13 papers
Rethinking Tabular Foundation Models On Data Streams
Nilesh Verma, Daniel Nowak-Assis, Afonso Lourenço +4
Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this success, interest is growing in…
TuiML: Machine Learning for AI Agents
Nilesh Verma, Nick Lim, Albert Bifet +1
Machine-learning libraries such as Weka and scikit-learn were designed for human programmers. Language-model agents now use these same libraries by recalling APIs from memory and w…
AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors
Maher Boughdiri, Mounira Msahli, Albert Bifet
Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipula…
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…
PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models
Mustafa Hayri Bilgin, Mariam Barry, Albert Bifet +2
Efficiently updating Large Language Models (LLMs) with new or evolving factual knowledge remains a central challenge, as even parameter-efficient adaptation can erode previously ac…
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…
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…
Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning
Yibin Sun, Nick Lim, Guilherme Weigert Cassales +4
Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the det…
Online Isolation Forest
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer +2
The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming c…
Evaluation for Regression Analyses on Evolving Data Streams
Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer +1
The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardiz…
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…
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…
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer +1
This paper introduces a group of novel datasets representing real-time time-series and streaming data of energy prices in New Zealand, sourced from the Electricity Market Informati…