Add to ORKGIn this project a stochastic method for general purpose optimization and machine learning is described. The method is derived from basic information-theoretic principles and generalizes the popular Cross Entropy method. The effectiveness of the method as a tool for statistical modeling and Monte Carlo simulation is demonstrated with an application to the problems of density estimation and data modeling
Les principaux sujets étudiés dans cette thèse concernent le développement d'algorithmes stochastiqu...
In this paper, we provide a new algorithm for the problem of stochastic global optimization where on...
We study a class of stochastic programs where some of the elements in the objective function are ran...
One of the significant challenges when solving optimization problems is addressing possible inaccura...
International audienceModern signal processing (SP) methods rely very heavily on probability and sta...
Sampling-based Evolutionary Algorithms (EA) are of great use when dealing with a highly non-convex a...
This thesis connects the optimization of stochastic functions and information theory in a new way. N...
One of the significant challenges when solving optimization problems is ad-dressing possible inaccur...
International audienceWe derive a stochastic search procedure for parameter optimization from two fi...
This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sampl...
Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field a...
The cross entropy (CE) method is a model based search method to solve optimization problems where th...
This thesis provides a rigorous framework for the solution of stochastic elliptic partial differenti...
Stochastic optimization (SO) is extensively studied in various fields, such as control engineering, ...
International audienceWe study the optimization of a continuous function by its stochastic relaxatio...
Les principaux sujets étudiés dans cette thèse concernent le développement d'algorithmes stochastiqu...
In this paper, we provide a new algorithm for the problem of stochastic global optimization where on...
We study a class of stochastic programs where some of the elements in the objective function are ran...
One of the significant challenges when solving optimization problems is addressing possible inaccura...
International audienceModern signal processing (SP) methods rely very heavily on probability and sta...
Sampling-based Evolutionary Algorithms (EA) are of great use when dealing with a highly non-convex a...
This thesis connects the optimization of stochastic functions and information theory in a new way. N...
One of the significant challenges when solving optimization problems is ad-dressing possible inaccur...
International audienceWe derive a stochastic search procedure for parameter optimization from two fi...
This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sampl...
Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field a...
The cross entropy (CE) method is a model based search method to solve optimization problems where th...
This thesis provides a rigorous framework for the solution of stochastic elliptic partial differenti...
Stochastic optimization (SO) is extensively studied in various fields, such as control engineering, ...
International audienceWe study the optimization of a continuous function by its stochastic relaxatio...
Les principaux sujets étudiés dans cette thèse concernent le développement d'algorithmes stochastiqu...
In this paper, we provide a new algorithm for the problem of stochastic global optimization where on...
We study a class of stochastic programs where some of the elements in the objective function are ran...