STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection

1 March 2021

Papers citing "STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection"

9 / 9 papers shown

Title
When to retrain a machine learning model Regol Florence Schwinn Leo Sprague Kyle Coates Mark Markovich Thomas OffRL 56 0 0 20 May 2025
Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning Xiaoyu Yang Jie Lu En Yu 59 1 0 19 May 2025
SUDS: A Strategy for Unsupervised Drift Sampling Christofer Fellicious Lorenz Wendlinger Mario Gancarski Jelena Mitrović Michael Granitzer 123 0 0 05 Nov 2024
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems Tri Minh Triet Pham Karthikeyan Premkumar Mohamed Naili Jinqiu Yang AI4TS 52 0 0 11 Oct 2024
Online Drift Detection with Maximum Concept Discrepancy Ke Wan Yi Liang Susik Yoon 107 3 0 07 Jul 2024
A comprehensive analysis of concept drift locality in data streams Gabriel J. Aguiar Alberto Cano 52 9 0 10 Nov 2023
Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis Elias Werner Nishant Kumar Matthias Lieber Sunna Torge Stefan Gumhold W. Nagel 58 4 0 17 Apr 2023
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis Bardh Prenkaj Paola Velardi 57 6 0 13 Feb 2023
Online Feature Selection for Efficient Learning in Networked Systems Xiaoxuan Wang Rolf Stadler 46 0 0 15 Dec 2021