Workshop Topics

This half-day workshop aims to address the challenges and opportunities of learning from small data, focusing on methodologies that optimize the entire learning pipeline under data-scarce conditions. The workshop will bring together researchers and practitioners from diverse disciplines, including physics-informed machine learning, few-shot learning, in-context learning, data augmentation, active learning, semi-supervised learning, transfer learning, and experimental design and optimization. Additionally, we will explore the role of surrogate models, which reduce the need for extensive data collection by approximating expensive-to-evaluate functions, making them particularly useful in scenarios with limited data availability. These approaches, among other solutions for learning in data-scarce environments, will serve as the foundation for sharing innovative techniques, discussing best practices, and exploring the frontiers of learning from small data. By emphasizing techniques that minimize reliance on large data, the workshop seeks to inspire advancements in a range of fields where data is inherently limited or costly to obtain.

We invite contributions that introduce novel problem settings, propose groundbreaking methodologies, or share practical experiences in deploying systems designed to operate effectively with small data. Submissions that highlight interdisciplinary approaches or address domain-specific challenges in small data learning are particularly encouraged. Additionally, we welcome thoughtprovoking questions or critical perspectives that aim to spark discussion and push the boundaries of this important research area. In particular, we invite contributions that address aspects including, but not limited to:

Challenges and Advances in Learning from Data-Scarce Environments

• Methods capable of handling high-dimensional data efficiently in data-scarce environments

• New human-in-the-loop learning and application scenarios, e.g., brain-computer interfaces and crowdsourcing Techniques for adaptively learning from continuous data streams

• Strategies for real-world deployment in dynamic environments

• Methods for handling the imbalanced data, data drift, noisy labeled data problems

Developing scalable frameworks for large-scale data streams

Physics and Math-Informed Machine Learning Techniques

• Methods for employing physical laws to enhance learning with limited data

• Methods for applying physics-based constraints including boundary conditions and loss functions in the learning process

• Methods to handle ODEs and PDEs to effectively model dynamic physical systems

• Integrate control systems within frameworks to optimize performance and stability

• Apply techniques in fields like fluid dynamics and climate modeling for practical solutions

Transfer Learning, Data Augmentation, Generating Synthetic Data, Few-Shot Learning, Active Learning, and In-Context Learning

• Methods for adapting pre-trained models to improve performance with limited data

• Techniques for applying data augmentation to artificially expand dataset size and diversity

• Strategies for few-shot learning to enable effective learning with minimal labeled examples

• Meta-learning frameworks to improve few-shot learning performance across diverse tasks

• Methods for generating high-quality synthetic data to enrich existing datasets

• Strategies for active learning to prioritize the most informative data points for labeling

• Novel strategies of active learning that use low query budget

• New methods for applying active learning in high dimensional spaces (e.g., deep active learning)

• Selectively acquire features to maximize information gain from small datasets

• Implement strategies for effective label acquisition in multi-label active learning

• Techniques for in-context learning to adapt models dynamically based on provided contextual information

Semi-Supervised, Self-Supervised, Weak, and Federated Learning

• Methods that use both labeled and unlabeled data to improve model performance in data-scarce environments

• Generating pseudo-labels from model predictions to enhance learning from unlabeled data

• Combining weak classifiers to create robust and generalizable ensemble models

• Training models collaboratively across distributed devices while preserving data privacy

Design of Experiments and Surrogate Models in Small Data Contexts

• Methods for optimizing experimental design under limited data

• Integrating prior knowledge and constraints for efficiency

• Adaptive experimental design that adjusts dynamically

• Selecting the most informative experimental conditions

• Leveraging surrogate models (e.g., Gaussian Processes, Bayesian Optimization) to reduce data collection needs

• Applications of surrogate-assisted design in experiments, simulations, and optimization

Important Dates:

Submission open: TBA

Paper Submission Deadline: Friday, June 5, 2026

Author Notification: Friday, July 3, 2026 (ECML-PKDD offers the early bird registration rate until TBA)

Camera Ready: 10.07.2026

Workshop: TBA [At least one author of each accepted paper must be registered.]

Submission Instructions

We invite submissions of original work in the form of regular papers (8–16 pages) or extended abstracts (2–4 pages). All submissions will undergo a double-blind peer-review process, and accepted papers will be presented and discussed at the workshop. Extended abstracts are particularly suited for works-in-progress or industrial case studies. At least one author of each accepted paper is required to register for the conference and attend the workshop. 

We plan to publish the workshop proceedings with Springer, submissions will be made via CMT system of the ECML PKDD conference and the papers should be formatted in the (Lecture Notes in Computer Science ) LNCS style. Springer’s proceedings LaTeX templates are also available in Overleaf. 

Reviews are double-blind; papers must not include information that reveal the authors' identities. 

Program of the Workshop

TBA

Committee 

Organizing Committee (alphabetical, first name):

• Adrian Ulges: RheinMain University of Applied Sciences, Wiesbaden, Germany

• Alaa Tharwat: Bielefeld University of Applied Sciences and Arts (HSBI), Germany

• Barbara Hammer: Bielefeld University, Germany

• Markus Lange-Hegermann: OWL University of Applied Sciences and Arts, Lemgo, Germany

• Wolfram Schenck: Bielefeld University of Applied Sciences and Arts (HSBI), Germany

• Mohamed-Rafik Bouguelia: University of Doha for Science and Technology, Qatar