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20062026
most citedA self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania

6 citations · 19 across the 29 of their papers we have counts for

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math.PR2026

One-Cut Risk Profiles under Quadratic Loss: Discrete Convexity, Continuous Limits, and Higher Dimensions

Mihaela-Adriana Nistor, Ionel Popescu

In this note we study a two-regime representation of a loss random variable under quadratic error. For a finite law we compute exactly the change of the optimal risk when one atom…

math.PR2026

Non-Gaussianity of the Stagnation Law in Particle Swarm Optimization

Alexandra - Ionela Andriciuc, Ionel Popescu, David - Corneliu Turturean

We study one-dimensional particle swarm optimization during stagnation, with two fixed distinct attractors and equal independent uniform acceleration ranges. The position then sati…

math.PR2026

Rank-One Fluctuations in Averaging-Learning Dynamics

Ionel Popescu, Tushar Vaidya

We study averaging-learning dynamics without an exogenous ground truth: the reference signal is generated endogenously by the population. The dynamics combine a time-varying averag…

math.PR2022

From Monte Carlo to neural networks approximations of boundary value problems

Lucian Beznea, Iulian Cimpean, Oana Lupascu-Stamate +2

In this paper we study probabilistic and neural network approximations for solutions to Poisson equation subject to Holder data in general bounded domains of . We aim…

math.PR2021

Invariance principle of random projection for the norm

Juntao Duan, Ionel Popescu, Heinrich Matzinger

Johnson-Lindenstrauss guarantees certain topological structure is preserved under random projections when project high dimensional deterministic vectors to low dimensional vectors.…

math.PR2021

A product-CLT and its application in invariance principle of random projection

JunTao Duan, Ionel Popescu, Fan Zhou

Johnson-Lindenstrauss lemma states random projections can be used as a topology preserving embedding technique for fixed vectors. In this paper, we try to understand how random pro…