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

-maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it … to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model …

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 …

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 …