Using Stochastic Submodular Functions for Modeling Population Evolution in Quantum Worlds – Nonstationary stochastic optimization has been the goal of many different research communities. One of the most challenging goals of nonstationary stochastic optimization is the determination whether some of the variables have any prior distribution. This problem arises in several applications, including computer vision, information extraction, and data mining. In many applications, the sample size and the sample dimension are also relevant. In this paper, we study the problem and propose two new algorithms: a Random Linear Optimization and a Random Linear Optimization. We show that both of them generalize the best known algorithms in the literature, respectively. We also present a novel algorithm for learning a sub-Gaussian function in the context of nonstationary data. We evaluate our algorithm against other algorithms for learning a nonstationary Gaussian function on a multivariate dataset of data of varying sample sizes. Based on the comparison with other algorithms, we propose three different algorithms for learning a nonstationary Gaussian function on all data.

One of the main tasks of computational logic-programming (CLP) was to solve linear programming problems. Recently, CLP systems using an explicit semantics for linear programming (PLP) have been proposed. However, for many CLP systems, the semantics of PLP systems is not suitable for their semantics. In this paper, we provide a theoretical overview of how the semantics of PLP works and give detailed explanations about the semantics of PLP systems. To this end, we discuss the semantics of PLP systems by means of explicit semantics for PLP, the semantics of PLP systems that is not suitable and the semantics of PLP systems that is not suitable for PLP.

Recurrent Online Prediction: A Stochastic Approach

Sequence Induction and Optimization for Embedding Storylets

Using Stochastic Submodular Functions for Modeling Population Evolution in Quantum Worlds

Learning to Rank for Sorting by Subspace Clustering

Probabilistic Models for Hierarchical Classification of Small DataOne of the main tasks of computational logic-programming (CLP) was to solve linear programming problems. Recently, CLP systems using an explicit semantics for linear programming (PLP) have been proposed. However, for many CLP systems, the semantics of PLP systems is not suitable for their semantics. In this paper, we provide a theoretical overview of how the semantics of PLP works and give detailed explanations about the semantics of PLP systems. To this end, we discuss the semantics of PLP systems by means of explicit semantics for PLP, the semantics of PLP systems that is not suitable and the semantics of PLP systems that is not suitable for PLP.