We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns αapproximate solutions for distributions over objectives, we compute a distribution over solutions that is α-approximate in the worst case. We show that deran...

We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations. Further, we use a novel approach to sampling support points, allowing faster construction of the acquisition function. This allows us to achieve optimization with lower overheads than previous ap...

Deformable image registration (DIR) has potential to enable novel approaches in radiotherapy (RT) such as dose accumulation, online adaptive planning, and response monitoring. Although DIR is predominantly formulated as a single-objective optimization problem, its inherent nature is multi-objective, i.e., there are multiple, conflicting objectives that need to be optimized simultaneously. A maj...

Many real-world optimization problems typically involve multiple (conflicting) objectives [...]

Most of the research on job shop scheduling problem are concerned with minimization of a single objective. However, the real world applications of job shop scheduling problems are involved in optimizing multiple objectives. Therefore, in recent years ant colony optimization algorithms have been proposed to solve job shop scheduling problems with multiple objectives. In this paper, some recent m...

A number of the practical scenarios relating to sensor networks are modeled as multiobjective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to choose one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/outp...

Robust policy optimization acknowledges that risk-aversion plays a vital role in real-world decision-making. When faced with uncertainty about the effects of actions, the policy that maximizes expected utility over the unknown parameters of the system may also carry with it a risk of intolerably poor performance. One might prefer to accept lower utility in expectation in order to avoid, or redu...