
On the Cryptographic Hardness of Learning Single Periodic Neurons
We show a simple reduction which demonstrates the cryptographic hardness...
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SelfRegularity of NonNegative Output Weights for Overparameterized TwoLayer Neural Networks
We consider the problem of finding a twolayer neural network with sigmo...
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It was "all" for "nothing": sharp phase transitions for noiseless discrete channels
We establish a phase transition known as the "allornothing" phenomenon...
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Optimal Private Median Estimation under Minimal Distributional Assumptions
We study the fundamental task of estimating the median of an underlying ...
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Group testing and local search: is there a computationalstatistical gap?
In this work we study the fundamental limits of approximate recovery in ...
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The AllorNothing Phenomenon in Sparse Tensor PCA
We study the statistical problem of estimating a rankone sparse tensor ...
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Free Energy Wells and Overlap Gap Property in Sparse PCA
We study a variant of the sparse PCA (principal component analysis) prob...
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Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena
In the context of neural network models, overparametrization refers to t...
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AllorNothing Phenomena: From SingleLetter to High Dimensions
We consider the linear regression problem of estimating a pdimensional ...
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Stationary Points of Shallow Neural Networks with Quadratic Activation Function
We consider the problem of learning shallow neural networks with quadrat...
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Inference in HighDimensional Linear Regression via Lattice Basis Reduction and Integer Relation Detection
We focus on the highdimensional linear regression problem, where the al...
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The Landscape of the Planted Clique Problem: Dense subgraphs and the Overlap Gap Property
In this paper we study the computationalstatistical gap of the planted ...
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The AllorNothing Phenomenon in Sparse Linear Regression
We study the problem of recovering a hidden binary ksparse pdimensiona...
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A simple bound on the BER of the MAP decoder for massive MIMO systems
The deployment of massive MIMO systems has revived much of the interest ...
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Private Algorithms Can Always Be Extended
We consider the following fundamental question on ϵdifferential privacy...
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Private Algorithms Can be Always Extended
We consider the following fundamental question on ϵdifferential privacy...
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Revealing Network Structure, Confidentially: Improved Rates for NodePrivate Graphon Estimation
Motivated by growing concerns over ensuring privacy on social networks, ...
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High Dimensional Linear Regression using Lattice Basis Reduction
We consider a high dimensional linear regression problem where the goal ...
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Sparse HighDimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm
We consider a sparse high dimensional regression model where the goal is...
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Orthogonal Machine Learning: Power and Limitations
Double machine learning provides √(n)consistent estimates of parameters...
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HighDimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition
We consider a sparse linear regression model Y=Xβ^*+W where X has a Gaus...
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