Add to ORKGMany problems in data science (e.g. machine learning, optimization and statistics) can be cast as loss minimization problems of the form min x∈R
Stochastic global optimization methods are methods for solving a global optimization prob-lem incorp...
This paper studies the problem of expected loss minimization given a data distribution that is depen...
NPS-55-90-14 Approved for public release; distribution is unlimited. Prepared for: T̂aval Postgradua...
International audienceIn a learning context, data distribution are usually unknown. Observation mode...
It was considered the possibility of optimization approach of the risk management problems based on ...
Most optimization problems in real life do not have accurate estimates of the problem parameters at ...
Optimization is essential in data science literature. The data science optimization studies all opti...
Ph.D.Electrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp:...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
and to lend or sell such copies for private, scholarly or scientific research purposes only. Where t...
Stochastic optimization refers to a collection of methods for minimizing or maximizing an objective ...
A simple optimization principle f (θ)g(θ) b κ Objective: min θ∈Θ f (θ) Principle called Majorization...
Package bmrm implements the ”Bundle Methods for Regularized Risk Mini-mization ” proposed by Teo et ...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
We consider an optimization problem in which some uncertain parmeters are replaced by random variabl...
Stochastic global optimization methods are methods for solving a global optimization prob-lem incorp...
This paper studies the problem of expected loss minimization given a data distribution that is depen...
NPS-55-90-14 Approved for public release; distribution is unlimited. Prepared for: T̂aval Postgradua...
International audienceIn a learning context, data distribution are usually unknown. Observation mode...
It was considered the possibility of optimization approach of the risk management problems based on ...
Most optimization problems in real life do not have accurate estimates of the problem parameters at ...
Optimization is essential in data science literature. The data science optimization studies all opti...
Ph.D.Electrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp:...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
and to lend or sell such copies for private, scholarly or scientific research purposes only. Where t...
Stochastic optimization refers to a collection of methods for minimizing or maximizing an objective ...
A simple optimization principle f (θ)g(θ) b κ Objective: min θ∈Θ f (θ) Principle called Majorization...
Package bmrm implements the ”Bundle Methods for Regularized Risk Mini-mization ” proposed by Teo et ...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
We consider an optimization problem in which some uncertain parmeters are replaced by random variabl...
Stochastic global optimization methods are methods for solving a global optimization prob-lem incorp...
This paper studies the problem of expected loss minimization given a data distribution that is depen...
NPS-55-90-14 Approved for public release; distribution is unlimited. Prepared for: T̂aval Postgradua...