160 citations · 224 across the 25 of their papers we have counts for
5 papers · 2 filters
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank +3
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 ap…
Linear TreeShap
Peng Yu, Chao Xu, Albert Bifet +1
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting…
Open challenges for Machine Learning based Early Decision-Making research
Alexis Bondu, Youssef Achenchabe, Albert Bifet +6
More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends…
Green Accelerated Hoeffding Tree
Eva Garcia-Martin, Albert Bifet, Niklas Lavesson +2
State-of-the-art machine learning solutions mainly focus on creating highly accurate models without constraints on hardware resources. Stream mining algorithms are designed to run…
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap…