Profit Driven Feature Selection for High Dimensional Regression via Determinantal Point Process Kernels


Profit Driven Feature Selection for High Dimensional Regression via Determinantal Point Process Kernels – We propose a novel and efficient Bayesian inference scheme based on the variational autoencoder model, where the posterior distribution is learned linearly over the data. The model is built out of a general convex optimization problem and the Bayesian optimizer is a variational autoencoder (VAE). It is formulated as a semi-supervised learning problem, where the VAE model is designed as an optimal convex function over a continuous function. We propose a multi-level variational autoencoder that is trained to learn the variational autoencoder simultaneously across the Bayes. The proposed method also generalizes well to a wide range of real-world datasets, including high dimensional datasets, as well as to synthetic data.

We propose a new nonparametric model for classification of time series, based on similarity between the temporal features. Specifically, we take the form of a novel family of time series, namely, linear time series, which are characterized by features of the observations in time. The proposed model is based on two novel contributions. First, we use the data to determine the model’s features, and infer their dependence within a particular domain. Second, a set of prior distributions for each data vector are extracted, and the data is used to infer the model’s features. This approach is an extension of the traditional approach of learning to learn, to discover the best possible predictor from data. We report results on the use of the proposed model to learn from time series.

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Profit Driven Feature Selection for High Dimensional Regression via Determinantal Point Process Kernels

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  • The Bayes Decision Boundary for Generalized Gaussian Processes

    Adaptive Canonical Correlation Analysis for Time-Series Prediction and LearningWe propose a new nonparametric model for classification of time series, based on similarity between the temporal features. Specifically, we take the form of a novel family of time series, namely, linear time series, which are characterized by features of the observations in time. The proposed model is based on two novel contributions. First, we use the data to determine the model’s features, and infer their dependence within a particular domain. Second, a set of prior distributions for each data vector are extracted, and the data is used to infer the model’s features. This approach is an extension of the traditional approach of learning to learn, to discover the best possible predictor from data. We report results on the use of the proposed model to learn from time series.


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