DeepAI AI Chat
Log In Sign Up

Near-Optimal Active Learning of Multi-Output Gaussian Processes

by   Yehong Zhang, et al.
National University of Singapore

This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. In contrast to existing works, our active learning problem involves selecting not just the most informative sampling locations to be observed but also the types of measurements at each selected location for minimizing the predictive uncertainty (i.e., posterior joint entropy) of a target phenomenon of interest given a sampling budget. Unfortunately, such an entropy criterion scales poorly in the numbers of candidate sampling locations and selected observations when optimized. To resolve this issue, we first exploit a structure common to sparse MOGP models for deriving a novel active learning criterion. Then, we exploit a relaxed form of submodularity property of our new criterion for devising a polynomial-time approximation algorithm that guarantees a constant-factor approximation of that achieved by the optimal set of selected observations. Empirical evaluation on real-world datasets shows that our proposed approach outperforms existing algorithms for active learning of MOGP and single-output GP models.


page 1

page 2

page 3

page 4


Safe Active Learning for Multi-Output Gaussian Processes

Multi-output regression problems are commonly encountered in science and...

Active Bayesian Optimization: Minimizing Minimizer Entropy

The ultimate goal of optimization is to find the minimizer of a target f...

Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Many real-world datasets can be represented in the form of a graph whose...

Active Learning for Single Neuron Models with Lipschitz Non-Linearities

We consider the problem of active learning for single neuron models, als...

GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model

Central to robot exploration and mapping is the task of persistent local...

Near-optimal irrevocable sample selection for periodic data streams with applications to marine robotics

We consider the task of monitoring spatiotemporal phenomena in real-time...

Simple and near-optimal algorithms for hidden stratification and multi-group learning

Multi-group agnostic learning is a formal learning criterion that is con...