|
BOOK
Handbook of Cluster Analysis with Christian Hennig, Fionn Murtagh and Roberto Rocci
MANIFOLD LEARNING, GRAPH EMBEDDING
"Manifold
Learning: what, How and Why", with Hanyu Zhang (to appear)
in Annual Reviews of Statistics and Its Application, Volume
11, 2024.
"The consistency of Dictionary Based Manifold Learning", with Samson Koelle, Hanyu Zhang, and Vlad Murad, AISTATS 2024 (arXiv version here).
"Manifold Coordinates with Physical Meaning", with Samson Koelle, Hanyu Zhang, and Yu-Chia Chen, Journal of Machine Learning Research, 2022. (arXiv:1811.11891) and slides
"The decomposition of the higher-order homology embedding constructed from the k-Laplacian", with Yu-Chia Chen, NeurIPS 2021 (Oral)
"Tangent space least adaptive clustering", with Samson Koelle and James Buenfil, ICML 2021 Workshop on Unsupervised Reinforcement Learning
"Selecting the independent coordinates of manifolds with large aspect ratios", with Yu-Chia Chen, arXiv:1907.01651, NeurIPS 2019
"A regression approach for explaining manifold embedding coordinates", with Samson Koelle and Hanyu Zhang, arXiv:1811.11891, 2018
"Improved graph Laplacian via geometric self-consistency" with Dominique Perrault-Joncas and James McQueen NIPS 2017 slides
"Near isometry by Riemannian Relaxation" (and supplement) with James McQueen and Dominique Perrault-Joncas, NIPS 2016 slides
See how Riemannian Relaxation recovers the shape of a squished sphere (avi)
(mov)
"megaman: Manifold Learning with Millions of points" with James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang, and JMLR short version ""
"Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery" with Dominique Perrault-Joncas, (submitted) 2013 slides
"Directed Graph Embedding: an Algorithm based onContinuous Limits of Laplacian-type Operators" with Dominique Perrault-Joncas, NIPS 2011
"Manifold Learning for Real Data" tutorial lectures at the Fields Institute, 2022
Slides for Manifold Learning in the age of Big Data the SPPEXA Annual Plenary Meeting, 2019
Slides for Geometrically faithful non-linear dimension reduction (Is Manifold Learning for toy data only?) at the UC Davis Statistics Symposium, 2016
CLUSTERING
"Good (K-means) clusterings are unique (up to small perturbations)", Journal of Multivariate Analysis, 2018
"How to tell when a clustering is (approximately) correct using convex relaxations", NeurIPS 2018
"Spectral clustering", an introduction for the 2010's, published in this book
"How the initialization affects the stability of the k-means algorithm", with Sebastien Bubeck and Ulrike von Luxburg, ESAIM: Probability and Statistics 16, 436-452, 2012
"Local equivalences of distances between clusterings - A geometric perspective" Machine Learning Journal, 2011
"Clustering by weighted cuts in directed graphs" with William Pentney, presented at the 2007 SIAM Conference on Data Mining SDM 2007
"The uniqueness of a good clustering for K-means", presented at ICML 2006
An expanded and improved version
"Comparing clusterings -- an information based distance", Journal of Multivariate Analysis, 98, pp 873-895, 2007.
"Comparing subspace clusterings", with Anne Patrikainen, IEEE Transactions on Knowledge and Data Engineering (TKDE) 18(7),902-916
"Spectral clustering of biological sequence data", with William Pentney, AAAI, 2005
"Comparing clusterings -- an axiomatic view". Presented at ICML 2005.
"Unsupervised spectral learning", with Susan Shortreed, UAI 2005.
"Spectral clustering for Microsoft Netscan data", UW CSE-05-06-05 Technical report and CSSS paper no. 49, with Anne Patrikainen
"Regularized spectral learning", UW Statistics Technical Report 465, with Susan Shortreed and Liang Xu. Presented at AISTATS 2005
"Clustering by Intersection-Merging", UW Statistics Technical Report 451, with Qunhua Li
"A comparison of spectral clustering algorithms", UW CSE
Technical report 03-05-01, with Deepak Verma
"Multiway cuts and spectral clustering" with Liang Xu (submitted)
"Comparing clusterings" UW Statistics Technical Report
418 and COLT 03 (pdf)
"The multicut lemma" UW Statistics Technical Report 417
"A random walks view of spectral segmentation" Meila, M., Shi J., AISTATS 2001
"An Experimental Comparison of
Several Clustering and Initialization Methods" Meila, M., Heckerman D., Microsoft Research Technical
report MSR-TR-98-06.
, UAI 1998 and
Machine Learning Journal, 42:9--42, 2000.
NETWORKS
"Measuring the robustness of graph properties", with Yali Wan, arXiv:1901.09661, 2018.
"Graph clustering: block models and model free results" with Yali Wan , NIPS 2016 (to appear)
"Benchmarking recovery theorems for the Degree Corrected Stochastic Block Model" with Yali Wan, ISAIM 2016
"A class of network models recoverable by spectral clustering" with Yali Wan, NIPS 2015
PREFERENCES, RANKINGS AND INTRANSITIVITY (slides)
"Recursive Inversion Models for Permutations" with Chris Meek, NIPS 2014 (slides)
"Consensus ranking with signed permutations" with Raman Arora, AISTATS 2013
"Experiments with Kemeny ranking: what works when? with Alnur Ali, Mathematical Social Sciences 64,28-40, 2012
"Preferences in college applications -- a non-parametric Bayesian analysis of top-10 rankings" Alnur Ali, Marina Meila, Brendan Murphy, Harr Chen, NIPS Workshop on Computational Social Science and the Wisdom of Crowds, 2010 (slides)
"Dirichlet Process Mixtures of Generalized Mallows Models" Marina Meila and Harr Chen, UAI 2010, Catalina Island, CA.
