Search Results for author:
Found 8 papers, 3 papers with code
Mutant fate in spatially structured populations on graphs: connecting models to experiments
Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix.
Evolution of cooperation in deme-structured populations on graphs
Models of spatially structured populations with one individual per node of a graph have shown that cooperation, modeled via the prisoner's dilemma, can be favored by natural selection.
Frequent asymmetric migrations suppress natural selection in spatially structured populations
Natural microbial populations often have complex spatial structures.
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula)
For sufficiently strongly convex problems, we show that the two-layer vector approximate message passing algorithm (2-MLVAMP) converges, where the convergence analysis is done by checking the stability of an equivalent dynamical system, which gives the result for such problems.
Asymptotic errors for convex penalized linear regression beyond Gaussian matrices
We consider the problem of learning a coefficient vector $x_{0}$ in $R^{N}$ from noisy linear observations $y=Fx_{0}+w$ in $R^{M}$ in the high dimensional limit $M, N$ to infinity with $\alpha=M/N$ fixed.
Learning performance in inverse Ising problems with sparse teacher couplings
These results are considered to be exact in the thermodynamic limit on locally tree-like networks, such as the regular random or Erd\H{o}s--R\'enyi graphs.
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning
Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher complexity.
On the Universality of Noiseless Linear Estimation with Respect to the Measurement Matrix
In a noiseless linear estimation problem, one aims to reconstruct a vector x* from the knowledge of its linear projections y=Phi x*.
