eyke Hüllermeier: Identifying Top-k Players in Cooperative Games via Shapley Bandits
Martin Atzmueller, Osnabrück University
Emmanuel Müller, TU Dortmund University
Achim Rettinger, Trier University
Alexander Hinneburg, Martin-Luther-University Halle-Wittenberg
Andreas Hotho, University of Würzburg
Ansgar Scherp, Ulm University
Arthur Zimek, University of Southern Denmark
Bernd Bischl, Ludwig-Maximilians-University Munich
Christian Bauckhage, University of Bonn
Eirini Ntoutsi, Freie Universität Berlin
Ernesto Diaz-Aviles, Libre AI
Florian Lemmerich, University of Passau
Gerd Stumme, University of Kassel
Hannes Mühleisen, Centrum Wiskunde & Informatica (CWI)
Johannes Fürnkranz, JKU Linz
Klaus-Dieter Althoff, University of Hildesheim
Kristian Kersting, TU Darmstadt
Marius Kloft, TU Kaiserslautern
Marwan Hassani, Eindhoven University of Technology
Matthias Hagen, Bauhaus University Weimar
Ralf Krestel, Hasso Plattner Institute
Rainer Gemulla, University of Mannheim
Robert Jäschke, Humboldt-Universität zu Berlin
Stephan Günnemann, TU Munich
Thomas Seidl, Ludwig-Maximilians-University Munich
Ulf Leser, Humboldt-Universität zu Berlin
Ute Schmid, University of Bamberg
Wouter Duivesteijn, Eindhoven University of Technology
Submissions should be alternatively: 4-6 pages long for short papers, from 7 up to 12 pages long for full papers. Pre-published Paper, i.e., one-pager summaries for resubmissions recently published at other renowned conferences - 1 Page abstract, plus link to original paper - that will be presented at KDML but will not be re-published. All contributions must be submitted via EasyChair as PDF at: https://easychair.org/conferences/?conf=lwda2021 Please select the track “Knowledge Discovery and Machine Learning (KDML)” when submitting your paper. The publication of the conference proceedings will be with CEUR Workshop Proceedings (CEUR-WS.org). All papers should be formatted with the customized CEUR template: https://www.overleaf.com/read/sxqkrwncfrhg. All workshop participants have to register for the LWDA 2021 conference.
Please note that all deadlines are AoE (Anywhere on Earth).