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Survival and Neural Models for Private Equity Exit Prediction
Within the Private Equity (PE) market, the event of a private company undertaking an Initial Public Offering (IPO) is usually a very high-return one for the investors in the company.
Sparse $\ell_1$ and $\ell_2$ Center Classifiers
We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i. e., the globally optimal set of features is selected) and at a quasi-linear computational cost.
A Universal Approximation Result for Difference of log-sum-exp Neural Networks
We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node (LSE networks) is a smooth universal approximator of continuous functions over convex, compact sets.
Log-sum-exp neural networks and posynomial models for convex and log-log-convex data
Under a suitable exponential transformation, the class of LSET functions maps to a family of generalized posynomials GPOST, which we similarly show to be universal approximators for log-log-convex functions.
Convex Relaxations for Pose Graph Optimization with Outliers
Pose Graph Optimization involves the estimation of a set of poses from pairwise measurements and provides a formalization for many problems arising in mobile robotics and geometric computer vision.
