About

Nowadays, many practical decision tasks require to build models based on data which included serious difficulties, as imbalanced class distributions, a high number of classes, high-dimensional features, a small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods that can combat the aforementioned difficulties should focus on intense research. The main aim of this workshop is to discuss the problems of data difficulties, identify new issues, and shape future directions for research.

Submission instructions and conference proceedings

Workshop LDD follows all requirements of the ECAI 2025 main conference. Papers must be written in English, be prepared for double-blind review using the ECAI LaTeX template, and not exceed 7 pages (plus at most 1 extra page for references). Excessive use of typesetting tricks to make things fit is not permitted. Please do not modify the style files or layout parameters. Conference proceedings will be publised The Proceedings of Machine Learning Research series.

Topics

Learning from imbalanced data

You try to build a model, but it is biased towards the class that is better represented in the dataset.

Learning from imbalanced data streams, including concept drift management

The situation turns out to be even more difficult than in the previous case, because the data arrives (potentially) forever.

Learning from multi-view/multimodal data

You solve the problem of the curse of dimensionality through space decomposition and ensemble methods.

Automated machine learning

As in meta-learning, you try to give the method full control over the learning process.

Life-long machine learning

You already have a working model, but it turns out that it should solve a new task. And you really don't want to train it from the ground up.

Learning with limited ground truth access

You have experts to label the data, but there are a million objects and only three experts.

Learning in a open set

You're training your model to tell dogs from cats, but you also want to know what happens when you show it a raccoon.

Learning from high dimensional data

In the general case, you have a very large number of features in the set, but you don't want to solve this problem with multi-view approaches.

Learning with a high number of classes

Sometimes there are more classes than objects in a set.

Learning from massive data, including instance and prototype selection

You are trying to manage the problem of a very large dataset by initially sorting it out and finding the most valuable instances.

Learning based on limited data sets, including one-shot learning

It turns out that your data set is not massive. On the contrary, it covers only a few cases. What are you doing?

Learning from incomplete data

Or maybe the data set is not too small, but it turns out to be extremely leaky?

Case studies and real-world applications

Share your struggles with the real datasets!

Organizers

We’re researchers from Department of Systems and Computer Networks, which since 25 years conducts fundamendal research on Machine Learning models in difficult scenarios. We are from Poland.

Paweł Zyblewski

Assistant Professor at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland

Paweł Ksieniewicz

Associate Professor of Computer Science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.

Michał Woźniak

Professor of Computer Science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland.