6 papers
Adjusted Scores for Discrete Langevin Algorithms
Armand Gissler, Saeed Saremi, Francis Bach
Sampling from discrete distributions is a ubiquitous task in machine learning, recently revisited by the emergence of discrete diffusion models. While Langevin algorithms constitut…
Irreducibility of nonsmooth state-space models with an application to CMA-ES
Armand Gissler, Shan-Conrad Wolf, Anne Auger +1
We analyze a stochastic process resulting from the normalization of states in the zeroth-order optimization method CMA-ES. On a specific class of minimization problems where the ob…
Asymptotic estimations of a perturbed symmetric eigenproblem
Armand Gissler, Anne Auger, Nikolaus Hansen
We study ill-conditioned positive definite matrices that are disturbed by the sum of rank-one matrices of a specific form. We provide estimates for the eigenvalues and eigenvec…
On the irreducibility and convergence of a class of nonsmooth nonlinear state-space models on manifolds and their applications to zeroth-order optimization
Armand Gissler, Alain Durmus, Anne Auger
In this paper, we analyze a large class of general nonlinear state-space models on a state-space X, defined by the recursion , , wh…
A note on the K-epigraph
Armand Gissler, Tim Hoheisel
We study the question as to when the closed convex hull of a K-convex map equals its K-epigraph. In particular, we shed light onto the smallest cone K such that a given map has con…
Scaling-invariant functions versus positively homogeneous functions
Cheikh Touré, Armand Gissler, Anne Auger +1
Scaling-invariant functions preserve the order of points when the points are scaled by the same positive scalar (with respect to a unique reference point). Composites of strictly m…