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Related lectures (32)

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Decision Making Under Uncertainty

Explores decision-making under uncertainty, focusing on Kilian Schindler's posthumous PhD thesis on scalable stochastic optimization and scenario reduction.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Gradient Noise: Smooth and Nonsmooth Risks

Explores the impact of gradient noise on optimization algorithms, focusing on smooth and nonsmooth risk functions and the derivation of gradient noise moments.

Structures in Non-Convex Optimization

Explores non-convex optimization in deep learning, covering critical points, SGD convergence, saddle points, and adaptive gradient methods.

Memory-Efficient Adaptive Optimization

Explores memory-efficient adaptive optimization for large-scale learning and the challenges of memory overhead in training large models.

Sparse Regression and Convex Optimization

Explores sparse regression, convex optimization, and efficient solution algorithms in data analysis.

Variance Reduction Techniques

Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.

Mirror Prox: Optimization and Norms

Explores Mirror Prox optimization, argmin function, norms, and their practical applications in optimization problems.

Stochastic Simulation: Uncertainty Quantification

Explores uncertainty quantification using Quasi Monte Carlo methods and discrepancy measures for integral approximation and volume estimation.

Coordinate Descent: Efficient Optimization Techniques

Covers coordinate descent, a method for optimizing functions by updating one coordinate at a time.