
When Do Neural Networks Outperform Kernel Methods?
For a certain scaling of the initialization of stochastic gradient desce...
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The generalization error of maxmargin linear classifiers: Highdimensional asymptotics in the overparametrized regime
Modern machine learning models are often so complex that they achieve va...
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The estimation error of general first order methods
Modern largescale statistical models require to estimate thousands to m...
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The generalization error of random features regression: Precise asymptotics and double descent curve
Deep learning methods operate in regimes that defy the traditional stati...
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Inference in Graphical Models via Semidefinite Programming Hierarchies
Maximum A posteriori Probability (MAP) inference in graphical models amo...
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Learning Combinations of Sigmoids Through Gradient Estimation
We develop a new approach to learn the parameters of regression models w...
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Fundamental Limits of Weak Recovery with Applications to Phase Retrieval
In phase retrieval we want to recover an unknown signal x∈ C^d from n q...
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Nonnegative Matrix Factorization via Archetypal Analysis
Given a collection of data points, nonnegative matrix factorization (NM...
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Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
A number of statistical estimation problems can be addressed by semidefi...
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Spectral algorithms for tensor completion
In the tensor completion problem, one seeks to estimate a lowrank tenso...
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How Well Do Local Algorithms Solve Semidefinite Programs?
Several probabilistic models from highdimensional statistics and machin...
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The Landscape of Empirical Risk for Nonconvex Losses
Most highdimensional estimation and prediction methods propose to minim...
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Performance of a community detection algorithm based on semidefinite programming
The problem of detecting communities in a graph is maybe one the most st...
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Online Rules for Control of False Discovery Rate and False Discovery Exceedance
Multiple hypothesis testing is a core problem in statistical inference a...
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A Grothendiecktype inequality for local maxima
A large number of problems in optimization, machine learning, signal pro...
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Convergence rates of subsampled Newton methods
We consider the problem of minimizing a sum of n functions over a convex...
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Debiasing the Lasso: Optimal Sample Size for Gaussian Designs
Performing statistical inference in highdimension is an outstanding cha...
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Improved SumofSquares Lower Bounds for Hidden Clique and Hidden Submatrix Problems
Given a large data matrix A∈R^n× n, we consider the problem of determini...
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Finding One Community in a Sparse Graph
We consider a random sparse graph with bounded average degree, in which ...
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A statistical model for tensor PCA
We consider the Principal Component Analysis problem for large tensors o...
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Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques
These notes review six lectures given by Prof. Andrea Montanari on the t...
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Guess Who Rated This Movie: Identifying Users Through Subspace Clustering
It is often the case that, within an online recommender system, multiple...
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Sparse PCA via Covariance Thresholding
In sparse principal component analysis we are given noisy observations o...
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Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the ...
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Model Selection for HighDimensional Regression under the Generalized Irrepresentability Condition
In the highdimensional regression model a response variable is linearly...
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Hypothesis Testing in HighDimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
We consider linear regression in the highdimensional regime where the n...
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Linear Bandits in High Dimension and Recommendation Systems
A large number of online services provide automated recommendations to h...
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Accelerated TimeofFlight Mass Spectrometry
We study a simple modification to the conventional time of flight mass s...
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Identifying Users From Their Rating Patterns
This paper reports on our analysis of the 2011 CAMRa Challenge dataset (...
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On the tradeoff between complexity and correlation decay in structural learning algorithms
We consider the problem of learning the structure of Ising models (pairw...
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Information Theoretic Limits on Learning Stochastic Differential Equations
Consider the problem of learning the drift coefficient of a stochastic d...
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Regularization for Matrix Completion
We consider the problem of reconstructing a low rank matrix from noisy o...
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Which graphical models are difficult to learn?
We consider the problem of learning the structure of Ising models (pairw...
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Estimation of LowRank Matrices via Approximate Message Passing
Consider the problem of estimating a lowrank symmetric matrix when its ...
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The landscape of the spiked tensor model
We consider the problem of estimating a large rankone tensor u^⊗ k∈( R...
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An Instability in Variational Inference for Topic Models
Topic models are Bayesian models that are frequently used to capture the...
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On the Connection Between Learning TwoLayers Neural Networks and Tensor Decomposition
We establish connections between the problem of learning a twolayers ne...
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A Mean Field View of the Landscape of TwoLayers Neural Networks
Multilayer neural networks are among the most powerful models in machin...
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Contextual Stochastic Block Models
We provide the first information theoretic tight analysis for inference ...
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The threshold for SDPrefutation of random regular NAE3SAT
Unlike its cousin 3SAT, the NAE3SAT (notallequal3SAT) problem has th...
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Adapting to Unknown Noise Distribution in Matrix Denoising
We consider the problem of estimating an unknown matrix ∈^m× n, from obs...
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TAP free energy, spin glasses, and variational inference
We consider the SherringtonKirkpatrick model of spin glasses with ferro...
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The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning
The Lasso is a popular regression method for highdimensional problems i...
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Analysis of a TwoLayer Neural Network via Displacement Convexity
Fitting a function by using linear combinations of a large number N of `...
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Surprises in HighDimensional Ridgeless Least Squares Interpolation
Interpolators  estimators that achieve zero training error  have att...
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Meanfield theory of twolayers neural networks: dimensionfree bounds and kernel limit
We consider learning two layer neural networks using stochastic gradient...
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Fundamental Barriers to HighDimensional Regression with Convex Penalties
In highdimensional regression, we attempt to estimate a parameter vecto...
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On the computational tractability of statistical estimation on amenable graphs
We consider the problem of estimating a vector of discrete variables (θ_...
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Linearized twolayers neural networks in high dimension
We consider the problem of learning an unknown function f_ on the ddime...
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Limitations of Lazy Training of Twolayers Neural Networks
We study the supervised learning problem under either of the following t...
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