Andrea Montanari
35 papers · 2009–2024 · 6 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (18) π§ Keyword Pioneer π Renaissance Researcher (6) π Interdisciplinary Bridge π Conference Polyglot (6)
π
Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(18)
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(5)
π±
Topic Pioneer
π¬
Deep Specialist
(16)
π
Keyword Champion
π₯
Mega-Team
(40)
π
Century Club
(35)
β‘
Prolific Year
(5)
π
Trend Setter
ποΈ
Keyword Collector
(62)
β
The Questioner
(2)
π₯
Unstoppable
(12)
Conferences
NIPS (18)
COLT (8)
JMLR (4)
ICML (3)
AISTATS (1)
ICLR (1)
Top co-authors
Research topics
Keywords
hypothesis testing
(4)
neural network
(4)
spectral method
(4)
high-dimensional regression
(3)
high-dimensional statistics
(3)
lasso regression
(3)
neural tangent kernel
(3)
information theory
(3)
principal component analysis
(3)
graphical model
(3)
network inference
(2)
sparse principal component analysis
(2)
statistical learning
(2)
matrix completion
(2)
model selection
(2)
stochastic gradient descent
(2)
tensor decomposition
(2)
community detection
(2)
graph clustering
(2)
semidefinite programming
(2)
Papers
Scaling laws for learning with real and surrogate data
NIPS 2024
Towards a statistical theory of data selection under weak supervision
ICLR 2024
Compressing Tabular Data via Latent Variable Estimation
ICML 2023
Underspecification Presents Challenges for Credibility in Modern Machine Learning
JMLR 2022
Universality of empirical risk minimization
COLT 2022
High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks
COLT 2022
Learning with invariances in random features and kernel models
COLT 2021
Streaming Belief Propagation for Community Detection
NIPS 2021
When Do Neural Networks Outperform Kernel Methods?
NIPS 2020
The estimation error of general first order methods
COLT 2020
An Instability in Variational Inference for Topic Models
ICML 2019
Limitations of Lazy Training of Two-layers Neural Network
NIPS 2019
On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition
AISTATS 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
COLT 2019
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval
COLT 2018
Contextual Stochastic Block Models
NIPS 2018
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
COLT 2017
Inference in Graphical Models via Semidefinite Programming Hierarchies
NIPS 2017
Sparse PCA via Covariance Thresholding
JMLR 2016
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems
COLT 2015
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
NIPS 2015
Convergence rates of sub-sampled Newton methods
NIPS 2015
Sparse PCA via Covariance Thresholding
NIPS 2014
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
JMLR 2014
Cone-Constrained Principal Component Analysis
NIPS 2014
A statistical model for tensor PCA
NIPS 2014
Learning Mixtures of Linear Classifiers
ICML 2014
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
NIPS 2013
Estimating LASSO Risk and Noise Level
NIPS 2013
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models
NIPS 2013
Matrix Completion from Noisy Entries
JMLR 2010
The LASSO risk: asymptotic results and real world examples
NIPS 2010
Learning Networks of Stochastic Differential Equations
NIPS 2010
Matrix Completion from Noisy Entries
NIPS 2009
Which graphical models are difficult to learn?
NIPS 2009