# Marina Meila-Predoviciu

#### Contact

**
Preprints
**

**
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…