In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data. [pdf]

In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches. [pdf] [slides]

We consider the case when relationships are postulated to exist due to hidden common causes. We discuss how the resulting graphical model differs from Markov networks, and how it describes different types of real-world relational processes. A Bayesian nonparametric classification model is built upon this graphical representation and evaluated with several empirical studies. GOTO Ricardo Silva's homepage for [pdf], [data] and [code]

In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for directed and undirected networks connecting homogeneous or heterogenous nodes. The framework suggests an intimate connection between link prediction and transfer learning, which were traditionally two separate topics. [pdf]

Censored targets, such as the time to events in survival analysis, can generally be represented by intervals on the real line. In this paper, we propose a novel support vector technique (named SVCR) for regression on censored targets. Interestingly, this approach provides a general formulation for both standard regression and binary classification tasks. [pdf] [longer version]

Correlation between instances is often modelled via a kernel function using input attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and input attributes using Gaussian process techniques. This approach provides a novel non-parametric Bayesian framework with a data-dependent prior for supervised learning tasks. We also apply this framework to semi-supervised learning. Experimental results on several real world data sets verify the usefulness of this algorithm. [pdf]

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. [pdf]

We consider the problem of utilizing unlabeled data for Gaussian process inference. Using a geometrically motivated data-dependent prior, we propose a graph-based construction of semi-supervised Gaussian processes. We demonstrate this approach empirically on several classification problems. [pdf]

In this paper, we propose a new basis selection criterion for building sparse GP regression models that provides promising gains in accuracy as well as efficiency over previous methods. Our algorithm is much faster than that of Smola and Bartlett, while, in generalization it greatly outperforms the information gain approach proposed by Seeger et al, especially on the quality of predictive distributions. [ps] [code]

In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relations in the Bayesian framework. The generalized formulation is also applicable to tackle many multiclass problems. [ps] [code]

In this paper, we propose two new support vector formulations for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. [ps] [code]

In this paper, we present a probabilistic approach to ordinal regression in Gaussian processes. In the Bayesian framework of Gaussian processes, we propose a likelihood function for ordinal variables that is a generalization of the probit function. Two inference techniques, based on Laplace approximation and expectation propagation respectively, are applied for model selection. [ps] [code]

In this paper, we describe a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian framework. [pdf] [code]

In this paper, we present a graphical model that extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles for protein structure prediction. A novel parameterized model is proposed as the likelihood function for the SSMM. By incorporating the information from long range interactions in beta-sheets, this model is capable of carrying out inference on contact maps. [pdf] [webserver]

In this paper, we use soft insensitive loss function in likelihood evaluation, and describe a Bayesian framework in a stationary Gaussian process. Bayesian methods are used to implement model adaptation, while keeping the merits of support vector regression, such as quadratic programming and sparseness. Moreover, confidence interval is provided in prediction. [code]