Author

M. Stojnic

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Preprint Aug 2026

Proving Lehner's formulas via RDT -- indefinite and asymmetric scenarios

The strong asymptotic freeness established in [28,53] allows for the study of norms of Gaussian matrix polynomials via corresponding free operator counterparts. For spectral edges of semicircular free counterparts to symmetric Kronecker-Gaussian matrices, Lehner in [39] determined closed-form analytical characterizatio...

M. Stojnic · 0 citations

Aug 2026

Exact capacity of the wide hidden layer treelike neural networks with generic activations.

Wide hidden layer networks are examined and it is uncovered that certain aspects of such difficulties miraculously disappear and certain aspects of such difficulties miraculously disappear.

M. Stojnic · Neural Networks · 1 citation

Jul 2026

Precise sample covariance spectral norm error - an RDT view

We study the sample covariance error of centered Gaussians. A remarkable breakthrough [66] established the correct error scaling order and explicitly revealed the critical role of both the effective rank and the true covariance spectrum. In this work, we move beyond scaling characterizations and determine the precise l...

M. Stojnic · arXiv.org · 2 citations