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ORCIDMarina Meila
Person information
- affiliation: University of Washington, Seattle, WA, USA
- affiliation (former): Massachusetts Institute of Technology, Cambridge, MA, USA
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2020 – today
- 2024
[c44]Samson J. Koelle, Hanyu Zhang, Octavian-Vlad Murad, Marina Meila:
Consistency of Dictionary-Based Manifold Learning. AISTATS 2024: 4348-4356- 2023
- [j16]Nikolaos Evangelou
, Felix Dietrich, Eliodoro Chiavazzo, Daniel Lehmberg, Marina Meila, Ioannis G. Kevrekidis:
Double Diffusion Maps and their Latent Harmonics for scientific computations in latent space. J. Comput. Phys. 485: 112072 (2023) - [i24]Hanyu Zhang, Marina Meila:
The Parametric Stability of Well-separated Spherical Gaussian Mixtures. CoRR abs/2302.00242 (2023) - [i23]Hanyu Zhang, Samson J. Koelle, Marina Meila:
Dictionary-based Manifold Learning. CoRR abs/2302.00263 (2023) - [i22]Marina Meila, Hanyu Zhang:
Manifold learning: what, how, and why. CoRR abs/2311.03757 (2023) - 2022
- [j15]Samson J. Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen:
Manifold Coordinates with Physical Meaning. J. Mach. Learn. Res. 23: 133:1-133:57 (2022) - [j14]Marina Meila, Annelise Wagner, Christopher Meek:
Recursive inversion models for permutations. Stat. Comput. 32(4): 54 (2022) - [i21]Charvi Rastogi, Ivan Stelmakh, Xinwei Shen, Marina Meila, Federico Echenique, Shuchi Chawla, Nihar B. Shah:
To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online. CoRR abs/2203.17259 (2022) - [i20]Nikolaos Evangelou, Felix Dietrich, Eliodoro Chiavazzo, Daniel Lehmberg, Marina Meila, Ioannis G. Kevrekidis:
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space. CoRR abs/2204.12536 (2022) - 2021
- [c43]Yu-Chia Chen, Marina Meila:
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian. NeurIPS 2021: 15695-15709 - [e3]Marina Meila, Tong Zhang:
Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research 139, PMLR 2021 [contents] - [i19]Yu-Chia Chen, Marina Meila, Ioannis G. Kevrekidis:
Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data. CoRR abs/2103.07626 (2021) - [i18]Yali Wan, Marina Meila:
A class of network models recoverable by spectral clustering. CoRR abs/2104.10347 (2021) - [i17]Yu-Chia Chen, Marina Meila:
The decomposition of the higher-order homology embedding constructed from the k-Laplacian. CoRR abs/2107.10970 (2021) - [i16]Marina Meila, Hanyu Zhang:
Distribution free optimality intervals for clustering. CoRR abs/2107.14442 (2021) - 2020
- [i15]Marina Meila:
Guarantees for Hierarchical Clustering by the Sublevel Set method. CoRR abs/2006.10274 (2020)
2010 – 2019
- 2019
- [j13]Marina Meila:
Good (K-means) clusterings are unique (up to small perturbations). J. Multivar. Anal. 173: 1-17 (2019) - [c42]Yu-Chia Chen, Marina Meila:
Selecting the independent coordinates of manifolds with large aspect ratios. NeurIPS 2019: 1086-1095 - [i14]Yali Wan, Marina Meila:
Measuring the Robustness of Graph Properties. CoRR abs/1901.09661 (2019) - [i13]Yu-Chia Chen, Marina Meila:
Selecting the independent coordinates of manifolds with large aspect ratios. CoRR abs/1907.01651 (2019) - 2018
- [c41]Marina Meila:
How to tell when a clustering is (approximately) correct using convex relaxations. NeurIPS 2018: 7418-7429 - [i12]Marina Meila:
How to sample connected K-partitions of a graph. CoRR abs/1808.00050 (2018) - [i11]Marina Meila, Samson J. Koelle, Hanyu Zhang:
A regression approach for explaining manifold embedding coordinates. CoRR abs/1811.11891 (2018) - 2017
- [c40]Dominique Joncas, Marina Meila, James McQueen:
Improved Graph Laplacian via Geometric Self-Consistency. NIPS 2017: 4457-4466 - 2016
- [j12]James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang:
Megaman: Scalable Manifold Learning in Python. J. Mach. Learn. Res. 17: 148:1-148:5 (2016) - [j11]Marina Meila, Harr Chen:
