Accepted Papers

Here is the list of this year’s accepted papers:

  1. Credal Knowledge Tracing for Imprecise and Uncertain MCQ
    • Dorra Sassi, Constance Thierry, David Gross-Amblard
  2. Paving the Way for the Prevention of Injuries in Outdoor Running
    • Bouke Scheltinga, Jasper Reenalda, Jaap Buurke, Joost Kok
  3. Estimating the Learning Capacity of Bacterial Metabolic Networks
    • Bastien Mollet, Paul Ahavi, Antoine Cornuéjols, Jean-Loup Faulon, Evelyne Lutton, Alberto Tonda
  4. Semi-supervised learning with pairwise instance comparisons for medical instance classification
    • Anne Rother, Till Ittermann, Myra Spiliopoulou
  5. Local-global Data Augmentation for Contrastive Learning in Static Sign Language Recognition
    • Ariel Basso Madjoukeng, Bélise Kenmogne Edith, Pierre Poitier, Benoît Frénay, Jérôme Fink
  6. SiamCircle: Trajectory Representation Learning in Free Settings
    • Maedeh Nasri, Mitra Baratchi, Alexander Koutamanis, Carolien Rieffe
  7. Synthetic Tabular Data Detection In the Wild
    • G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona, Tanguy Urvoy
  8. Assessing the Impact of Graph Structure Learning in Graph Deviation Networks
    • Canberk Ozen, Slawomir Nowaczyk, Prayag Tiwari, Sepideh Pashami
  9. The When and How of Target Variable Transformations
    • Loren Nuyts, Jesse Davis
  10. Transfer learning for balancing performance and scalability of ML models
    • Mateusz Żarski, Slawomir Nowaczyk
  11. Balancing global importance and source proximity for personalized recommendations using random walk length
    • Tsuyoshi Yamashita, Kunitake Kaneko
  12. Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data
    • Marco Loog, Jesse Krijthe, Manuele Bicego
  13. BOWSA: a contribution of sensitivity analysis to improve Bayesian optimization for parameter tuning
    • Lise Kastner, Bertrand Cuissart, Jean-Luc Lamotte
  14. Overfitting in Combined Algorithm Selection and Hyperparameter Optimization
    • Sietse Schröder, Mitra Baratchi, Jan Van Rijn
  15. Local Subgroup Discovery on Attributed Network Graphs
    • Carl Vico Heinrich, Tommie Lombarts, Jules Mallens, Luc Tortike, David Wolf, Wouter Duivesteijn
  16. Imposing Constraints in Probabilistic Circuits via Gradient Optimization
    • Soroush Ghandi, Benjamin Quost, Cassio de Campos
  17. Improving Next Tokens via Second-Last Predictions with ‘Generate and Refine’
    • Johannes Schneider
  18. Detection of Large Language Model Contamination with Tabular Data
    • Benoît Ronval, Pierre Dupont, Siegfried Nijssen
  19. Data Augmentation involving GMM and LLM
    • Noor Khalal, Abdallah Djamai, Imed Keraghel, Mohamed Nadif
  20. Make Literature-Based Discovery Great Again through Reproducible Pipelines
    • Bojan Cestnik, Andrej Kastrin, Boshko Koloski, Nada Lavrač
  21. Extracting information in a low-resource setting: case study on bioinformatics workflows
    • Clémence Sebe, Sarah Cohen-Boulakia, Olivier Ferret, Aurélie Névéol
  22. Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages
    • Vu Minh Hoang Dang, Rakesh Verma
  23. Expertise Prediction of Tetris Players Using Eye Tracking Information
    • Stijn Rotman, Gianluca Guglielmo, Boris Cule, Michal Klincewicz
  24. Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks
    • Julian Vexler, Björn Vieten, Martin Nelke, Stefan Kramer
  25. Bridging Spatial and Temporal Contexts: Sparse Transfer Learning
    • Daniel Persson, William Wahlberg, Anna Vettoruzzo, Slawomir Nowaczyk
  26. Meta-learning and Data Augmentation for Stress Testing Forecasting Models
    • Ricardo Inácio, Vitor Cerqueira, Marília Barandas, Carlos Soares
  27. Pragmatic Paradigm for Multi-stream Regression
    • Nuwan Gunasekara, Slawomir Nowaczyk, Sepideh Pashami
  28. Two-in-one Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient under Anesthesia.
    • Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet
  29. An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
    • Miro Miranda, Francisco Mena, Andreas Dengel
  30. Performative Drift Resistant Classification using Generative Domain Adversarial Networks
    • Maciej Makowski, Brandon Gower-Winter, Georg Krempl
  31. Extracting Moore Machines from Transformers using Queries and Counterexamples
    • Rik Adriaensen, Jaron Maene
  32. Obtaining Example-Based Explanations from Deep Neural Networks
    • Genghua Dong, Henrik Bostrom, Michalis Vazirgiannis, Roman Bresson
  33. Relevance-aware Algorithmic Recourse
    • Dongwhi Kim, Nuno Moniz
  34. Expanding Polynomial Kernels for Global and Local Explanations of Support Vector Machines
    • Rikard Vinge, Stefan Byttner, Jens Lundström
  35. A Constrained Declarative Based Approach for Explainable Clustering
    • Mathieu Guilbert, Christel Vrain, Thi-Bich-Hanh Dao