2012/01/01 by Yuyang Wang, Roni Khardon
Computer Science · Engineering · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Artificial intelligence #Computer science #Control Systems and Identification #Function (biology) #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Inference #Machine learning #Set (abstract data type) #Task (project management) #cs.LG #stat.ML
paper · pdf · doi:10.1007/978-3-642-33460-3_51
Preliminary version appeared in ECML2012
openalex publication_date 2012/01/01 · arxiv created 2012/11/28 · arxiv updated 2012/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Multi-task learning models using Gaussian processes (GP) have been developed and successfully applied in various applications. The main difficulty with this approach is the computational cost of inference using the union of examples from all tasks. Therefore sparse solutions, that avoid using the entire data directly and instead use a set of informative "representatives" are desirable. The paper investigates this problem for the grouped mixed-effect GP model where each individual response is given by a fixed-effect, taken from one of a set of unknown groups, plus a random individual effect function that captures variations among individuals. Such models have been widely used in previous work but no sparse solutions have been developed. The paper presents the first sparse solution for such problems, showing how the sparse approximation can be obtained by maximizing a variational lower bound on the marginal likelihood, generalizing ideas from single-task Gaussian processes to handle the mixed-effect model as well as grouping. Experiments using artificial and real data validate the approach showing that it can recover the performance of inference with the full sample, that it outperforms baseline methods, and that it outperforms state of the art sparse solutions for other multi-task GP formulations.