Lectures related to Sparse dictionary learning

LASSO Regression: Sparse Signal Induction

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

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

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

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.

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.

Concise Signal Models and Compressive Sensing

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

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.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

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

Contrastive losses: Word2Vec and Skip-gram

Covers contrastive losses in Word2Vec and Skip-gram models, negative sampling, Noise Contrastive Estimation, and InfoNCE/CPC.

Spike Wigner Model

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

Recommender Systems: Matrix Factorization & Evaluation

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

Fenchel Conjugation: Basics and Applications

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

From Stochastic Gradient Descent to Non-Smooth Optimization

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

Cavity method and Approximate Message Passing

Explores the cavity method, Approximate Message Passing, and phase transitions in probabilistic models.

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.

Scientific Machine Learning: Applications and Algorithms

Explores scientific machine learning applications, challenges with sparse data, and physics-inspired algorithms to improve spectral methods.

Support Vector Machines: Hyperparameters and V-SVM

Explores SVM hyperparameters, V-SVM with variable p, and RVM.