Methods of machine learning are approaching their limits whenever training data of a high quality are scarce. The potential reasons for data scarcity are manifold: limited capabilities of human supervisors and processing systems, a need for early predictions which can later be refined, or transfer settings where the only available data stem from some different learning task. Situations like these demand methods that improve the overall life-cycle of machine learning models, including interactions with human supervisors, interactions with other processing systems, and adaptations to different forms of data that become available at different points in time. This demand includes techniques for evaluating the impact of additional resources (e.g., data) on the learning process; strategies for actively selecting information to be processed or queried; techniques for reusing knowledge over time, across different domains or tasks, by recognizing similarities and by adapting to changes; and methods for effectively using different types of information, like labeled and unlabeled data, constraints, and knowledge. Techniques of this kind are being investigated, for example, in the areas of adaptive, active, semi-supervised, and transfer learning. While these investigations often happen in isolation of each other, real use cases of machine learning require interactive and adaptive systems that operate under changing conditions and address the challenges of volume, velocity, and variability of the data. This workshop will continue to stimulate research on systems that combine multiple areas of interactive and adaptive machine learning, by bringing together researchers and practitioners from these different areas. We welcome contributions that present a novel problem, propose a new approach, report practical experience with such a system, or raise open questions for the research community.

The event continues a successful series of workshops and tutorials at ECML-PKDD 2017 in Skopje (Workshop & Tutorial), IJCNN 2018 in Rio (Tutorial), ECML-PKDD 2018 in Dublin (Workshop), ECML-PKDD 2019 in Würzburg (Workshop & Tutorial), ECML-PKDD 2020 (hosted in Ghent, online Workshop), ECML-PKDD 2021 (hosted in Bilbao, online Workshop), ECML-PKDD 2022 in Grenoble (Workshop), ECML-PKDD 2023 in Torino (Workshop), and ECML-PKDD 2024 in Vilnius (Workshop & Tutorial).

This half-day workshop evolves around techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. Therefore, we welcome contributions that present a new problem setting, propose a novel approach, or report experience with the practical deployment of such a system and raise unsolved questions to the research community. In particular, we welcome contributions that address aspects including, but not limited to:

In particular, we welcome contributions that address aspects including, but not limited to:

Novel Techniques for Active, Semi-Supervised, Transfer, or Weakly Supervised Learning
• methods for big, evolving, or streaming data
• methods for recent complex model structures such as deep learning neural networks or recurrent neural networks
• methods for interacting with imperfect or multiple oracles, e.g., learning from crowds
• methods for incorporating domain knowledge and constraints
• methods for timing the interaction and for combining different types of information
• online and ensemble methods for evolving models and systems, with specific switching and fusion techniques, and (inter-)active data integration techniques
Contrastive Learning for Active Learning
• methods combining contrastive learning with active learning for improved sample selection
• techniques integrating self-supervised pre-training into active learning frameworks
• approaches for reducing labeled data dependency using contrastive learning
• cost-aware and resource-efficient strategies for active learning
LLMs with Active Learning
• active learning techniques for efficient fine-tuning of large language models (LLMs)
• human-in-the-loop approaches for iterative LLMs improvement
• LLMs for informed sample selection
• automated sample selection methods for LLMs training
• evaluation methodologies for active learning in LLMs applications
Innovative Use and Applications of Active, Semi-Supervised, Transfer, or Weakly Supervised Learning
• for filtering, forgetting, resampling
• for active class or feature selection, e.g., from multi-modal data
• for detection of change, outliers, frauds, or attacks
• new interactive learning protocols and application scenarios, e.g., brain-computer interfaces, crowdsourcing, etc.
• in application in data-intensive sciences
• in applications with real-world deployment
Techniques for Combined Interactive Adaptive Learning
• methods combining adaptive, active, semi-supervised, or transfer learning techniques
• cost-aware methods and methods for estimating the impact of employing additional resources, such as data or processing capacities, on the learning progress
• methodologies for the evaluation of such techniques and for comparative studies
• methods for automating the control of an interactive adaptive learning process.