Bayesian Non-Parametric Clustering of Ranking Data. IEEE Trans. Pattern Anal. Mach. Intell. 38(11): 2156-2169 (2016) - [c39]Yali Wan, Marina Meila:
Benchmarking recovery theorems for the DC-SBM. ISAIM 2016 - [c38]Yali Wan, Marina Meila:
Graph Clustering: Block-models and model free results. NIPS 2016: 2478-2486 - [c37]James McQueen, Marina Meila, Dominique Joncas:
Nearly Isometric Embedding by Relaxation. NIPS 2016: 2631-2639 - [i10]James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang:
megaman: Manifold Learning with Millions of points. CoRR abs/1603.02763 (2016) - 2015
- [c36]Yali Wan, Marina Meila:
A class of network models recoverable by spectral clustering. NIPS 2015: 3285-3293 - [e2]Marina Meila, Tom Heskes:
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands. AUAI Press 2015, ISBN 978-0-9966431-0-8 [contents] - 2014
- [c35]Christopher Meek, Marina Meila:
Recursive Inversion Models for Permutations. NIPS 2014: 631-639 - [i9]Dominique C. Perrault-Joncas, Marina Meila:
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs. CoRR abs/1406.0013 (2014) - [i8]Dominique C. Perrault-Joncas, Marina Meila:
Improved graph Laplacian via geometric self-consistency. CoRR abs/1406.0118 (2014) - [i7]Zaeem Hussain, Marina Meila:
Graph Sensitive Indices for Comparing Clusterings. CoRR abs/1411.7582 (2014) - 2013
- [c34]Raman Arora, Marina Meila:
Consensus Ranking with Signed Permutations. AISTATS 2013: 117-125 - [i6]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian Learning of Tree Belief Networks. CoRR abs/1301.3875 (2013) - [i5]Marina Meila, David Heckerman:
An Experimental Comparison of Several Clustering and Initialization Methods. CoRR abs/1301.7401 (2013) - 2012
- [j10]Marina Meila:
Local equivalences of distances between clusterings - a geometric perspective. Mach. Learn. 86(3): 369-389 (2012) - [j9]Alnur Ali, Marina Meila:
Experiments with Kemeny ranking: What works when? Math. Soc. Sci. 64(1): 28-40 (2012) - [i4]Marina Meila, Harr Chen:
Dirichlet Process Mixtures of Generalized Mallows Models. CoRR abs/1203.3496 (2012) - [i3]Marina Meila, Le Bao:
Estimation and Clustering with Infinite Rankings. CoRR abs/1206.3270 (2012) - [i2]Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff A. Bilmes:
Consensus ranking under the exponential model. CoRR abs/1206.5265 (2012) - [i1]Susan M. Shortreed, Marina Meila:
Unsupervised spectral learning. CoRR abs/1207.1358 (2012) - 2011
- [c33]Dominique C. Perrault-Joncas, Marina Meila:
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators. NIPS 2011: 990-998 - 2010
- [j8]Marina Meila, Le Bao:
An Exponential Model for Infinite Rankings. J. Mach. Learn. Res. 11: 3481-3518 (2010) - [c32]Dominique C. Perrault-Joncas, Marina Meila, Marc Scott:
Building a Job Lanscape from Directional Transition Data. AAAI Fall Symposium: Manifold Learning and Its Applications 2010 - [c31]Marina Meila, Harr Chen:
Dirichlet Process Mixtures of Generalized Mallows Models. UAI 2010: 358-367 - [c30]Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila:
Learning Bayesian Network Structure using LP Relaxations. AISTATS 2010: 358-365
2000 – 2009
- 2009
- [c29]Bhushan Mandhani, Marina Meila:
Tractable Search for Learning Exponential Models of Rankings. AISTATS 2009: 392-399 - 2008
- [c28]Marina Meila, Caren Marzban, Ulvi Yurtsever:
Gravimetric Detection by Compressed Sensing. IGARSS (2) 2008: 193-196 - [c27]Marina Meila, Le Bao:
Estimation and clustering with infinite rankings. UAI 2008: 393-402 - 2007
- [j7]Anamika Sarkar, Marina Meila, B. Robert Franza:
IκB, NF-κB Regulation Model: Simulation Analysis of Small Number of Molecules. EURASIP J. Bioinform. Syst. Biol. 2007 (2007) - [c26]Marina Meila, William Pentney:
Clustering by weighted cuts in directed graphs. SDM 2007: 135-144 - [c25]Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff A. Bilmes:
Consensus ranking under the exponential model. UAI 2007: 285-294 - [c24]Marina Meila, Xiaotong Shen:
