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07/24/2023
Tuning-free one-bit covariance estimation using data-driven dithering
We consider covariance estimation of any subgaussian distribution from f...
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01/11/2023
Plug-in Channel Estimation with Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems
As the array dimension of massive MIMO systems increases to unprecedente...
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04/08/2022
Fast metric embedding into the Hamming cube
We consider the problem of embedding a subset of ℝ^n into a low-dimensio...
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01/13/2022
Sharp estimates on random hyperplane tessellations
We study the problem of generating a hyperplane tessellation of an arbit...
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07/31/2021
The Separation Capacity of Random Neural Networks
Neural networks with random weights appear in a variety of machine learn...
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06/11/2021
New challenges in covariance estimation: multiple structures and coarse quantization
In this self-contained chapter, we revisit a fundamental problem of mult...
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04/02/2021
Covariance estimation under one-bit quantization
We consider the classical problem of estimating the covariance matrix of...
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09/17/2020
Binarized Johnson-Lindenstrauss embeddings
We consider the problem of encoding a set of vectors into a minimal numb...
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07/08/2020
Statistical post-processing of wind speed forecasts using convolutional neural networks
Current statistical post-processing methods for probabilistic weather fo...
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05/14/2020
Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs
We study sparse recovery with structured random measurement matrices hav...
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12/17/2018
Robust one-bit compressed sensing with partial circulant matrices
We present optimal sample complexity estimates for one-bit compressed se...
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05/23/2018
Robust one-bit compressed sensing with non-Gaussian measurements
We study memoryless one-bit compressed sensing with non-Gaussian measure...
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02/17/2014
Dimensionality reduction with subgaussian matrices: a unified theory
We present a theory for Euclidean dimensionality reduction with subgauss...
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11/11/2013