Roman Vershynin

According to our database1, Roman Vershynin authored at least 56 papers between 2002 and 2024.

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Bibliography

2024
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data.
Found. Comput. Math., February, 2024

Differentially Private Synthetic High-dimensional Tabular Stream.
CoRR, 2024

Metric geometry of the privacy-utility tradeoff.
CoRR, 2024

Online Differentially Private Synthetic Data Generation.
CoRR, 2024

An Algorithm for Streaming Differentially Private Data.
CoRR, 2024

2023
The quarks of attention: Structure and capacity of neural attention building blocks.
Artif. Intell., June, 2023

AVIDA: An alternating method for visualizing and integrating data.
J. Comput. Sci., April, 2023

Privacy of Synthetic Data: A Statistical Framework.
IEEE Trans. Inf. Theory, 2023

Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval.
J. Mach. Learn. Res., 2023

Differentially private low-dimensional representation of high-dimensional data.
CoRR, 2023

Covariance loss, Szemeredi regularity, and differential privacy.
CoRR, 2023

Algorithmically Effective Differentially Private Synthetic Data.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data.
SIAM J. Math. Data Sci., September, 2022

AVIDA: Alternating method for Visualizing and Integrating Data.
CoRR, 2022

Private measures, random walks, and synthetic data.
CoRR, 2022

The Quarks of Attention.
CoRR, 2022

2021
A theory of capacity and sparse neural encoding.
Neural Networks, 2021

2020
Memory Capacity of Neural Networks with Threshold and Rectified Linear Unit Activations.
SIAM J. Math. Data Sci., 2020

Memory capacity of neural networks with threshold and ReLU activations.
CoRR, 2020

2019
Polynomial Threshold Functions, Hyperplane Arrangements, and Random Tensors.
SIAM J. Math. Data Sci., 2019

The capacity of feedforward neural networks.
Neural Networks, 2019

2018
Information-Theoretic Bounds and Phase Transitions in Clustering, Sparse PCA, and Submatrix Localization.
IEEE Trans. Inf. Theory, 2018

On Neuronal Capacity.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods.
Proceedings of the Conference On Learning Theory, 2018

2017
Concentration and regularization of random graphs.
Random Struct. Algorithms, 2017

Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees.
CoRR, 2017

2016
The Generalized Lasso With Non-Linear Observations.
IEEE Trans. Inf. Theory, 2016

Four lectures on probabilistic methods for data science.
CoRR, 2016

A simple tool for bounding the deviation of random matrices on geometric sets.
CoRR, 2016

Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization.
CoRR, 2016

2015
On the Effective Measure of Dimension in the Analysis Cosparse Model.
IEEE Trans. Inf. Theory, 2015

Concentration and regularization of random graphs.
CoRR, 2015

Sparse random graphs: regularization and concentration of the Laplacian.
CoRR, 2015

2014
Invertibility of symmetric random matrices.
Random Struct. Algorithms, 2014

Dimension Reduction by Random Hyperplane Tessellations.
Discret. Comput. Geom., 2014

Optimization via Low-rank Approximation, with Applications to Community Detection in Networks.
CoRR, 2014

Community detection in sparse networks via Grothendieck's inequality.
CoRR, 2014

2013
Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach.
IEEE Trans. Inf. Theory, 2013

2012
One-bit compressed sensing with non-Gaussian measurements
CoRR, 2012

Introduction to the non-asymptotic analysis of random matrices.
Proceedings of the Compressed Sensing, 2012

2011
One-bit compressed sensing by linear programming
CoRR, 2011

2010
Uncertainty principles and vector quantization.
IEEE Trans. Inf. Theory, 2010

Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit.
IEEE J. Sel. Top. Signal Process., 2010

Introduction to the non-asymptotic analysis of random matrices
CoRR, 2010

2009
Beyond Hirsch Conjecture: Walks on Random Polytopes and Smoothed Complexity of the Simplex Method.
SIAM J. Comput., 2009

Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit.
Found. Comput. Math., 2009

2008
Greedy signal recovery and uncertainty principles.
Proceedings of the Computational Imaging VI, 2008

Greedy signal recovery review.
Proceedings of the 42nd Asilomar Conference on Signals, Systems and Computers, 2008

2007
Sampling from large matrices: An approach through geometric functional analysis.
J. ACM, 2007

Some problems in asymptotic convex geometry and random matrices motivated by numerical algorithms
CoRR, 2007

One sketch for all: fast algorithms for compressed sensing.
Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 2007

2006
Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
CoRR, 2006

Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements.
Proceedings of the 40th Annual Conference on Information Sciences and Systems, 2006

A Randomized Solver for Linear Systems with Exponential Convergence.
Proceedings of the Approximation, 2006

2005
Error Correction via Linear Programming.
Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 2005

2002
Entropy, Combinatorial Dimensions and Random Averages.
Proceedings of the Computational Learning Theory, 2002


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