barycentric model in the space of probability measures. Moreover, a machine learning type procedure for model selection, based in …

general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the … routines then build solutions to the original problem.In our previous indirect GAs, learning is implicit and is restricted to … needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning …

This paper will give a brief overview of the work of introducing machine learning intelligence in the Kineta e … techniques have been implemented in the system and evaluated. As of late we have investigated the use of manifold learning; a …

to learn from price innovations, but this adverse effect can be circumvented by learning from a collection of assets that …

the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by …We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in …