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05/31/2023
Dictionary Learning under Symmetries via Group Representations
The dictionary learning problem can be viewed as a data-driven process t...
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03/18/2022
Generative Principal Component Analysis
In this paper, we study the problem of principal component analysis with...
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01/20/2022
Learning with latent group sparsity via heat flow dynamics on networks
Group or cluster structure on explanatory variables in machine learning ...
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08/08/2021
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
In 1-bit compressive sensing, each measurement is quantized to a single ...
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06/29/2021
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Compressive phase retrieval is a popular variant of the standard compres...
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05/06/2021
Estimation and inference of signals via the stochastic geometry of spectrogram level sets
Spectrograms are fundamental tools in the detection, estimation and anal...
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11/12/2020
On a Variational Approximation based Empirical Likelihood ABC Method
Many scientifically well-motivated statistical models in natural, engine...
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08/07/2020
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
We propose a novel stochastic network model, called Fractal Gaussian Net...
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07/08/2020
Learning from DPPs via Sampling: Beyond HKPV and symmetry
Determinantal point processes (DPPs) have become a significant tool for ...
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02/17/2020
Transmission and navigation on disordered lattice networks, directed spanning forests and scaling limits
Stochastic networks based on random point sets as nodes have attracted c...
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10/03/2018