5 accepted papers
Multi-output Gaussian process (GP) regression has been widely used as a flexible nonparametric Bayesian model for predicting multiple correlated outputs given inputs. However, the cubic complexity in the sample size and the output dimensions for inverting the kernel matrix has limited their use in t…
We address the multi-task Gaussian process (GP) regression problem with the goal of decomposing input effects on outputs into components shared across or specific to tasks and samples. We propose a family of mixed-effects GPs, including doubly and translated mixed-effects GPs, that performs such a d…
In this paper, we address the problem of jointly estimating dependencies across samples and dependencies across multiple features, where each set of dependencies is modeled as an inverse covariance matrix. In particular, we study a matrix-variate Gaussian distribution with the Kronecker-sum of sampl…
We address the problem of learning the structure of a high-dimensional Gaussian graphical model conditional on covariates, when each sample belongs to groups at multiple levels of hierarchy. The existing statistical methods for learning covariate-conditioned Gaussian graphical models focused on lea…
This paper addresses the problem of scalable optimization for L1-regularized conditional Gaussian graphical models. Conditional Gaussian graphical models generalize the well-known Gaussian graphical models to conditional distributions to model the output network influenced by conditioning input var…