Compressed sensing

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Sound Field Reconstruction at Low Frequencies

Explores sound field reconstruction at low frequencies in rooms, emphasizing room modes, modal decomposition, and numerical validation.

Generalized Linear Models

Explores Generalized Linear Models, Bayesian methods, compressed sensing, and perception in high-dimensional statistics.

Parameter Estimation for Deformable Objects

By David Millard explores parameter estimation for deformable objects in robotic manipulation tasks, focusing on the challenges and solutions in dealing with complex dynamics and using finite element techniques.

Binary Spiked Matrix Estimation

Explores binary spiked matrix estimation, analyzing consistent equations and Bayesian estimators.

Projected Gradient Descent: Convergence and Optimization

Explores Projected Gradient Descent for optimization and control systems.

The Spike-Wigner model

Explores the Spike-Wigner model, low-rank matrix factorization, and Gaussian matrices.

Structured Sparsity: Atomic Norms and Convex Optimization

Explores atomic norms, convex optimization, and structured sparsity in mathematical data analysis.

Fenchel Conjugation: Basics and Applications

Introduces Fenchel conjugation, exploring its properties, examples, and applications in nonsmooth optimization problems and minimax formulations.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

Primal-dual Optimization: Methods and Applications

Explores primal-dual optimization methods, algorithms, convergence, and applications in nonconvex optimization and image deconvolution.