arXiv Machine Learning August 5, 2026 By Swapn Shah, Keith Burghardt

NOMADD: Numerical Optimization of Models Adapting to Data Drift

Read the original on arXiv Machine Learning →

arXiv:2608. 02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jun 9

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods

arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.

By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet

benchmarks

arXiv Machine Learning

Jun 2

Tempora: Characterising the Time-Contingent Utility of Online Test-Time Adaptation

arXiv:2602. 06136v2 Announce Type: replace Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled samples.

By Sudarshan Sreeram, Young D. Kwon, Cecilia Mascolo

arXiv AI

Jun 19

Learner-based Concept Drift Detection: Analysis and Evaluation

arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.

By Md Moman Ul Haque Khan, Samira Sadaoui

arXiv Machine Learning

Aug 4

Logit-Origin Centering for Singleton Test-Time Adaptation

arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.

By Mayank Sharma, Rohit Kumar Mourya, Pratik Mazumder

llms computer-vision benchmarks

arXiv Machine Learning

5d ago

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.

By Jakub Pele\v{s}ka, Gustav \v{S}\'ir

fine-tuning benchmarks

arXiv Machine Learning

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TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.

By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt

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A Framework for Evaluating and Benchmarking Concept Drift Detection Methods

Tempora: Characterising the Time-Contingent Utility of Online Test-Time Adaptation

Learner-based Concept Drift Detection: Analysis and Evaluation

Logit-Origin Centering for Singleton Test-Time Adaptation

Incremental Evaluation and Training in Relational Deep Learning

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks