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Found 4 papers, 1 papers with code
Expert-Driven Monitoring of Operational ML Models
We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models.
Forming Ensembles at Runtime: A Machine Learning Approach
To tackle this problem, in this paper we propose to recast the ensemble formation problem as a classification problem and use machine learning to efficiently form ensembles at scale.
Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study
Results: Our results show that (i) established TD types, variations of them, and four new TD types (data, model, configuration, and ethics debt) are present in AI-based systems, (ii) 72 antipatterns are discussed in the literature, the majority related to data and model deficiencies, and (iii) 46 solutions have been proposed, either to address specific TD types, antipatterns, or TD in general.
Decentralized Optimization of Vehicle Route Planning -- A Cross-City Comparative Study
Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router.
