4 papers
In-context Learning of Evolving Data Streams with Tabular Foundational Models
Afonso Lourenço, João Gama, Eric P. Xing +1
State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the eme…
Bridging Streaming Continual Learning via In-Context Large Tabular Models
Afonso Lourenço, João Gama, Eric P. Xing +1
In streaming scenarios, models must learn continuously, adapting to concept drifts without erasing previously acquired knowledge. However, existing research communities address the…
DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices
Afonso Lourenço, João Rodrigo, João Gama +1
The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-tim…
On-device edge learning for IoT data streams: a survey
Afonso Lourenço, João Rodrigo, João Gama +1
This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart…