Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure

Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data

The expressive power of Gaussian process (GP) models comes at a cost of poor scalability in the size of the data. To improve their scalability, this paper presents an overview of our recent progress in scaling up GP models for large spatiotemporally correlated data through parallelization on clusters of machines, online learning, and nonmyopic active sensing/learning.Singapore-MIT Alliance (Subaward Agreement No. 41)Singapore-MIT Alliance (Subaward Agreement No. 52

GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction

This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202