47 citations · 47 across the 8 of their papers we have counts for
10 papers
From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616)
Alicia Colmenero-Fernández
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through emergent coordination and cloud…
A prism hierarchy of learning regimes in large linear autoencoders
Eugene Golikov, Yaroslav Gusev, Dmitry Yarotsky
Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable. It i…
Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions
Dmitry Yarotsky, Eugene Golikov, Yaroslav Gusev
We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learning problems. Our key innovation…
Corner Gradient Descent
Dmitry Yarotsky
We consider SGD-type optimization on infinite-dimensional quadratic problems with power law spectral conditions. It is well-known that on such problems deterministic GD has loss co…
SGD with memory: fundamental properties and stochastic acceleration
Dmitry Yarotsky, Maksim Velikanov
An important open problem is the theoretically feasible acceleration of mini-batch SGD-type algorithms on quadratic problems with power-law spectrum. In the non-stochastic setting,…
Generalization error of spectral algorithms
Maksim Velikanov, Maxim Panov, Dmitry Yarotsky
The asymptotically precise estimation of the generalization of kernel methods has recently received attention due to the parallels between neural networks and their associated kern…
Learnability of high-dimensional targets by two-parameter models and gradient flow
Dmitry Yarotsky
We explore the theoretical possibility of learning -dimensional targets with -parameter models by gradient flow (GF) when . Our main result shows that if the targets are…
Structure of universal formulas
Dmitry Yarotsky
By universal formulas we understand parameterized analytic expressions that have a fixed complexity, but nevertheless can approximate any continuous function on a compact set. Ther…
GTApprox: surrogate modeling for industrial design
Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev +4
We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approxim…
Ground states in relatively bounded quantum perturbations of classical lattice systems
D. A. Yarotsky
We consider ground states in relatively bounded quantum perturbations of classical lattice models. We prove general results about such perturbations (existence of the spectral gap,…