Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

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Rejection Ensembles with?Online Calibrationnt. One promising approach for optimizing resource consumption is rejection ensembles. Rejection ensembles combine a small model deployed to an edge device with a large model deployed in the cloud with a rejector tasked to determine the most suitable model for a given input. Due to its novelty, exis

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Interpetable Target-Feature Aggregation for?Multi-task Learning Based on?Bias-Variance Analysisformance. Previous works have proposed approaches to MTL that can be divided into feature learning, focused on the identification of a common feature representation, and task clustering, where similar tasks are grouped together. In this paper, we propose an MTL approach at the intersection between t

ALB

The Simpler The Better: An Entropy-Based Importance Metric to?Reduce Neural Networks’ Depthmpler downstream tasks, which do not necessarily require a large model’s complexity. Motivated by the awareness of the ever-growing AI environmental impact, we propose an efficiency strategy that leverages prior knowledge transferred by large models. Simple but effective, we propose a method relying

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Towards Few-Shot Self-explaining Graph Neural Networksy in critical domains such as medicine. A promising approach is the self-explaining method, which outputs explanations along with predictions. However, existing self-explaining models require a large amount of training data, rendering them unavailable in few-shot scenarios. To address this challenge

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