Gaussian Process

Gaussian processes (GPs) are probabilistic models used for function approximation and uncertainty quantification, offering a powerful framework for various applications. Current research focuses on extending GPs' capabilities through novel architectures like deep GPs and hybrid models combining GPs with neural networks or other machine learning techniques, addressing scalability and computational efficiency challenges, particularly in high-dimensional or time-varying settings. These advancements are significantly impacting fields like robotics, control systems, and scientific modeling by providing robust, uncertainty-aware predictions and enabling more reliable decision-making in complex systems. The development of efficient algorithms and theoretical analyses further enhances the practical applicability and trustworthiness of GP-based methods.

Papers

February 28, 2023

February 23, 2023

Sharp Calibrated Gaussian Processes
Alexandre Capone, Geoff Pleiss, Sandra Hirche
Gaussian Process Quantile Forecast

February 21, 2023

Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression
Wenqi Cao, Gianluigi Pillonetto
Gaussian Process System Identification Impulse Response Bayesian Regularization Gaussian Approximation Independent Variable Collinearity

February 20, 2023

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick
Gaussian Process Fluid Dynamic Vector Field Ocean Current Ocean Data Buoy Detection

February 19, 2023

Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator
Dionissios T. Hristopulos
Gaussian Process Spectral Method Harmonic Oscillator Covariance Kernel

February 16, 2023

GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
Gaussian Process Power System Optimal Power Flow Economic Dispatch Optimization Tool

February 12, 2023

Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Zehao Niu, Mihai Anitescu, Jie Chen
Semi Supervised Learning Gaussian Process Graph Structured Data Graph Neural Graph Kernel Small Kernel Covariance Kernel

February 9, 2023

Gaussian Process-Gated Hierarchical Mixtures of Experts
Yuhao Liu, Marzieh Ajirak, Petar Djuric
Gaussian Process Variational Inference Expert Knowledge RSD Difference of Gaussian Gating Mechanism Hierarchical Mixture

February 8, 2023

Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Gaussian Process Multiple Instance Learning Instance Learning Deep Multiple Instance Gaussian Attention

February 7, 2023

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
Jonathan Parkinson, Wei Wang
Deep Learning Inherent Interpretability Gaussian Process Protein Sequence Small Molecule Linear Kernel Kernel Convolution

February 6, 2023

Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
Leonid Erlygin, Vladimir Zholobov, Valeriia Baklanova, Evgeny Sokolovskiy, Alexey Zaytsev
Gaussian Process Time Series Forecasting Uncertainty Estimation Surrogate Model Robust Model Accurate Uncertainty

February 3, 2023

January 30, 2023

January 26, 2023

Estimating Causal Effects using a Multi-task Deep Ensemble
Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David Carlson
Gaussian Process Causal Effect Causal Effect Estimation Multimodal Regression

Gaussian Process

Papers

Interactive Segmentation as Gaussian Process Classification

Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization

Sharp Calibrated Gaussian Processes

Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents

Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator

GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow

Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning

Gaussian Process-Gated Hierarchical Mixtures of Experts

Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust

Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds

Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery

Learning Choice Functions with Gaussian Processes

Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes

Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces

Estimating Causal Effects using a Multi-task Deep Ensemble