Blogs on Guest article » Lamarr Institute

CapyMOA: Efficient Machine Learning for Evolving Data Streams

Dr. Heitor Murilo Gomes,

25. February 2026

Real-time data keeps changing – your models should too. CapyMOA brings high-performance stream learning and concept drift handling to Python, bridging MOA efficiency with practical usability....

Illustration in a Van-Gogh-inspired style showing a calm humanoid robot observing a flowing stream of data that transforms into adaptive decision trees. Swirling blue and yellow brushstrokes depict real-time data streams, changing graphs, and evolving tree structures, symbolizing Streaming Gradient Boosting and online learning under concept drift.

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Streaming Gradient Boosting: Pushing Online Learning Beyond its Limits

Dr. Nuwan Gunasekara,

04. February 2026

Streaming Gradient Boosting (SGB) brings gradient boosting to evolving data streams—adapting models to concept drift in real time with strong accuracy and efficiency across classification and regression tasks....

00 Blog Beitragsbild HEROS - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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Green Online Learning with Heterogeneous Ensembles

Kirsten Köbschall,

29. January 2026

What if greener AI could also be more accurate? Discover how heterogeneous online ensembles like HEROS boost performance, cut resource costs, and adapt instantly to evolving data streams....

00 Blog Studis fuer Studis LDA - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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Linear discriminant analysis: A method for dimension reduction of classifiable data sets

Fabian Gawellek,

19. April 2023

Large amounts of data are problematic when calculating ML algorithms. One solution lies in dimension reduction methods such as linear discriminant analysis (LDA), which reduces the number of data features while taking into account the existing class labels....

00 Blog Studis fuer Studis PCA - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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Principal component analysis – A method for dimension reduction on large amounts of data

Jan Stettner,

12. April 2023

Learning on large, high-dimensional data sets provides particularly meaningful results, but consumes a lot of memory and computing capacity. The dimension reduction method can help to solve this problem....

230222 Blog CAIS - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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(No) opinion on Artificial Intelligence? The public’s view on AI

Center of Advanced Internet Studies (CAIS),

22. February 2023

Have you had a conversation recently about how Artificial Intelligence is influencing the way we live together in society? The new data dashboard of the Artificial Intelligence Opinion Monitor visualizes how people assess the use of AI....

00 Blog PhenoRob Cluster - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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Drones and robots for a sustainable agriculture of the future

PhenoRob ,

05. October 2022

In this blog post, the Cluster of Excellence “PhenoRob - Robotics and Phenotyping for Sustainable Crop Production” from the University of Bonn introduces itself and provides exciting insights into its research fields relating to Agriculture 4.0....

00 Blog Sachweh GaiaX - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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Gaia X: The cloud for AI development?

Timon Sachweh,

06. April 2022

Data is being collected like never before, but what to do with it? How can data protection be guaranteed while at the same time creating added value through data-driven algorithms? Gaia-X addresses all these questions....

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How to Evaluate Chat Quality Using Standard NLP Benchmarks

Rostislav Nedelchev,

02. March 2022

Until now, language model-based assessments of chatbots have only produced a score for overall quality, often neglecting the context of the dialogue. With models that are trained on GLUE tasks, this has come to an end....