
Analysis of Drifting Features
The notion of concept drift refers to the phenomenon that the distributi...
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Counterfactual Explanations of Concept Drift
The notion of concept drift refers to the phenomenon that the distributi...
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Efficient drift parameter estimation for ergodic solutions of backward SDEs
We derive consistency and asymptotic normality results for quasimaximum...
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Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)ES with 1/5 Success Rule
This paper explores the use of the standard approach for proving runtime...
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Conceptdrifting Data Streams are Time Series; The Case for Continuous Adaptation
Learning from data streams is an increasingly important topic in data mi...
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Detecting model drift using polynomial relations
Machine learning (ML) models serve critical functions, such as classifyi...
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Unified Shapley Framework to Explain Prediction Drift
Predictions are the currency of a machine learning model, and to underst...
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A probability theoretic approach to drifting data in continuous time domains
The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. Albeit many attempts were made to deal with drift, formal notions of drift are applicationdependent and formulated in various degrees of abstraction and mathematical coherence. In this contribution, we provide a probability theoretical framework, that allows a formalization of drift in continuous time, which subsumes popular notions of drift. In particular, it sheds some light on common practice such as changepoint detection or machine learning methodologies in the presence of drift. It gives rise to a new characterization of drift in terms of stochastic dependency between data and time. This particularly intuitive formalization enables us to design a new, efficient drift detection method. Further, it induces a technology, to decompose observed data into a drifting and a nondrifting part.
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