9 papers
Rashomon Alignment
Moisés Santos, Peter van der Putten, Bernhard Pfahringer +1
We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models. Existing functional similarity measures are distributional, quantifying differ…
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden +2
Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, p…
CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
Heitor Murilo Gomes, Anton Lee, Nuwan Gunasekara +9
CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured framework for real-time learning, su…
Policy Gradient with Adaptive Entropy Annealing for Continual Fine-Tuning
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank +1
Despite their success, large pretrained vision models remain vulnerable to catastrophic forgetting when adapted to new tasks in class-incremental settings. Parameter-efficient fine…
Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity
Léa Cassé, Bernhard Pfahringer, Albert Bifet +1
Near-term quantum machine learning must balance expressivity, optimization, and hardware constraints. We study quantum re-uploading units (QRUs) as compact circuits and compare the…
ARES: Anomaly Recognition Model For Edge Streams
Simone Mungari, Albert Bifet, Giuseppe Manco +1
Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time. Anomaly detection in t…