Preface. AISTATS 2007: 1-2 - [e1]Marina Meila, Xiaotong Shen:
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, March 21-24, 2007. JMLR Proceedings 2, JMLR.org 2007 [contents] - 2006
- [j6]Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila, Gabriel Berson, Michael L. Cunningham, Raymond W. Sze:
Symbolic Signatures for Deformable Shapes. IEEE Trans. Pattern Anal. Mach. Intell. 28(1): 75-90 (2006) - [j5]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian learning of tree belief networks. Stat. Comput. 16(1): 77-92 (2006) - [j4]Anne Patrikainen, Marina Meila:
Comparing Subspace Clusterings. IEEE Trans. Knowl. Data Eng. 18(7): 902-916 (2006) - [c23]Marina Meila:
The uniqueness of a good optimum for K-means. ICML 2006: 625-632 - 2005
- [c22]William Pentney, Marina Meila:
Spectral Clustering of Biological Sequence Data. AAAI 2005: 845-850 - [c21]Marina Meila, Susan M. Shortreed, Liang Xu:
Regularized spectral learning. AISTATS 2005: 230-237 - [c20]Marina Meila:
Comparing clusterings: an axiomatic view. ICML 2005: 577-584 - [c19]Susan M. Shortreed, Marina Meila:
Unsupervised Spectral Learning. UAI 2005: 534-541 - 2004
- [j3]Cody C. T. Kwok, Dieter Fox, Marina Meila:
Real-time particle filters. Proc. IEEE 92(3): 469-484 (2004) - 2003
- [c18]Marina Meila:
Data centering in feature space. AISTATS 2003: 209-216 - [c17]Marina Meila:
Comparing Clusterings by the Variation of Information. COLT 2003: 173-187 - [c16]Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila:
A New Paradigm for Recognizing 3-D Object Shapes from Range Data. ICCV 2003: 1126-1133 - [c15]Cody C. T. Kwok, Dieter Fox, Marina Meila:
Adaptive real-time particle filters for robot localization. ICRA 2003: 2836-2841 - [c14]Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila, Gabriel Berson:
Discriminating Deformable Shape Classes. NIPS 2003: 1491-1498 - 2002
- [c13]Cody C. T. Kwok, Dieter Fox, Marina Meila:
Real-Time Particle Filters. NIPS 2002: 1057-1064 - 2001
- [j2]Marina Meila, David Heckerman:
An Experimental Comparison of Model-Based Clustering Methods. Mach. Learn. 42(1/2): 9-29 (2001) - [c12]Marina Meila, Jianbo Shi:
A Random Walks View of Spectral Segmentation. AISTATS 2001: 203-208 - [c11]Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila:
A New Signature-Based Method for Efficient 3-D Object Recognition. CVPR (1) 2001: 769-776 - [c10]Jeff A. Bilmes, Gang Ji, Marina Meila:
Intransitive Likelihood-Ratio Classifiers. NIPS 2001: 1141-1148 - 2000
- [j1]Marina Meila, Michael I. Jordan:
Learning with Mixtures of Trees. J. Mach. Learn. Res. 1: 1-48 (2000) - [c9]Marina Meila, Jianbo Shi:
Learning Segmentation by Random Walks. NIPS 2000: 873-879 - [c8]Marina Meila, Tommi S. Jaakkola:
Tractable Bayesian Learning of Tree Belief Networks. UAI 2000: 380-388
1990 – 1999
- 1999
- [b1]Marina Meila-Predoviciu:
Learning with Mixtures of Trees. Massachusetts Institute of Technology, Cambridge, MA, USA, 1999 - [c7]Marina Meila:
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data. ICML 1999: 249-257 - [c6]Tommi S. Jaakkola, Marina Meila, Tony Jebara:
Maximum Entropy Discrimination. NIPS 1999: 470-476 - 1998
- [c5]Marina Meila, David Heckerman:
An Experimental Comparison of Several Clustering and Initialization Methods. UAI 1998: 386-395 - 1997
- [c4]Marina Meila, Michael I. Jordan:
An Objective Function for Belief Net Triangulation. AISTATS 1997: 355-362 - [c3]Marina Meila, Michael I. Jordan:
Estimating Dependency Structure as a Hidden Variable. NIPS 1997: 584-590 - 1996
- [c2]Marina Meila, Michael I. Jordan:
Triangulation by Continuous Embedding. NIPS 1996: 557-563 - 1995
- [c1]Marina Meila, Michael I. Jordan:
Learning Fine Motion by Markov Mixtures of Experts. NIPS 1995: 1003-1009
Coauthor Index
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last updated on 2024-09-13 00:44 CEST by the dblp team
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