Bayesian Nonparametric Modeling of Streaming Data Using the Kernel-fitting Technique – The problem of nonparametric regularization is a significant task in the area of probabilistic probabilistic programming (PPMP). Recent approaches to this problem have been mainly focused on the Bayesian framework. Bayesian regularization has attracted significant attention in probabilistic programming. In addition, the method and its advantages have been explored extensively. In this paper we provide a comprehensive set of tools for evaluating and exploring Bayesian regularization. The tool can be easily adapted as a part of a new framework for regularization. We show that it is an effective tool to guide regularization decisions, and that Bayesian regularization can be evaluated under various conditions, including a Bayesian probabilistic programming model, a natural oracle model, or a probabilistic probability distribution. Finally, we analyze the benefits and limitations of Bayesian regularization under different conditions—the setting where we perform the regularization and its limitations in practice.
We propose an ensemble model to estimate the importance of a ranking by combining the data of two different classes. We formulate the method as an optimization problem over the learning function, and then build an ensemble of multiple models. Our performance evaluation shows that the ensemble model achieves reasonable performance compared with the state-of-the-art ensemble learning methods for rank and ranking.
Towards Optimal Cooperative and Efficient Hardware Implementations
A Minimax Stochastic Loss Benchmark
Bayesian Nonparametric Modeling of Streaming Data Using the Kernel-fitting Technique
Learning and Inference from Large-Scale Non-stationary Global Change Models
Learning to rank with hidden measuresWe propose an ensemble model to estimate the importance of a ranking by combining the data of two different classes. We formulate the method as an optimization problem over the learning function, and then build an ensemble of multiple models. Our performance evaluation shows that the ensemble model achieves reasonable performance compared with the state-of-the-art ensemble learning methods for rank and ranking.