Author

Daniel Schoess

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#machine learning Preprint Sep 2026

When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events

Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on which future periods contain activity. Standard benchmarks do not assess this. On five sparse datasets, we rank positions within forecast windows that contain both events a...

Daniel Schoess, F. von Wangenheim · 0 citations

#machine learning Preprint Sep 2026

I Don't Miss You, but I Do: Self-Explanation Faithfulness of Modality Missingness in Vision-Language Models

Vision-language models (VLMs) are increasingly used in settings where some input modalities may be unavailable, yet we know little about whether they can faithfully explain how such missing information affects their own predictions. We introduce an interventional protocol for evaluating self-explanations of modality dy...

Aydin Javadov, Daniel Schoess, F. von Wangenheim · 0 citations