This paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Modelassisted CMA-ES, the Robust Kriging Metamodel CMAES, and the Surrogate CMA-ES. In add...

This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of b...

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has ...

We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values.We describe a general method for solving such a problem, which alternates between optimization and worst-case analysis. With exact worst-case analysis, the method is shown to converge to a robust optimal point. ...

Article history: Received 12 September 2008 Received in revised form 23 December 2008 Accepted 2 February 2009

this paper considers the problem of stable limit cycles generating in a class of uncertain nonlinear systems which leads to stable oscillations in the system’s output.this is a wanted behavior in many practical engineering problems. for this purpose, first the equation of the desirable limit cycle is achieved according to shape, amplitude and frequency of the required output oscillations. then,...

This paper deals with convex optimization problems in the face of data uncertainty within the framework of robust optimization. It provides various properties and characterizations of the set of all robust optimal solutions of the problems. In particular, it provides generalizations of the constant subdifferential property as well as the constant Lagrangian property for solution sets of convex ...

Research in adversarial machine learning has shown how the performance of models can be seriously compromised by injecting even a small fraction poisoning points into training data. While effects on model accuracy such attacks have been widely studied, their potential other metrics remain to evaluated. In this work, we introduce an optimization framework for against algorithmic fairness, and de...

We study the empirical likelihood approach to construct confidence intervals for the optimal value and the optimality gap of a given solution, henceforth quantify the statistical uncertainty of sample average approximation, for optimization problems with expected value objectives and constraints where the underlying probability distributions are observed via limited data. This approach relies o...