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Bernhard Pfahringer

7 accepted papers

2026

Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the Past

ICML 2026poster

Memory is a critical component in replay-based continual learning (CL). Prior research has largely treated CL memory as a monolithic store of past data, focusing on how to select and store representative past examples. However, this perspective overlooks the higher-level memory architecture that gov…

Cited by 0SourceScholar
2025

Detection of Human and Machine-Authored Fake News in Urdu

ACL 2025long

The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditio…

2024

Online Isolation Forest

ICML 2024poster

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 context. Existing online anomaly detection methods also generally fail to address these constraints, resorting to periodi…

2024

Recurrent Concept Drifts on Data Streams

IJCAI 2024poster

In an era where machine learning permeates every facet of human existence, and data evolves incessantly, the application of machine learning models transcends mere data processing. It involves navigating constant changes exemplified by the phenomenon of concept drift, which often affects model perfo…

Cited by 6SourcePDFScholar
2024

Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme

IJCAI 2024poster

New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in…

Cited by 1SourcePDFScholar
2023

Survey on Online Streaming Continual Learning

IJCAI 2023poster

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning algorithm should adapt to input data distribution shifts without sacrificing accuracy. These distribution shifts are known as ”concept drifts” in the literature. SL provides many supervised, semi-supervised…

Cited by 31SourcePDFScholar
2022

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

NeurIPS 2022accept

Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to approximate the observed input distributions over time with a small memory and revisit them later to avoid forgetting. Des…