Nov 19, 2023 · Unsupervised visual domain adaptation: A deep max-margin gaussian process approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern ...

Sep 19, 2023 · If we're proper Bayesians, we determine probable functions by forming a posterior distribution over θ θ . Now consider how general this approach is. To ...

Feb 5, 2024 · This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs).

Oct 25, 2023 · Currently, k-nearest neighbor (kNN) approach is typically used as a prediction approach in operational forest inventories that require simultaneous predictions ...

Mar 27, 2024 · In this paper, we propose a novel approach, domain in- variant learning for Gaussian processes (DIL-GP), to itera- tively construct worst-case domains ...

Sep 15, 2023 · This approach is used by several machine learning libraries, notably scikit-learn, to provide an API that is consistent across all regression techniques in ...

Jul 5, 2023 · We demonstrate effectiveness of our approach on several applications involving Bayesian optimization, active learning, and continual learning. 2. Sequential ...

Feb 16, 2024 · Gaussian Processes in Machine Learning · Radial ... Time-series analysis is a statistical approach for analyzing data that has been structured through time.

Nov 15, 2023 · We propose a learning approach that models the latent radial in- teraction function as Gaussian processes, which can simultaneously fulfill two inference ...

Apr 18, 2024 · A Gaussian Process (GP) is a powerful tool in statistical modeling and machine learning that provides a probabilistic approach to forecasting in infinite- ...