Andrea Montanari
35 papers · 2009–2024 · 6 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 15 separate collaboration groups - the largest holds only 31% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Andrea Montanari?
6 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Yash Deshpande, Murat A Erdogdu, José Pereira
11 papers
· 2010–2018
· NeurIPS, JMLR, COLT
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Contextual Stochastic Block Models
NeurIPS 2018
Inference in Graphical Models via Semidefinite Programming Hierarchies
NeurIPS 2017
Sparse PCA via Covariance Thresholding
JMLR 2016
Convergence rates of sub-sampled Newton methods
NeurIPS 2015
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems
COLT 2015
A statistical model for tensor PCA
NeurIPS 2014
Cone-Constrained Principal Component Analysis
NeurIPS 2014
Sparse PCA via Covariance Thresholding
NeurIPS 2014
Estimating LASSO Risk and Noise Level
NeurIPS 2013
Learning Networks of Stochastic Differential Equations
NeurIPS 2010
The LASSO risk: asymptotic results and real world examples
NeurIPS 2010
9 papers whose co-authors appear nowhere else under this name
Scaling laws for learning with real and surrogate data
NeurIPS 2024
Towards a statistical theory of data selection under weak supervision
ICLR 2024
Compressing Tabular Data via Latent Variable Estimation
ICML 2023
High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks
COLT 2022
Underspecification Presents Challenges for Credibility in Modern Machine Learning
JMLR 2022
Universality of empirical risk minimization
COLT 2022
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
NeurIPS 2015
Learning Mixtures of Linear Classifiers
ICML 2014
Which graphical models are difficult to learn?
NeurIPS 2009