🔬 Scientific Background & Acknowledgments¶
⬅️ README | 📚 THEORY | 👥 AUTHORS
This project came to life by implementing cutting-edge research in white-box machine learning. We would like to deeply acknowledge the following colleagues for the works, research teams, and foundational authors whose theoretical breakthroughs made the TAM framework possible:
📐 Foundational Theory & Inspiration (GAMs)¶
Simon N. Wood: We acknowledge his definitive foundational work on Generalized Additive Models (GAMs). His research mathematically underpins continuous structural regularization, Penalized Splines, and scale-invariant tensor products.
🏗️ Core WeaKL Framework (StaticTAM, AdaptiveTAM, HierarchicalTAM)¶
Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer, and Yannig Goude: Their 2025 research on forecasting time series with constraints serves as the theoretical foundation for the WeaKL algorithm.
Nathan Doumèche: His 2025 PhD thesis provides the central reference for the “Online WeaKL” approach, utilized as a tensorial alternative for managing structural breaks.
Nathan Doumèche, Francis Bach, Gérard Biau, and Claire Boyer: Their work on Physics-Informed Kernel Learning (PIKL) demonstrates that linear differential constraints can be reduced to a convex quadratic penalty, forming the basis of our exact physics module.
⚖️ Expert Aggregation (OperaTAM)¶
Pierre Gaillard and Yannig Goude: We acknowledge their foundational 2016
OPERApackage. Their theoretical frameworks for the MLpol and EWA algorithms are implemented natively for the OperaTAM module.
🌐 Extended Mathematical & Algorithmic Influences¶
This framework is also built upon the mathematical foundations laid by the researchers cited in the references.bib file.