Marina Meila

Affiliations:
  • University of Washington, Seattle, WA, USA
  • Massachusetts Institute of Technology, Cambridge, MA, USA (former)


According to our database1, Marina Meila authored at least 71 papers between 1995 and 2023.

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Bibliography

2023
Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space.
J. Comput. Phys., 2023

Manifold learning: what, how, and why.
CoRR, 2023

Dictionary-based Manifold Learning.
CoRR, 2023

The Parametric Stability of Well-separated Spherical Gaussian Mixtures.
CoRR, 2023

2022
Recursive inversion models for permutations.
Stat. Comput., 2022

Manifold Coordinates with Physical Meaning.
J. Mach. Learn. Res., 2022

To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online.
CoRR, 2022

2021
Distribution free optimality intervals for clustering.
CoRR, 2021

Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data.
CoRR, 2021

The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Guarantees for Hierarchical Clustering by the Sublevel Set method.
CoRR, 2020

2019
Good (K-means) clusterings are unique (up to small perturbations).
J. Multivar. Anal., 2019

Measuring the Robustness of Graph Properties.
CoRR, 2019

Selecting the independent coordinates of manifolds with large aspect ratios.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
A regression approach for explaining manifold embedding coordinates.
CoRR, 2018

How to sample connected K-partitions of a graph.
CoRR, 2018

How to tell when a clustering is (approximately) correct using convex relaxations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Improved Graph Laplacian via Geometric Self-Consistency.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Bayesian Non-Parametric Clustering of Ranking Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2016

Megaman: Scalable Manifold Learning in Python.
J. Mach. Learn. Res., 2016

megaman: Manifold Learning with Millions of points.
CoRR, 2016

Graph Clustering: Block-models and model free results.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Nearly Isometric Embedding by Relaxation.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Benchmarking recovery theorems for the DC-SBM.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2016

2015
A class of network models recoverable by spectral clustering.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Improved graph Laplacian via geometric self-consistency.
CoRR, 2014

Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs.
CoRR, 2014

Graph Sensitive Indices for Comparing Clusterings.
CoRR, 2014

Recursive Inversion Models for Permutations.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Consensus Ranking with Signed Permutations.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Experiments with Kemeny ranking: What works when?
Math. Soc. Sci., 2012

Local equivalences of distances between clusterings - a geometric perspective.
Mach. Learn., 2012

2011
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
An Exponential Model for Infinite Rankings.
J. Mach. Learn. Res., 2010

Learning Bayesian Network Structure using LP Relaxations.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Dirichlet Process Mixtures of Generalized Mallows Models.
Proceedings of the UAI 2010, 2010

Building a Job Lanscape from Directional Transition Data.
Proceedings of the Manifold Learning and Its Applications, 2010

2009
Tractable Search for Learning Exponential Models of Rankings.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008
Estimation and clustering with infinite rankings.
Proceedings of the UAI 2008, 2008

Gravimetric Detection by Compressed Sensing.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2008

2007
Preface.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

IκB, NF-κB Regulation Model: Simulation Analysis of Small Number of Molecules.
EURASIP J. Bioinform. Syst. Biol., 2007

Consensus ranking under the exponential model.
Proceedings of the UAI 2007, 2007

Clustering by weighted cuts in directed graphs.
Proceedings of the Seventh SIAM International Conference on Data Mining, 2007

2006
Comparing Subspace Clusterings.
IEEE Trans. Knowl. Data Eng., 2006

Tractable Bayesian learning of tree belief networks.
Stat. Comput., 2006

Symbolic Signatures for Deformable Shapes.
IEEE Trans. Pattern Anal. Mach. Intell., 2006

The uniqueness of a good optimum for K-means.
Proceedings of the Machine Learning, 2006

2005
Unsupervised Spectral Learning.
Proceedings of the UAI '05, 2005

Comparing clusterings: an axiomatic view.
Proceedings of the Machine Learning, 2005

Regularized spectral learning.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

Spectral Clustering of Biological Sequence Data.
Proceedings of the Proceedings, 2005

2004
Real-time particle filters.
Proc. IEEE, 2004

2003
Discriminating Deformable Shape Classes.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Adaptive real-time particle filters for robot localization.
Proceedings of the 2003 IEEE International Conference on Robotics and Automation, 2003

A New Paradigm for Recognizing 3-D Object Shapes from Range Data.
Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV 2003), 2003

Comparing Clusterings by the Variation of Information.
Proceedings of the Computational Learning Theory and Kernel Machines, 2003

Data centering in feature space.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2001
An Experimental Comparison of Model-Based Clustering Methods.
Mach. Learn., 2001

Intransitive Likelihood-Ratio Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

A New Signature-Based Method for Efficient 3-D Object Recognition.
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

A Random Walks View of Spectral Segmentation.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000
Learning with Mixtures of Trees.
J. Mach. Learn. Res., 2000

Learning Segmentation by Random Walks.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999
Learning with Mixtures of Trees.
PhD thesis, 1999

Maximum Entropy Discrimination.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data.
Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999

1998
An Experimental Comparison of Several Clustering and Initialization Methods.
Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

1997
Estimating Dependency Structure as a Hidden Variable.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

1996
Triangulation by Continuous Embedding.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995
Learning Fine Motion by Markov Mixtures of Experts.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995


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