Stochastic optimization

Related lectures (32)

Page 1 of 4

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.

Delves into deep learning optimization, challenges, SGD variants, critical points, overparametrized networks, and adaptive methods.

Covers protein stability assessment using RFP and GFP, flow cytometry, mutagenesis, calcium reporters, and CRISPR-Cas9 experiments.

Explores adaptive gradient methods and their impact on optimization scenarios, including AdaGrad, ADAM, and RMSprop.

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

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

Covers the stochastic simulations course, G/G/1 queueing model, computational finance, statistics, physics, and Bayesian inference.

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Explores stochastic simulation, rare events, and the Crude Monte Carlo method, emphasizing the importance of thresholds and closed-form expressions.