Special Issue on Machine Learning Journal

The Machine Learning journal invites submissions of research papers addressing all aspects of discovery science – a research discipline concerned with the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, artificial intelligence and big data analytics, as well as their application in various domains.

Submissions addressing all aspects of discovery science are welcome. Research papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data, as well as heterogeneous, continuous or imprecise data are encouraged. Research papers in the fields of computational scientific discovery, mining scientific data, computational creativity and discovery informatics are also welcome. Submissions addressing applications of artificial intelligence in different domains of science, including biomedicine and life sciences, materials science, astronomy, physics, chemistry, as well as social sciences are encouraged. Finally, submissions addressing ethical and trustworthiness aspects of artificial intelligence such as explainability, interpretability, fairness, bias, privacy, robustness and accountability are also encouraged.

Possible topics include, but are not limited to:

  • Machine Learning, including supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning
  • Active learning, online learning, transfer learning, continual learning etc.
  • Reinforcement learning
  • AutoML, Meta-Learning, Planning to Learn
  • Representation learning for vision, text, audio, language, and other data modalities
  • Knowledge Discovery and Data Mining
  • Anomaly and Outlier Detection
  • Learning from Complex Data
    • Data Streams, Evolving Data, Change Detection & Concept drift
    • Time-Series Analysis
    • Spatial, Temporal and Spatio-temporal Data Analysis
    • Unstructured Data Analysis (textual and web data)
    • Learning on graphs and other topologies
    • Complex Network Analysis
  • Causal Modeling and reasoning
  • Neuro-symbolic learning & hybrid AI systems (logic & formal reasoning, etc.)
  • Physics-informed machine learning
  • Computational equation discovery and Symbolic Regression
  • Data and Knowledge Visualization
  • Explainable AI and Interpretable Machine Learning
  • Human-Machine Interaction for Knowledge Discovery and Management
  • AI and High-performance Computing, Grid and Cloud Computing
  • Optimisation
  • AI Creativity
  • Process Discovery and Analysis
  • Evaluation of Models and Predictions in Discovery Setting
  • Applications of the above techniques in scientific domains, such as Physical sciences (e.g., materials sciences, particle physics), Life sciences (e.g., biology, medicine, neuroscience etc.), Environmental sciences, Natural and social sciences

We invite both:

  • Extended contributions from papers previously published at Discovery Science 2025 or other relevant conferences, provided that the submission constitutes a significant contribution beyond the conference paper containing at least 30% of new material (e.g., extensions of the method, additional technical results, etc.) as compared to the conference version of the paper.
  • Original contributions falling within the topics of the Special Issue.

The guest editors (accounting for reviewers’ comments) will make the decision on whether the difference is significant enough to warrant publication. In case of extended contributions, the journal version should include a short paragraph explaining how it extends the previously published conference paper.

Schedule:

  • Paper submission deadline: February 13, 2026
  • First notification of acceptance: May 22, 2026
  • Deadline for revised submissions: July 10, 2026
  • Final notification of acceptance: September 18, 2026
  • Expected publication date (online): November/December 2026

Submission procedure

To submit to this issue, authors have to make a journal submission to the Springer Machine Learning journal and select the type of submission to be for the “S.I.: Discovery Science 2025” special issue. It is highly recommended that submitted papers do not exceed 20 pages including references. Every paper may be accompanied with unlimited appendices.

The papers should be formatted using Springer Nature’s LaTeX template. The journal requires authors to include an information sheet as a supplementary material that contains a short summary of their contribution and specifically address the following questions:

  • What is the main claim of the paper? Why is this an important contribution to the machine learning literature? [“We are the first to have done X” is not an acceptable answer without stating the importance of X.]
  • What is the evidence you provide to support your claim? Be precise. [“The evidence is provided by experiments and/or theoretical analysis” is not an acceptable answer without a summary of the main results and their implications.]
  • What papers by other authors make the most closely related contributions, and how is your paper related to them?
  • Have you published parts of your paper before, for instance in a conference? If so, give details of your previous paper(s) and a precise statement detailing how your paper provides a significant contribution beyond the previous paper(s).

Guest Editors

  • Gianvito Pio, University of Bari Aldo Moro, Bari, Italy
  • Jurica Levatić, Jožef Stefan Institute, Ljubljana Slovenia
  • Nikola Simidjievski, Télécom Paris, Institut Polytechnique de Paris, Paris, France