served on my thesis committee, and I would also like to thank them for their contributions.

The problems of non-normality or functional relationships between variables may often be simplified by an appropriate transformation. However, the evidence for transformations may sometimes depend crucially on one or a few observations. Therefore, the purpose of the paper is to develop a method that will not be influenced by potential outliers during the process of data transformations. The con...

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In modern activities such as banking, homeland security, information transportation, telecommunication, etc., people work with large and high dimension data sets. But, the higher the dimension the higher the probability that outliers will be present in the data sets. The ability to detect outliers in high dimension multivariate data sets is a challenging task. In this circumstance, robust estim...

Robust Reinforcement Learning aims to derive an optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative. Our soft-robust framework is an attempt to overcome this issue. In this paper, we present a novel Soft-Robust ActorCritic algorithm (SR-AC). It lear...