Completed
Learning Linear Dynamical Systems with Hankel Nuclear Norm Regularization
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Low-Rank Models and Applications
Automatically move to the next video in the Classroom when playback concludes
- 1 Learning Linear Dynamical Systems with Hankel Nuclear Norm Regularization
- 2 Beyond Lazy Training for Over-parameterized Tensor Decomposition
- 3 A family of measurement matrices for generalized compressed sensing
- 4 Dual Principal Component Pursuit
- 5 Function space view of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
- 6 Data-driven dynamic interpolation and approximation
- 7 Tomographic Imaging with Model Uncertainty
- 8 Rigidity theory for Gaussian graphical models: the maximum likelihood threshold of a graph
- 9 Non-Separable Relaxations of a Class of Rank Penalties
- 10 Robust Low-Rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method
- 11 Computational Barriers to Estimation from Low-Degree Polynomials
- 12 PCA for High-Dimensional Heteroscedastic Data
- 13 PCA, Double Descent, and Gaussian Processes
- 14 Sample Optimal Algorithms for Low Rank Approximation of PSD and Distance Matrices
- 15 Imputing Missing Data with the Low-Rank Gaussian Copula
