A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...

We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a ...

Japanese researchers develop an adaptive robot motion system that enables human-like grasping using minimal training data.

Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic ...

Researchers develop an adaptive motion system that allows robots to generate human-like movements with minimal data ...

Motivated by Gaussian tests for a time series, we are led to investigate the asymptotic behavior of the residual empirical processes of stochastic regression models. These models cover the fixed ...

By James McCaffrey 02/03/2025 The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as linear ...