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Juhani Kivimäki

2 accepted papers

2025

Confidence-based Estimators for Predictive Performance in Model Monitoring (Abstract Reprint)

IJCAI 2025

After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model’s predictions against ground truth labels. For this to be possible, the ground truth labels must be available relativ

Cited by 0SourcePDFScholar
2025

Estimating Model Performance Under Covariate Shift Without Labels

NeurIPS 2025poster

After deployment, machine learning models often experience performance degradation due to shifts in data distribution. It is challenging to assess post-deployment performance accurately when labels are missing or delayed. Existing proxy methods, such as data drift detection, fail to measure the effe…

Cited by 0SourceScholar