Marina Meila
16 papers · 2000–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (10) π Conference Polyglot (4)
πΊοΈ
Taxonomy Completionist
(10)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Keyword Trendsetter Combo
(5)
π±
Topic Pioneer
π
Keyword Champion
ποΈ
Keyword Collector
(78)
π
Trend Setter
π
Century Club
(16)
π₯
Unstoppable
(7)
π
Conference Pioneer
Conferences
NIPS (9)
AISTATS (3)
JMLR (3)
ICML (1)
Top co-authors
Keywords
manifold learning
(5)
dimensionality reduction
(4)
spectral method
(3)
spectral clustering
(2)
graph clustering
(2)
laplace-beltrami operator
(2)
stochastic block model
(2)
community detection
(2)
probabilistic model
(2)
manifold embedding
(2)
dimension reduction
(2)
graph laplacian
(2)
structure learning
(1)
k-means clustering
(1)
density estimation
(1)
metric learning
(1)
unsupervised learning
(1)
bayesian inference
(1)
graph representation
(1)
convex optimization
(1)
Papers
The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction
ICML 2025
Consistency of Dictionary-Based Manifold Learning
AISTATS 2024
Manifold Coordinates with Physical Meaning
JMLR 2022
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian
NIPS 2021
Selecting the independent coordinates of manifolds with large aspect ratios
NIPS 2019
How to tell when a clustering is (approximately) correct using convex relaxations
NIPS 2018
Improved Graph Laplacian via Geometric Self-Consistency
NIPS 2017
Graph Clustering: Block-models and model free results
NIPS 2016
Nearly Isometric Embedding by Relaxation
NIPS 2016
Megaman: Scalable Manifold Learning in Python
JMLR 2016
A class of network models recoverable by spectral clustering
NIPS 2015
Recursive Inversion Models for Permutations
NIPS 2014
Consensus Ranking with Signed Permutations
AISTATS 2013
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators
NIPS 2011
Learning Bayesian Network Structure using LP Relaxations
AISTATS 2010
Learning with Mixtures of Trees
JMLR 2000