Non-linear Multitask Learning with Deep Gaussian Processes
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes that capture within-task information and shared processes that capture across-task dependencies. We propose two different methods for segmenting the latent space: through hard coding shared and task-specific processes or through soft sharing with Automatic Relevance Determination kernels. We show that our formulation is able to improve the learning performance and transfer information between the tasks, outperforming other probabilistic multi-task learning models across real-world and benchmarking settings.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingGaussian ProcessesMulti-Task LearningSimilar Papers 제목 키워드 기반
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge in the form of constraints that require…
Gaussian ProcessesOn the relationship between multitask neural networks and multitask Gaussian Processes
Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN. In this work, we establish a formal conne…
Bayesian InferenceGaussian ProcessesExact and general decoupled solutions of the LMC Multitask Gaussian Process model
The Linear Model of Co-regionalization (LMC) is a very general model of multitask gaussian process for regression or classification. While its expressivity and conceptual simplicity are appealing, naive implementations h…
A dependent partition-valued process for multitask clustering and time evolving network modelling
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragme…
ClusteringGaussian ProcessesTime SeriesTime Series AnalysisA conditional one-output likelihood formulation for multitask Gaussian processes
Multitask Gaussian processes (MTGP) are the Gaussian process (GP) framework's solution for multioutput regression problems in which the $T$ elements of the regressors cannot be considered conditionally independent given …
Gaussian Processes