Marina Meila

Professor, University of Washington

Phone +1 206 543-7237
UW Box Number 354322
Homepage Personal Home Page 
ORCID iD  0000-0002-3989-8853 

Research: Graphical probability models, machine learning, algorithms, data mining.


Manifold Coordinates with Physical Meaning
Samson Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen
Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space. One of the aims of dimension reduction is to…

Guarantees for Hierarchical Clustering by the Sublevel Set method
Marina Meila
Meila (2018) introduces an optimization based method called the Sublevel Set method, to guarantee that a clustering is nearly optimal and "approximately…

How to sample connected $K$-partitions of a graph
Marina Meila
A connected undirected graph $G=(V,E)$ is given. This paper presents an algorithm that samples (non-uniformly) a $K$ partition $U_1,\ldots U_K$ of the graph…

megaman: Manifold Learning with Millions of points
James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are,…

An Experimental Comparison of Several Clustering and Initialization Methods
Marina Meila, David Heckerman
We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering…

Improved graph Laplacian via geometric self-consistency
Dominique Perrault-Joncas, Marina Meila
We address the problem of setting the kernel bandwidth used by Manifold Learning algorithms to construct the graph Laplacian. Exploiting the connection between…

Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
Dominique Perrault-Joncas, Marina Meila
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite…

Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery
Dominique Perraul-Joncas, Marina Meila
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the…

Tractable Bayesian Learning of Tree Belief Networks
Marina Meila, Tommi S. Jaakkola
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete…

Unsupervised spectral learning
Susan Shortreed, Marina Meila
In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is…

Consensus ranking under the exponential model
Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff A. Bilmes
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We…

Estimation and Clustering with Infinite Rankings
Marina Meila, Le Bao
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM…

Dirichlet Process Mixtures of Generalized Mallows Models
Marina Meila, Harr Chen
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior…