Machine Learning Regression

Machine learning regression focuses on building models that predict continuous outcomes, aiming for accurate and unbiased predictions. Current research emphasizes improving model accuracy and addressing systematic biases, particularly in the prediction of extreme values, through techniques like constrained optimization and variance scaling. These advancements are crucial for reliable applications across diverse fields, including fluid dynamics simulations, energy price forecasting, and medical image analysis, where accurate and fair predictions are essential.

Papers

September 6, 2024

FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
Franziska Griese, Fabian Hoppe, Alexander Rüttgers, Philipp Knechtges
Neural Network Inverse Problem Fluid Dynamic Data Discretization Navier Stokes Machine Learning Regression Incompressible Fluid

August 29, 2024

Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis
Yibin Sun, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet
Novel Dataset Machine Learning Regression Gas Price Stream Analytics

May 24, 2024

A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction
Hwiyoung Lee, Shuo Chen
Application Proficiency Absolute Stance Bias Language Correction Brain Age Systematic Bias Biased Prediction Machine Learning Regression

March 18, 2024

PETScML: Second-order solvers for training regression problems in Scientific Machine Learning
Stefano Zampini, Umberto Zerbinati, George Turkiyyah, David Keyes
Gradient Method Scientific Machine Learning Unconstrained Optimization Second Order Algorithm Machine Learning Regression

October 18, 2023

Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?
Pascal Pernot
Loss Function Adaptive Importance Strong Consistency Prediction Uncertainty Machine Learning Regression Binning Method Isotonic Regression

October 11, 2023

Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data
Philip Wong, Phue Thant, Pratiksha Yadav, Ruta Antaliya, Jongwook Woo
Predictive Model Predictive Performance Machine Learning Regression

October 5, 2023

Distribution-free risk assessment of regression-based machine learning algorithms
Sukrita Singh, Neeraj Sarna, Yuanyuan Li, Yang Li, Agni Orfanoudaki, Michael Berger
Conformal Prediction Risk Assessment Distribution Free Machine Learning Regression Failure Probability

May 25, 2023

Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach
Imra Aqeel, Abdul Majid
Covid 19 Molecular Docking Non Structural Protein Drug Candidate Drug Repurposing Machine Learning Regression

November 23, 2022

Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
Jirong Yi, Qiaosheng Zhang, Zhen Chen, Qiao Liu, Wei Shao, Yusen He, Yaohua Wang
Stochastic Gradient Descent Mutual Information Cross Entropy Loss Information Theoretic Entropy Minimization Machine Learning Regression

October 13, 2022

Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing
Andrei Velichko, Maksim Belyaev, Matthias P. Wagner, Alireza Taravat
Remote Sensing Singular Value Decomposition Entanglement Entropy Machine Learning Regression Entropy Approximation

August 8, 2022

Learning to Learn to Predict Performance Regressions in Production at Meta
Moritz Beller, Hongyu Li, Vivek Nair, Vijayaraghavan Murali, Imad Ahmad, Jürgen Cito, Drew Carlson, Ari Aye, Wes Dyer
LeArning Abstract Performance Prediction Meta Level Production Incident Machine Learning Regression

February 2, 2022

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
Mostafa M. Mohamed, Björn W. Schuller
Machine Learning Procedural Fairness Label Bias Data Balancing Data Normalisation Machine Learning Regression

Machine Learning Regression

Papers

FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems

Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis

A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction

PETScML: Second-order solvers for training regression problems in Scientific Machine Learning

Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?

Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data

Distribution-free risk assessment of regression-based machine learning algorithms

Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach

Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems

Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing

Learning to Learn to Predict Performance Regressions in Production at Meta

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems