3 citations · 3 across the 6 of their papers we have counts for
6 papers
Well-conditioned iterative methods for large open quantum systems
Gaspard Beugnot, Paul Gregory, Rémi Robin +1
Markovian open quantum systems are well modeled by the Lindblad Master Equation (ME) , where is a linear (super…
Variational Perturbation Theory in Open Quantum Systems for Efficient Steady State Computation
André Melo, Gaspard Beugnot, Fabrizio Minganti
Determining the steady state of an open quantum system is crucial for characterizing quantum devices and studying various physical phenomena. Often, computing a single steady state…
GloptiNets: Scalable Non-Convex Optimization with Certificates
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
We present a novel approach to non-convex optimization with certificates, which handles smooth functions on the hypercube or on the torus. Unlike traditional methods that rely on a…
On the Benefits of Large Learning Rates for Kernel Methods
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms.…
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels. For least squares, it allows to derive various regularizati…
Improving Approximate Optimal Transport Distances using Quantization
Gaspard Beugnot, Aude Genevay, Kristjan Greenewald +1
Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden. Linear programming a…