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Open access Oct 2026

Bridging Machine Learning and Algorithmic Information Theory, Part VII: Algorithmic Information Kernels and Kernel Discrepancies on Countable Spaces

Compression-based dissimilarities such as the normalized compression distance are widely used, but direct exponentiation need not produce a positive semidefinite kernel. We develop a systematic interface between prefix algorithmic information theory and three kernel discrepancy methods on finite or countable spaces: ma...

B. Hamzi, Marcus Hutter, H. Owhadi · 0 citations
Open access Sep 2026

Category-Native Solomonoff Approximation: From Algorithmic Geometry to Kernels, Operators, and Induction

This theory-and-position paper argues that practical approximation must be category-native: one should first declare the mathematical category in which a computable shadow will live, then use that category’s native comparison functional, complexity code, and inductive object.

B. Hamzi, Marcus Hutter · 1 citation

Data-efficient Kernel Methods for Learning Hamiltonian Systems

This work proposes kernel-based methods for identifying and forecasting Hamiltonian systems directly from trajectory data, and provides a more general, problem-agnostic numerical framework that goes beyond Hamiltonian systems and can be used for data-driven learning of arbitrary dynamical systems.

Yasamin Jalalian, Mostafa Samir, B. Hamzi et al. · 4 citations

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