Stochastic block model

Thresholds in Recovery of Sparse Stochastic Block Models by Allan Sly

COLLOQUIUM THRESHOLDS IN RECOVERY OF SPARSE STOCHASTIC BLOCK MODELS SPEAKER: Allan Sly ( Princeton University) DATE: Wed, 13 February 2019, 15:00 to 16:00 VENUE: Madhava Lecture Hall, ICTS Campus, Bangalore RESOURCES ABSTRACT The stochastic block model is an inhomogeneous random gra

From playlist ICTS Colloquia

Jocelyne Bion Nadal: Approximation and calibration of laws of solutions to stochastic...

Abstract: In many situations where stochastic modeling is used, one desires to choose the coefficients of a stochastic differential equation which represents the reality as simply as possible. For example one desires to approximate a diffusion model with high complexity coefficients by a m

From playlist Probability and Statistics

Davide Gabrielli : Macroscopic fluctuation theory / Particle systems, scaling limits and...

Abstract: In this first lecture I will introduce a class of stochastic microscopic models very useful as toy models in non equilibrium statistical mechanics. These are multi-component stochastic particle systems like the exclusion process, the zero range process and the KMP model. I will d

From playlist Mathematical Physics

Basic stochastic simulation b: Stochastic simulation algorithm

(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Specify system Determine duration until next event Exponentially distributed waiting times Determine what kind of reaction next event will be For more information, please search the internet for "stochastic simulation algorithm" or "kin

From playlist Probability, statistics, and stochastic processes

Alison Etheridge: Spatial population models (1/4)

Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev

From playlist Summer School on Stochastic modelling in the life sciences

Alison Etheridge: Spatial population models (4/4)

Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev

From playlist Summer School on Stochastic modelling in the life sciences

Alison Etheridge: Spatial population models (3/4)

Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev

From playlist Summer School on Stochastic modelling in the life sciences

Alison Etheridge: Spatial population models (2/4)

Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev

From playlist Summer School on Stochastic modelling in the life sciences

Willem van den Boom - Bayesian Learning of Graph Substructures

Willem van den Boom (National University of Singapore) presents "Bayesian Learning of Graph Substructures, 5 August 2022.

From playlist Statistics Across Campuses

Applied Math Perspectives on Stochastic Climate Models ( 2 ) - Andrew J. Majda

Lecture 2: Applied Math Perspectives on Stochastic Climate Models Abstract: We are entering a new era of Stochastic Climate Modeling. Such an approach is needed for several reasons: 1) to model crucial poorly represented processes in contemporary comprehensive computer models such as inte

From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology

Structured Regularization Summer School - É. Chouzenoux - 21/06/2017

Emilie Chouzenoux (Paris-Est): Majorization-Minimization Subspace Algorithms for Large Scale Data Processing Abstract: Recent developments in data processing drive the need for solving optimization problems with increasingly large sizes, stretching traditional techniques to their limits. N

From playlist Structured Regularization Summer School - 19-22/06/2017

Anirudh Sridhar (Princeton) -- Correlated Stochastic Block Models: Graph Matching Community Recovery

We consider the task of learning latent community structure from multiple correlated networks. First, we study the problem of learning the latent vertex correspondence between two edge-correlated stochastic block models, focusing on the regime where the average degree is logarithmic in the

From playlist Northeastern Probability Seminar 2021

Consistent Spectral Clustering of Network Block Models under Local Differential Privacy

A Google TechTalk, presented by Jonathan Hehir & Aleksandra Slavkovic, Penn State, at the 2021 Google Federated Learning and Analytics Workshop, Nov. 8-10, 2021. For more information about the workshop: https://events.withgoogle.com/2021-workshop-on-federated-learning-and-analytics/#conte

From playlist 2021 Google Workshop on Federated Learning and Analytics

Elias Khalil - Neur2SP: Neural Two-Stage Stochastic Programming - IPAM at UCLA

Recorded 02 March 2023. Elias Khalil of the University of Toronto presents "Neur2SP: Neural Two-Stage Stochastic Programming" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Stochastic Programming is a powerful modeling framework for decision-making under un

From playlist 2023 Artificial Intelligence and Discrete Optimization

Kevin Yang (Stanford) -- Kardar-Parisi-Zhang equation from some long-range particle systems

We discuss some new results on the Kardar-Parisi-Zhang equation as the continuum limit for height functions associated to long-range variations on ASEP and open ASEP. The method of proof is primarily based on localizing certain aspects of the dynamical approach in the energy solution theor

From playlist Columbia SPDE Seminar

Data Driven Methods for Complex Turbulent Systems ( 3 ) - Andrew J. Majda

Lecture 3: Data Driven Methods for Complex Turbulent Systems Abstract: An important contemporary research topic is the development of physics constrained data driven methods for complex, large-dimensional turbulent systems such as the equations for climate change science. Three new approa

From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology

Frequency-ranked data-driven stochastic modelling... - Chekroun - Workshop 2 - CEB T3 2019

Chekroun (UCLA, USA) / 13.11.2019 Frequency - ranked data - driven stochastic modelling, and applications ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ T

From playlist 2019 - T3 - The Mathematics of Climate and the Environment

Hybrid sparse stochastic processes and the resolution of (...) - Unser - Workshop 2 - CEB T1 2019

Michael Unser (EPFL) / 12.03.2019 Hybrid sparse stochastic processes and the resolution of linear inverse problems. Sparse stochastic processes are continuous-domain processes that are specified as solutions of linear stochastic differential equations driven by white Lévy noise. These p

From playlist 2019 - T1 - The Mathematics of Imaging

Laurent Massoulié : Non-backtracking spectrum of random graphs: community detection and ...

Abstract: A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges and can be used to count non-backtracking walks of a given length. It has been used recently in th

From playlist Combinatorics