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Graph Implementations for Nonsmooth Convex Programs
Convex optimization nonsmooth optimization optimization modeling languages semidefinite programming
2015/7/9
We describe graph implementations, a generic method for representing a convex function via its epigraph, described in a disciplined convex programming framework. This simple and natural idea allows a ...
Graph cluster randomization: network exposure to multiple universes
Graph cluster randomization network exposure multiple universes
2013/6/17
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the ove...
Statistical Analysis of Metric Graph Reconstruction
Metric Graph Filament Reconstruction Manifold Learning Minimax Esti-mation
2013/6/13
A metric graph is a 1-dimensional stratified metric space consisting of vertices and edges or loops glued together. Metric graphs can be naturally used to represent and model data that take the form o...
Recovering Graph-Structured Activations using Adaptive Compressive Measurements
Recovering Graph-Structured Activations Adaptive Compressive Measurements
2013/6/13
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activate...
We consider the testing and estimation of change-points -- locations where the distribution abruptly changes -- in a data sequence. A new approach, based on scan statistics utilizing graphs representi...
Seeded Graph Matching
Seeded Graph Matching
2012/11/21
Graph inference is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. Given two graphs on the same ...
Graph-Based Tests for Two-Sample Comparisons of Categorical Data
Two-sample tests categorical data discrete data minimum spanning trees graph-based tests contingency table.
2012/9/18
We study the problem of two-sample comparisons with categorical data when the contingency table is sparsely populated. Classical methods, such as the Pearson's
Chi-square test and the deviance test, ...
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Data Representation Nonnegtive Matrix Factorization Graph Regularization Multiple Kernel Learning.
2012/9/18
Nonnegative Matrix Factorization (NMF) has been contin-uously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank ...
Iterative graph cuts for image segmentation with a nonlinear statistical shape prior
Image segmentation MM graph cuts energy minimization statistical shape prior kernel density estimation
2012/9/18
Shape-based regularization has proven to be a useful method for delineating objects from the noisy im-ages encountered in many applications when one has prior
knowledge of the shape of the targeted o...
Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Iterative Conditional Fitting Gaussian Ancestral Graph Models
2012/9/19
Ancestral graph models, introduced by Richard-son and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property that is closed under...
High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families
Graphical model selection Ising models Greedy algorithms
2011/7/19
We consider the problem of high-dimensional Ising (graphical) model selection. We propose a simple algorithm for structure estimation based on the thresholding of the empirical conditional mutual info...
Moment based estimation of stochastic Kronecker graph parameters
Moment based estimation stochastic Kronecker graph
2011/7/5
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters.
Behavior of Graph Laplacians on Manifolds with Boundary
Graph Laplacians Behavior Manifolds Boundary
2011/6/21
In manifold learning, algorithms based on graph Laplacians constructed from data have received
considerable attention both in practical applications and theoretical analysis. In particular, the
conv...
Deconvolution of mixing time series on a graph
Deconvolution of mixing time latent time series state-space model
2011/6/17
In many applications we are interested in making
inference on latent time series from indirect
measurements, which are often low-dimensional
projections resulting from mixing or aggregation.
Posit...
Interpreting Graph Cuts as a Max-Product Algorithm
Max-Product Interpreting Graph binary variable models
2011/6/16
The maximum a posteriori (MAP) con guration
of binary variable models with submodular
graph-structured energy functions
can be found eciently and exactly by graph
cuts. Max-product belief propaga...