As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …

Effectively storing carbon dioxide (CO 2) in geological formations synergizes with algal-
based removal technology, enhancing carbon capture efficiency, leveraging biological …

Shale swelling poses considerable challenges for companies involved in extended-reach
well drilling, particularly when it comes to maintaining wellbore stability. Despite the …

Recent work has extended the theoretical analysis of boosting algorithms to multiclass
problems and to online settings. However, the multiclass extension is in the batch setting …

Ensemble methods have been successfully used in the past to improve recommender
systems; however, they have never been studied with incremental recommendation …

How can we collect the most useful labels to learn a model selection policy, when presented
with arbitrary heterogeneous data streams? In this paper, we formulate this task as a …

In this paper, we study gradient boosting with distributed data streams over multi-agent
networks, and propose a distributed online gradient boosting algorithm. Considering limited …

We introduce the boosting notion of machine learning to the adaptive signal processing
literature. In our framework, we have several adaptive filtering algorithms, ie, the weak …

Abstract Machine learning has become an integral part of everyday life ranging from
applications in AI-powered search queries to (partial) autonomous driving. Many of the …

Zusammenfassung Moderne Techniken des maschinellen Lernens sind im Zeitalter der
Digitalisierung nicht mehr wegzudenken und ermöglichen die effiziente Analyse großer …