Open AccessBook
Stochastic Approximation Algorithms and Applications
Harold J. Kushner,George Yin +1 more
TLDR
Applications and issues application to learning, state dependent noise and queueing applications to signal processing and adaptive control mathematical background convergence with probability one, introduction weak convergence methods for general algorithms applications, proofs of convergence rate of convergence averaging of the iterates distributed/decentralized and asynchronous algorithms.Abstract:
Applications and issues application to learning, state dependent noise and queueing applications to signal processing and adaptive control mathematical background convergence with probability one - Martingale difference noise convergence with probability one - correlated noise weak convergence - introduction weak convergence methods for general algorithms applications - proofs of convergence rate of convergence averaging of the iterates distributed/decentralized and asynchronous algorithms.read more
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Variational Inference: A Review for Statisticians
TL;DR: For instance, mean-field variational inference as discussed by the authors approximates probability densities through optimization, which is used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling.
Journal ArticleDOI
Pegasos: primal estimated sub-gradient solver for SVM
TL;DR: A simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines, which is particularly well suited for large text classification problems, and demonstrates an order-of-magnitude speedup over previous SVM learning methods.
Posted Content
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
TL;DR: Bayesian optimization as mentioned in this paper employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function, which permits a utility-based selection of the next observation to make on the objective functions, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation, sampling areas likely to offer improvement over the current best observation.
Book
Martingale Methods in Financial Modelling
Marek Musiela,Marek Rutkowski +1 more
TL;DR: In this paper, the authors introduce the concept of discrete-time security markets for financial derivatives, and present a model of instantaneous forward rates and alternative market models for cross-currency derivatives.
Journal ArticleDOI
The statistical evaluation of social network dynamics
TL;DR: A class of statistical models is proposed for longitudinal network data that are continuous-time Markov chain models that can be implemented as simulation models and statistical procedures are proposed that are based on the method of moments.