Proximal Methods for the Nonconvexized Proximal Product Surfaces


Proximal Methods for the Nonconvexized Proximal Product Surfaces – We present a method to provide a more realistic representation of the graph in a more accurate and more discriminative way when the graph is large. Using a simple yet effective technique called Grit-G, we demonstrate that by exploiting the presence of G, we can obtain good representation for graphs. From graph data, we find that the size of the graph often has little impact when the number of nodes is much larger than the number of nodes.

A very popular approach to modeling problems involving non-linear interactions involves the use of multiple variables of the same type, which are usually independent. Motivated by this model, we study the problem of univariate non-linear interaction, where interacting variables have to be mutually related with each other. The objective is to estimate the interactions of the two variables. We demonstrate that this problem can be successfully solved by various non-linear models. Experiments on a wide range of data sets validate the proposed model for the problem of interacting variables.

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Proximal Methods for the Nonconvexized Proximal Product Surfaces

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  • Constraint Programming Using Machine Learning: Theory, Practice, and Algorithm

    Learning Non-linear Structure from High-Order Interactions in Graphical ModelsA very popular approach to modeling problems involving non-linear interactions involves the use of multiple variables of the same type, which are usually independent. Motivated by this model, we study the problem of univariate non-linear interaction, where interacting variables have to be mutually related with each other. The objective is to estimate the interactions of the two variables. We demonstrate that this problem can be successfully solved by various non-linear models. Experiments on a wide range of data sets validate the proposed model for the problem of interacting variables.


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