Abstract not found

Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.

We propose a novel approach for probabilis-tic multi-view clustering that combines view-specific models to improve global coherence. Global incoherence is measured by the differ-ence between view-specific cluster assignment responsibilities. New cluster responsibilities are estimated by optimizing a cost function that maximizes per-view accuracy subject to a user-specified global coherence threshold. When combined with a parameter estimation step, this modified inference encourages the estimation of model parameters that agree between views. We show that the modified inference remains convex when global coherence constraints are given by the norm of the difference between the respon-sibilities of each model. In addition, the global correction is embarrassingly parallel between ex-amples. The proposed approach is evaluated on a synthetic dataset as well as real data showing im-proved performance as compared to strong base-line methods for multi-view clustering. 1.

Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values. Additional side information may consist of feature vectors specific to entities corresponding to the rows and/or columns of such a matrix. Further information may also be available in the form of interactions or hierarchies among entities along the same mode (axis). We propose a novel approach for modeling transposable data with missing interactions given additional side information. The interactions are modeled as noisy observations from a latent noise free matrix generated from a matrix-variate Gaussian process. The construction of row and column covariances using side information provides a flexible mechanism for specifying a-priori knowledge of the row and column correlations in the data. Further, the use of such a prior combined with the side information enables predictions for new rows and columns not observed in the training data. In this work, we combine the matrix-variate Gaussian process model with low rank constraints. The constrained Gaussian process approach is applied to the prediction of hidden associations between genes and diseases using a small set of observed associations as well as prior covariances induced by gene-gene interaction networks and disease ontologies. The proposed approach is also applied to recommender systems data which involves predicting the item ratings of users using known associations as well as prior covariances induced by social networks. We present experimental results that highlight the performance of constrained matrix-variate Gaussian process as compared to state of the art approaches in each domain. 1

Both max-margin and Bayesian methods have been extensively studied in multi-task learning, but have rarely been considered together. We present Bayesian max-margin multi-task learning, which conjoins the two schools of methods, thus allowing the discriminative max-margin methods to enjoy the great flexibility of Bayesian methods on incorporating rich prior information as well as performing nonparametric Bayesian feature learning with the latent dimensionality resolved from data. We develop Gibbs sampling algorithms by exploring data augmentation to deal with the non-smooth hinge loss. For nonparametric models, our algorithms do not need to make mean-field assumptions or truncated approximation. Empirical results demonstrate superior performance than competitors in both multi-task classification and regression.