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Tobias Scheffer

Tobias Scheffer

D-Index & Metrics

Computer Science

D-Index
43
Citations
8160
World Ranking
7962
National Ranking
388

Overview

Tobias Scheffer is affiliated with the University of Potsdam in Germany and has a research focus primarily in the field of Computer Science. Their work spans various subfields including Cognitive Neuroscience, Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, and Human-Computer Interaction.

Their scholarly output includes publications in several venues, with multiple contributions to Procedia Computer Science, npj Digital Medicine, NAR Genomics and Bioinformatics, arXiv (Cornell University), and Zenodo (CERN European Organization for Nuclear Research).

The main topics covered in Tobias Scheffer's research reflect interdisciplinarity and technical depth. These topics include:

  • Computational Drug Discovery Methods
  • Gaze Tracking and Assistive Technology
  • EEG and Brain-Computer Interfaces
  • Topic Modeling
  • PARP inhibition in cancer therapy
  • Autism Spectrum Disorder Research
  • Attention Deficit Hyperactivity Disorder

Recent published papers demonstrate their range in both application and methodology. Notable papers include:

  • RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, 2020, Geoscientific model development
  • Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT), 2020, npj Digital Medicine
  • DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks, 2021, IEEE Transactions on Biometrics Behavior and Identity Science
  • Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading, 2023, Proceedings of the ACM on Human-Computer Interaction
  • Improving cognitive-state analysis from eye gaze with synthetic eye-movement data, 2024, Computers & Graphics

Their frequent collaborators include Paul Prasse, Lena A. Jäger, Silvia Makowski, David R. Reich, and Ralf Herwig. These coauthors have contributed multiple joint publications, indicating longstanding research partnerships.

Best Publications

  • Multi-view clustering

    S. Bickel;T. Scheffer

  • Active Hidden Markov Models for Information Extraction

    Tobias Scheffer;Christian Decomain;Stefan Wrobel

  • Discriminative learning for differing training and test distributions

    Steffen Bickel;Michael Brückner;Tobias Scheffer

  • Unsupervised prediction of citation influences

    Laura Dietz;Steffen Bickel;Tobias Scheffer

  • Discriminative Learning Under Covariate Shift

    Steffen Bickel;Michael Brückner;Tobias Scheffer

  • Stackelberg games for adversarial prediction problems

    Michael Brückner;Tobias Scheffer

  • RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

    Georgy Ayzel;Tobias Scheffer;Maik Heistermann

  • Knowledge Discovery in Databases: PKDD 2006

    Johannes Fürnkranz;Tobias Scheffer;Myra Spiliopoulou

  • Co-EM support vector learning

    Ulf Brefeld;Tobias Scheffer

  • Finding association rules that trade support optimally against confidence

    Tobias Scheffer

  • Efficient co-regularised least squares regression

    Ulf Brefeld;Thomas Gärtner;Tobias Scheffer;Stefan Wrobel

  • Static prediction games for adversarial learning problems

    Michael Brückner;Christian Kanzow;Tobias Scheffer

  • Multi-task learning for HIV therapy screening

    Steffen Bickel;Jasmina Bogojeska;Thomas Lengauer;Tobias Scheffer

  • {AUC} maximizing support vector learning

    U. Brefeld;T. Scheffer

  • A Nonergodic Ground-Motion Model for California with Spatially Varying Coefficients

    Niels Landwehr;Nicolas M. Kuehn;Tobias Scheffer;Norman Abrahamson

  • Taxonomic metagenome sequence assignment with structured output models

    Kaustubh R Patil;Peter Haider;Phillip B Pope;Peter J Turnbaugh

  • Thwarting the nigritude ultramarine: learning to identify link spam

    Isabel Drost;Tobias Scheffer

  • Dirichlet-Enhanced Spam Filtering based on Biased Samples

    Steffen Bickel;Tobias Scheffer

  • Semi-supervised learning for structured output variables

    Ulf Brefeld;Tobias Scheffer

  • Finding the most interesting patterns in a database quickly by using sequential sampling

    Tobias Scheffer;Stefan Wrobel

  • Proceedings of the 17th European conference on Machine Learning

    Johannes Fürnkranz;Tobias Scheffer;Myra Spiliopoulou

Frequent Co-Authors

Stefan Wrobel
Stefan Wrobel University of Bonn
Myra Spiliopoulou
Myra Spiliopoulou Otto-von-Guericke University Magdeburg
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Ralf Herbrich
Ralf Herbrich Hasso Plattner Institute
Ulf Leser
Ulf Leser Humboldt-Universität zu Berlin
Thorsten Joachims
Thorsten Joachims Cornell University
Isabel Dziobek
Isabel Dziobek Humboldt-Universität zu Berlin
Peter J. Turnbaugh
Peter J. Turnbaugh University of California, San Francisco
Thomas Lengauer
Thomas Lengauer Max Planck Institute for Informatics
Reinhold Kliegl
Reinhold Kliegl University of Potsdam

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