1 citations · 1 across the 7 of their papers we have counts for
7 papers
Event-Chain Monte Carlo for Yang-Mills SU(N) lattice field theory I : Design and proof of concept
Benoît Blossier, Manon Michel, Yacob Ozdalkiran
We develop two implementations of the Event-Chain Monte Carlo (ECMC) algorithm for Yang-Mills lattice gauge theories with the Wilson action. These algorithms consi…
Quantifying Uncertainty In Wide Two-Layer Neural Networks: On The Law Of The Limiting Fluctuation Process
Arnaud Descours, Arnaud Guillin, Geoffrey Lacour +3
Uncertainty quantification in neural networks prediction is a main issue for usual applications. Our approach seeks at reducing computation costs by directly evaluating uncertainty…
Activity-driven clustering and many-body steady state of jamming run-and-tumble particles
Leo Hahn, Arnaud Guillin, Manon Michel
We exactly resolve the three-particle steady state of run-and-tumble particles with jamming interactions, providing the first microscopic description beyond two bodies. The invaria…
Bosonized one-dimensional quantum systems through enhanced event-chain Monte Carlo
Oscar Bouverot-Dupuis, Alberto Rosso, Manon Michel
We design an enhanced Event-Chain Monte Carlo algorithm to study 1D quantum dissipative systems, using their bosonized representation. Expressing the bosonized Hamiltonian as a pat…
Convergence of non-reversible Markov processes via lifting and flow Poincar{é} inequality
Andreas Eberle, Arnaud Guillin, Leo Hahn +2
We propose a general approach for quantitative convergence analysis of non-reversible Markov processes, based on the concept of second-order lifts and a variational approach to hyp…
Long-time analysis of a pair of on-lattice and continuous run-and-tumble particles with jamming interactions
Arnaud Guillin, Leo Hahn, Manon Michel
Run-and-Tumble Particles (RTPs) are a key model of active matter. They are characterized by alternating phases of linear travel and random direction reshuffling. By this dynamic be…
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin +3
In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regress…