While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output ...

We study influence maximization problems over social networks, in the presence of competition. Our focus is on diffusion processes within the family of threshold models. Motivated by the general lack of positive results establishing monotonicity and submodularity of the influence function for threshold models, we introduce a general class of switching-selection threshold models where the switch...

Abstract. The area under the curve (AUC) measures such as the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPR) are known to be more appropriate than the error rate, especially, for imbalanced data sets. There are several algorithms to optimize AUC measures instead of minimizing the error rate. However, this idea has not been fu...

The goal of the assignment is to use the Expectation Maximization (EM) algorithm to estimate the parameters of a two-component Guassian Mixture in two dimensions. This involves estimating the mean vector μk and covariance matrix Σk for both distributions as well as the mixing coefficients (or prior probabilities) πk for each component k. EM works by first choosing an arbitrary parameter set. In...

We present an approach to reward maximiza-tion in a non-stationary mobile robot environment. The approach works within the realistic constraints of limited local sensing and limited a priori knowledge of the environment. It is based on the use of augmented Markov models (AMMs), a general modeling tool we have developed. AMMs are essentially Markov chains having additional statistics associated ...

We consider the classical problem of maximizing expected utility from terminal wealth. With the help of a martingale criterion explicit solutions are derived for power utility in a number of affine stochastic volatility models.

In probabilistic modeling, it is often useful to change the structure, or refactor, a model, so that it has a different number of components, different parameter sharing, or other constraints. For example, we may wish to find a Gaussian mixture model (GMM) with fewer components that best approximates a reference model. Maximizing the likelihood of the refactored model under the reference model ...