HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
Learning under Concept Drift: an Overview
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.
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Compositional Sparsity as an Inductive Bias for Neural Architecture Design
HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
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Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.