Author

Matthias Bogaert

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#machine learning Preprint Oct 2026

TMAML: Temporal Model-Agnostic Meta-Learning for Cold-Start Time Series Forecasting

Cold-start forecasting, the task of forecasting a time series with little to no historical data, is a common challenge. Addressing it requires approaches that learn quickly from few datapoints and leverage information from related series, typically through static covariates, to generalize well to unseen series. While s...

Wannes Janssens, Matthias Bogaert, D. van den Poel · 0 citations

Jul 2026

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

A multi-agent framework that reconciles factual grounding with ideological alignment by combining Supervised Fine-Tuning, Direct Preference Optimization, and Retrieval-Augmented Generation is presented, which yields a stable winner and ranking, and manifesto-anchored lineage reliably predicts real-world materialization...

Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel · arXiv.org · 0 citations