Author

G. Verbii

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Preprint Oct 2026

Beyond Wasserstein Proximity: Finding Better Transfer Sources

Source selection often favors similar data, yet marginal proximity need not identify the most useful pretraining source. We motivate a conditional alternative through domain-adaptation theory and a regression example in which broader source variation improves estimation despite increasing Wasserstein distance. We exami...

G. Verbii, Georgii Killinkarov, Vladimir Yafarov · 0 citations

#machine learning Preprint Aug 2026

Optimal Recovery Meets Bayesian Learning: Where Worst-Case Bounds Pay Off

Worst-case Optimal Recovery (OR) and Bayesian learning describe the same Gaussian-quadratic-Hilbert problems in two vocabularies. We sharpen the correspondence - the radius of information equals a nugget-optimized GP posterior variance and is attained by the posterior mean at a closed-form balance nugget - and measure,...

G. Verbii · 0 citations

Preprint Aug 2026

The Distributional View of Knowledge Distillation

A distributional view in which the teacher is represented not by a single softened output but by a family of multi-temperature views - marginals of the annealing path of its logits - and the student is trained against a geometry-aware aggregate of these views under an embedding-based ground cost is developed.

G. Verbii, Ju-Ho Lee · 0 citations

Preprint Aug 2026

One Inverse Step is a Convex Program: Bayes-Limit Calibration of Diffusion Inversion

One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local manifold geometry at the Bayes limit, strongly convex at the Bayes limit.

G. Verbii · 0 citations