"An exponential family model over infinite rankings by Marina Meila and Le Bao, Journal of Machine Learning Research, 10:3481--3518, 2010.
"Estimation and Clustering with Infinite Rankings" by Marina Meila and Le Bao, UAI 2008, Helsinki, Finland, and UW Statistics TR 529
"Clustering permutations by Exponential Blurring Mean-Shift", by Le Bao and Marina Meila, UW Statistics TR 524, 2007
"Consensus ranking under the exponential model", Marina Meila, Kapil Phadnis, Arthur Patterson and Jeff Bilmes, UAI 2007, Vancouver, BC and UW Statistics
TR 515
"Intransitivity in classification and choice", with Jeff Bilmes, UWEETR-2006-0021
GRAVIMETRIC INVERSION WITH SPARSITY CONSTRAINTS
"Gravimetric inversion by compressed sensing"
Marina Meila, Caren Marzban, Ulvi Yurtsever, IGARSS 2008
"Model free gravimetric inversion"
Hoyt Koepke, Marina Meila IGARSS 2009
GRAPHICAL PROBABILITY MODELS
"Learning Bayesian Network Structure using LP Relaxations"
Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila; AISTATS 2010
"Tractable
Bayesian Learning of Tree Belief Networks" by M. Meila and T. Jaakkola
Statistics and Computing, 26, pp 76-92, 2006
(or UAI 2000
version) (presentation)
"An
Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional
Sparse Data" by Marina Meila, AI Memo 1652, CBCL Paper 169. (presented at ICML-99)The
ICML version - condensed but more readable
"Learning with Mixtures of Trees" Marina Meila, Michael I. Jordan; Journal of Machine Learning Research, 1(Oct):1-48, 2000.
"A Domain Partitioning Approach
to Structure Learning for Graphical Models" by Meila, M., Jordan, M. I.; now part of my thesis.
"Triangulation by
continuous embedding" by Meila, M., Jordan, M. I., AI Memo
1605, CBCL Paper 146. It subsumes "An
Objective function for belief net triangulation" presented at
AISTATS-97 and "Triangulation by
continuous embedding" by Meila, M., Jordan, M. I. in Advances
in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan,
T. Petsche (eds.), MIT Press, 1997.
CLASSIFICATION
"Data centering in feature space" UW Statistics Technical Report
420, and short version presented at AISTATS
2003
"Intransitive likelihood ratio classifiers" by Jeff Bilmes, Gang Ji and Marina Meila, NIPS*2001
"Maximum Entropy
Discrimination" AI Memo 1668, by Jaakkola, T., Meila, M.,
Jebara, T., NIPS*1999 (see also the presentation)
MACHINE VISION
"Discriminating deformable shape classes" by S. Ruiz-Correa,
L. G. Shapiro, M. Meila, G. Berson, presented at NIPS 2003.
"A new paradigm for recognizing 3-D object shapes from range
data," by S. Ruiz-Correa, L. G. Shapiro, M. Meila, ICCV 2003, and a
long version with more experiments by S. Ruiz-Correa,
L. G. Shapiro, M. Meila, J. Cole, G. Berson, S. Capell, UW Technical
Report, 2003.
"A
new signature-based method for efficient 3-D object
recognition" by Salvador Ruiz-Correa, Linda Shapiro and Marina
Meila, CVPR 2000.
"A random walks view of spectral segmentation" Meila, M., Shi J., AISTATS 2001
"Learning segmentation by random walks" Meila, M., Shi J., NIPS 2000.
COMPUTATIONAL BIOLOGY
"IkB, NF-kB Regulation Model: Simulation Analysis of
Small Number of Molecules," by Anamika Sarkar, Marina Meila and Bob
Franza, EURASIP Journal on Bioinformatics and Systems Biology, vol.
2007, Article ID 25250, 2007.
PARTICLE FILTERS
"Real-time particle filters", Technical Report UW-CSE-02-07-01, with Cody Kwok and Dieter Fox, NIPS 2002 and IEEE Special Issue on Real-Time state estimation, 2004.
MIXTURES OF EXPERTS
"Markov Mixtures of Experts" by Meila, M., Jordan, M. I.,
in R. Murray-Smith and T. A. Johanssen (eds.) 'Multiple Model Approaches
to Nonlinear Modelling and Control', Taylor and Francis, 1996.
"Learning fine motion by Markov mixtures of experts" by Meila,
M., Jordan, M. I., in Advances in Neural Information Processing
Systems 8, D. Touretzky, M. C. Mozer and M. Hasselmo (eds.), MIT
Press, 1996 and its extended version Meila, M., Jordan, M. I. "Learning
fine motion by Markov mixtures of experts" AI Memo 1567, CBCL
Paper 133
Learning the parameters
of HMMs with auxilliary input by Meila, M. and Jordan, M. I. (1994) MIT Computational Cognitive Science
Tech. Report 9401
PHD THESIS
"Learning with
mixtures of trees", MIT Electrical Engineering and
Computer Science, 1999
|
