# Marina Meila-Predoviciu

#### Contact

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Preprints
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Manifold Coordinates with Physical Meaning
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Samson Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen
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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…

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

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How to sample connected $K$-partitions of a graph
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Marina Meila
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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…

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

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

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

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

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

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Tractable Bayesian Learning of Tree Belief Networks
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Marina Meila, Tommi S. Jaakkola
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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…

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

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

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Estimation and Clustering with Infinite Rankings
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Marina Meila, Le Bao
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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…

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