activity
20092026
most citedNext Order Asymptotics and Renormalized Energy for Riesz Interactions

93 citations · 187 across the 31 of their papers we have counts for

collaborators

38 papers

cs.LG2026

Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data

Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi +1

Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can…

stat.ML2025

On Universality of Deep Equivariant Networks

Marco Pacini, Mircea Petrache, Bruno Lepri +2

Universality results for equivariant neural networks remain rare. Those that do exist typically hold only in restrictive settings: either they rely on regular or higher-order tenso…

stat.ML2024

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks

Ashwin Samudre, Mircea Petrache, Brian D. Nord +1

There has been much recent interest in designing neural networks (NNs) with relaxed equivariance, which interpolate between exact equivariance and full flexibility for consistent p…

math.AP2024

Existence and uniqueness of Monge minimizers for a Multi-marginal Optimal Transport problem with intermolecular interactions cost

Augusto Gerolin, Mircea Petrache, Adolfo Vargas-Jimenez

We investigate a new multi-marginal optimal transport problem arising from a dissociation model in the Strong Interaction Limit of Density Functional Theory. In this short note, we…

math.OC2023

Optimal quantization with branched optimal transport distances

Paul Pegon, Mircea Petrache

We consider the problem of optimal approximation of a target measure by an atomic measure with atoms, in branched optimal transport distance. This is a new branched transport v…

math.PR2023

Effective recovery of Fourier spectra and spectral approximation by finite groups

Mircea Petrache, Rodolfo Viera

We prove a result on approximate recovery, with high probability, of subgroups of a finite nonabelian group from their random perturbations. We use this for ad-hoc sequences of…