A Generalized Online Convex Optimization Framework for Stochastic Nonparametric Learning


A Generalized Online Convex Optimization Framework for Stochastic Nonparametric Learning – In this paper, we propose a framework for optimizing the Fisher information (FIS) in a data-driven setting with a novel stochastic function estimation strategy. A key to performing this strategy is to take a local approximation (localization factor), which estimates the loss function of the data in terms of a stochastic Fisher information. We show the effectiveness of our framework using two simulated instances where the Fisher information is not available and the data is sampled from a distribution with no information about its distribution. In addition, we propose a simple alternative to our framework in which the information from the Bayesian distribution is available only for the training and regression instances, hence the training criterion is not specified. Finally, we provide a simple algorithm that allows us to evaluate the Fisher information of a data in terms of the stochastic Fisher information.

We propose an efficient and robust optimization algorithm for training Bayesian networks. We show several theoretical bounds on the Bayesian framework. Our algorithm is competitive with the state-of-the-art approaches and outperforms them. Moreover, we show how other methods, including the ones used in the literature, can be improved.

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A Generalized Online Convex Optimization Framework for Stochastic Nonparametric Learning

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    Learning Graphs from Continuous Time and Space VariablesWe propose an efficient and robust optimization algorithm for training Bayesian networks. We show several theoretical bounds on the Bayesian framework. Our algorithm is competitive with the state-of-the-art approaches and outperforms them. Moreover, we show how other methods, including the ones used in the literature, can be improved.


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