Lectures related to Sparse approximation

LASSO Regression: Sparse Signal Induction

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

Stochastic Gradient Descent: Optimization Techniques

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

Sound Field Reconstruction at Low Frequencies

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

Concise Signal Models and Compressive Sensing

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

L1 Regularization: Sparse Solutions and Dimensionality Reduction

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

Sparse Communication: Transformations and Applications

Explores the evolution from sparse modeling to sparse communication in neural networks for natural language processing tasks.

Projected Gradient Descent: Convergence and Optimization

Explores Projected Gradient Descent for optimization and control systems.

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.

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.

Spike Wigner Model

Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.

From Stochastic Gradient Descent to Non-Smooth Optimization

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

Recommender Systems: Matrix Factorization & Evaluation

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

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.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Recommender Systems: Matrix Factorization

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Primal-dual Optimization: Methods and Applications

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

Relevance Vector Machine: Addressing SVM Shortcomings

Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.