Probabilistic Regression

Probabilistic regression aims to predict not just a single value, but a probability distribution representing the uncertainty associated with a prediction. Current research focuses on improving the accuracy and efficiency of these predictions, particularly for multivariate data and large datasets, using methods like Gaussian processes, gradient boosting machines, and Bayesian neural networks. These advancements are crucial for applications requiring reliable uncertainty quantification, such as medical diagnosis, robotics, and environmental modeling, where understanding the confidence in predictions is paramount. Ongoing efforts also emphasize developing robust methods that handle outliers and imbalanced datasets, and creating more interpretable models through calibrated explanations.

Papers

October 28, 2024

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
Tuwe Löfström, Fatima Rabia Yapicioglu, Alessandra Stramiglio, Helena Löfström, Fabio Vitali
Machine Learning Model Line by Line Explanation Calibrated Prediction Probabilistic Regression

September 22, 2024

Scoring rule nets: beyond mean target prediction in multivariate regression
Daan Roordink, Sibylle Hess
Maximum Likelihood Distributional Learning Multivariate Regression Scoring Rule Target Prediction Probabilistic Regression

May 23, 2024

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian Process Adaptive Preconditioner Covariance Conditioning Conjugate Gradient Iterative Method Probabilistic Regression

November 23, 2023

HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms
Uddeshya Upadhyay, Sairam Bade, Arjun Puranik, Shahir Asfahan, Melwin Babu, Francisco Lopez-Jimenez, Samuel J. Asirvatham, Ashim Prasad, Ajit Rajasekharan, Samir Awasthi, Rakesh Barve
Uncertainty Estimation Uncertainty Calibration Canine Electrocardiogram Gradient Correction Medical Time Series Probabilistic Regression

August 30, 2023

Calibrated Explanations for Regression
Tuwe Löfström, Helena Löfström, Ulf Johansson, Cecilia Sönströd, Rudy Matela
Explainable Artificial Intelligence Novel Regression Probabilistic Regression Feature Importance Explanation

May 24, 2023

Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Jungtaek Kim
Semi Supervised Learning Gaussian Process Bayesian Optimization Bayesian Neural Network Density Ratio Probabilistic Regression

November 2, 2022

Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics
Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters
Variational Inference Local Regression Bayesian Kernel Probabilistic Learning Probabilistic Regression Hierarchical Mixture

October 28, 2022

Nonparametric Probabilistic Regression with Coarse Learners
Brian Lucena
Conditional Density Nonparametric Regression Probabilistic Regression Nonparametric Approach Base Classifier

October 13, 2022

Multi-Target XGBoostLSS Regression
Alexander März
Regression Task Gradient Boosting Multi Target Probabilistic Regression

November 30, 2021

Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm
L. A. Bull, P. A. Gardner, T. J. Rogers, N. Dervilis, E. J. Cross, E. Papatheou, A. E. Maguire, C. Campos, K. Worden
Bayesian Model Accurate Predictive Model Wind Farm Probabilistic Regression Power Curve Model Power Data

November 19, 2021

Probabilistic Regression with Huber Distributions
David Mohlin, Gerald Bianchi, Josephine Sullivan
Neural Network Covariance Matrix Facial Landmark Positive Semi Definite Probabilistic Regression Huber Distribution