Skip to content. | Skip to navigation

Personal tools

Theme for TIFR Centre For Applicable Mathematics, Bangalore

Navigation

You are here: Home / Events / Random fields indexed by amenable groups arise naturally in machine learning algorithms for structured and dependent data.

Random fields indexed by amenable groups arise naturally in machine learning algorithms for structured and dependent data.

Parthanil Roy (ISI Bangalore)
Speaker
Parthanil Roy (ISI Bangalore)
When May 31, 2022
from 04:00 PM to 05:00 PM
Where zoom talk
Add event to calendar vCal
iCal
Abstract

Random fields indexed by amenable groups arise naturally in machine learning algorithms for structured and dependent data. On the other hand, mixing properties of such fields are extremely important tools for investigating asymptotic properties of any method/algorithm in the context of space-time statistical inference. In this work, we find a necessary and sufficient condition for weak mixing of a left-stationary symmetric stable random field indexed by an amenable group in terms of its Rosinski representation. The main challenge is ergodic theoretic - more precisely, the unavailability of an ergodic theorem for nonsingular (but not necessarily measure preserving) actions of amenable groups even along a tempered Følner sequence. We remove this obstacle with the help of a truncation argument along with the seminal work of Lindenstrauss (2001) and Tempelman (2015), and finally applying the Maharam skew-product. This work extends the domain of application of the speaker’s previous paper connecting stable random fields with von Neumann algebras via the group measure space construction of Murray and von Neumann (1936). In particular, weak mixing has now become W*- rigid properties for stable random fields indexed by any amenable group, not just Z^d. We have also shown that many stable random fields generated by natural geometric actions of hyperbolic groups on various negatively curved spaces are actually mixing and hence weakly mixing.

This talk is based on an ongoing joint work with Mahan Mj (TIFR Mumbai) and Sourav Sarkar (University of Cambridge).
Filed under: