We develop a tractable and flexible data-driven approach for incorporating side information into multi-stage stochastic programming. The proposed framework uses predictive machine learning methods (such as k-nearest neighbors, kernel regression, random forests) to weight the relative importance of various uncertainty sets in robust optimization formulation. Through novel measure concentration r...

Many real-world tasks include some kind of parameter estimation, i.e., determination a encoded in probability distribution. Often, such distributions arise from stochastic processes. For stationary process with temporal correlations, the random variables that constitute it are identically distributed but not independent. This is case, for instance, quantum continuous measurements. In this paper...

In this paper, we study a continuous time structural asset value model for two correlated firms using two-dimensional Brownian motion. We consider the situation of incomplete information, where information set available to market participants includes default each firm and periodic reports. In situation, becomes totally inaccessible stopping participants. The origin...

In this report, we study source coding problems with decoder side information (SI). Source coding problems with decoder side information are a special case of distributed source coding problems. We outline the relation between various distributed source coding problems in Table 1 with the help of Figure 1. For reference, we also include the standard single-source lossless and lossy compression ...