We describe an interesting application of the principle of local learning to density estimation. Locally weighted fitting of a Gaussian with a regularized full covariance matrix yields a density estimator which displays improved behavior in the case where much of the probability mass is...

We aim at modelling fat-tailed densities whose distributions are unknown but are potentially asymmetric. In this context, the standard normality assumption is not appropriate.In order to make as few distributional assumptions as possible, we use a non-parametric algorithm to model the center of...

We consider sequential data that is sampled from an unknown process, so that the data are not necessarily iid. We develop a measure of generalization for such data and we consider a recently proposed approach to optimizing hyper-parameters, based on the computation of the gradient of a model...

Multi-task learning is a process used to learn domain-specific bias. It consists in simultaneously training models on different tasks derived from the same domain and forcing them to exchange domain information. This transfer of knowledge is performed by imposing constraints on the parameters...

In this paper, we set the basis for learning a multitype assets portfolio management technique relying on no assumptions over the distributions of the financial data. The neural network based model tries to capture patterns in the evolution of the market. Furthermore, the model allows a...

Prior work on option pricing falls mostly in two categories: it either relies on strong distributional or economical assumptions, or it tries to mimic the Black-Scholes formula through statistical models, trained to fit today's market price based on information available today. The work...

We introduce an asset-allocation framework based on the active control of the value-at- risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with...

Metric-based methods, which use unlabeled data to detect gross differences in behavior away from the training points, have recently been introduced for model selection, often yielding very significant improvements over alternatives (including cross-validation). We introduce extensions that take...

This paper studies an out-of-sample statistic for time-series prediction that is analogous to the widely used R2 in-sample statistic. We propose and study methods to estimate the variance of this out-of-sample statistic. We suggest that the out-of-sample statistic is more robust to...

The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly...