Lectures related to Compressed sensing

LASSO Regression: Sparse Signal Induction

Explores LASSO regression for inducing sparsity in signals through gradient descent.

Concise Signal Models and Compressive Sensing

Explores concise signal models, compressive sensing, sparsity, atomic norms, and non-smooth minimization using subgradient descent.

Non-smoothness and Compressive Sensing

Explores non-smooth minimization, compressive sensing, sparse signal recovery, and simple representations using atomic sets and atoms.

Compressive Sensing

Explores compressive sensing theory and hardware implementations for signal reconstruction.

Stochastic Gradient Descent: Optimization Techniques

Explores stochastic gradient descent and non-smooth optimization techniques for sparsity and compressive sensing.

Linear Models: LASSO and AMP

Covers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.

From Stochastic Gradient Descent to Non-Smooth Optimization

Covers stochastic optimization, sparsity, and non-smooth minimization via subgradient descent.

Splines and Imaging: From Compressed Sensing to Deep Neural Nets

Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.

Stochastic Gradient Descent: Optimization Techniques

Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.

Functional Linear Regression: Sparse Estimation and Adaptive Methods

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

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.