Juan Pablo Vigneaux

Currently, I am an Olga Taussky and John Todd Instructor in Mathematics at Caltech, mentored by Matilde Marcolli. We co-organize the Information, Geometry, and Physics Seminar.

Previously, I was a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences. In 2019, I obtained my Ph.D. in Mathematics at the University of Paris under the supervision of Daniel Bennequin.

I’m broadly interested in the concept of information. My work deals with:

Characterizations of information measures such as entropy (operational, algebraic, and topological characterizations).
Dimension theory: relationship between fractal dimensions and information dimension.
Probability measures with geometric structure, such as rectifiable measures and stratified measures.
Magnitude and diversity as categorical generalizations of cardinality and entropy, respectively.
Combinatorial (“microcanonical”) models for generalized entropies.
Optimal transport and functional inequalities.

selected publications

2023

Journal

Typicality for stratified measures

Juan Pablo Vigneaux

IEEE Transactions on Information Theory, 2023

Abs arXiv Website

We define \emphstratified measures on Euclidean space as convex combinations of rectifiable measures. They are possibly singular with respect to the Lebesgue measure and generalize discrete-continuous mixtures. A stratified measure ρcan thus be represented as \sum_i=1^k q_i \rho_i, where (q_1,..,q_k) is a probability vector and each \rho_i is absolutely continuous with respect to the m_i-Hausdorff measure \mu_i on a m_i-rectifiable set E_i (e.g. a smooth m_i-manifold if m_i>0 or a countable set if m_i=0). We introduce a set of strongly typical realizations of ρ^⊗n that occur with high probability; they are supported on a finite union of strata E_i_1\times ⋯\times E_i_n whose dimensions “per factor” concentrate around the \emphmean dimension \sum_i=1^k q_i m_i. For each n, an appropriate sum of Hausdorff measures on the different strata gives a notion of reference “volume”; the exponential growth rate of the typical set’s volume is quantified by Csiszar’s \emphgeneralized entropy of ρwith respect to μ=\sum_i=1^k \mu_i. This entropy satisfies a chain rule; the conditional term is related to the volume growth of the typical realizations in each stratum. Under suitable hypotheses, our notion of mean dimension coincides with Rényi’s information dimension when applied to stratified measures, but the generalized entropy used here differs from Rényi’s dimensional entropy.
Journal

A formula for the categorical magnitude in terms of the Moore-Penrose pseudoinverse

Stephanie Chen, and Juan Pablo Vigneaux

Bulletin of the Belgian Mathematical Society - Simon Stevin, 2023

To appear in Vol. 30, Issue 3

Abs arXiv

The magnitude of finite categories is a generalization of the Euler characteristic. It is defined using the coarse incidence algebra of rational-valued functions on the given finite category, and a distinguished element in this algebra: the Dirichlet zeta function. The incidence algebra may be identified with the algebra of n\times n matrices over the rational numbers, where n is the cardinality of the underlying object set. The Moore-Penrose pseudoinverse of a matrix is a generalization of the inverse; it exists and is unique for any given matrix over the complex numbers. In this article, we derive a new method for calculating the magnitude of a finite category, using the pseudoinverse of the matrix that corresponds to the zeta function. The magnitude equals the sum of the entries of this pseudoinverse.

2020

Journal

Information structures and their cohomology

Juan Pablo Vigneaux

Theory and Applications of Categories, 2020

Abs arXiv Website

We introduce the category of information structures, whose objects are suitable diagrams of measurable sets that encode the possible outputs of a given family of observables and their mutual relationships of refinement; they serve as mathematical models of contextuality in classical and quantum settings. Each information structure can be regarded as a ringed site with trivial topology; the structure ring is generated by the observables themselves and its multiplication corresponds to joint measurement. We extend Baudot and Bennequin’s definition of information cohomology to this setting, as a derived functor in the category of modules over the structure ring, and show explicitly that the bar construction gives a projective resolution in that category, recovering in this way the cochain complexes previously considered in the literature. Finally, we study the particular case of a one-parameter family of coefficients made of functions of probability distributions. The only 1-cocycles are Shannon entropy or Tsallis α-entropy, depending on the value of the parameter.

2019

Journal

Information theory with finite vector spaces

Juan Pablo Vigneaux

IEEE Transactions on Information Theory, 2019

Abs arXiv Slides Website

Whereas Shannon entropy is related to the growth rate of multinomial coefficients, we show that the quadratic entropy (Tsallis 2-entropy) is connected to their q-deformation; when q is a prime power, these q-multinomial coefficients count flags of finite vector spaces with prescribed length and dimensions. In particular, the q-binomial coefficients count vector subspaces of given dimension. We obtain in this way a combinatorial explanation for the nonadditivity of the quadratic entropy, which arises from a recursive counting of flags. We show that statistical systems whose configurations are described by flags provide a frequentist justification for the maximum entropy principle with Tsallis statistics. We introduce then a discrete-time stochastic process associated to the q-binomial probability distribution, that generates at time n a vector subspace of \mathbb F_q^n (here \mathbb F_q is the finite field of order q). The concentration of measure on certain "typical subspaces" allows us to extend the asymptotic equipartition property to this setting. The size of the typical set is quantified by the quadratic entropy. We discuss the applications to Shannon theory, particularly to source coding, when messages correspond to vector spaces.