Bias Correction

Bias correction in machine learning and related fields aims to mitigate systematic errors that skew model outputs or analyses, leading to inaccurate predictions or unfair outcomes. Current research focuses on developing and evaluating various bias correction techniques, including those based on propensity score matching, attention mechanisms in deep learning models, and generative adversarial networks, applied across diverse domains such as climate modeling, healthcare, and crime prediction. Effective bias correction is crucial for ensuring the reliability and fairness of machine learning models, improving the accuracy of scientific inferences, and promoting equitable applications in various sectors.

Papers

May 23, 2023

Counterfactual Augmentation for Multimodal Learning Under Presentation Bias
Victoria Lin, Louis-Philippe Morency, Dimitrios Dimitriadis, Srinagesh Sharma
Multimodal Learning High Quality Counterfactuals Bias Correction Representation Bias Counterfactual Augmentation

April 7, 2023

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
Sacha Morin, Robin Legault, Félix Laliberté, Zsuzsa Bakk, Charles-Édouard Giguère, Roxane de la Sablonnière, Éric Lacourse
Python Library Gaussian Mixture Latent Feature Variable Model Bias Correction Pseudo Likelihood

February 7, 2023

Learning bias corrections for climate models using deep neural operators
Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis
Climate Model Earth System Model Bias Correction Deep Operator VI DeepONet Deep Neural Operator Digital Algorithmic Nudging DeepONet Architecture

December 29, 2022

A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific
Wei Zhang, Yueyue Jiang, Junyu Dong, Xiaojiang Song, Renbo Pang, Boyu Guoan, Hui Yu
Deep Learning Wind Speed Bias Correction Spatiotemporal Deep Learning

December 16, 2022

Deep learning for bias-correcting CMIP6-class Earth system models
Philipp Hess, Stefan Lange, Christof Schötz, Niklas Boers
Earth System Model Bias Correction Extreme Precipitation Coupled Model Intercomparison Project

November 17, 2022

Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic
Rendani Mbuvha, Julien Yise Peniel Adounkpe, Wilson Tsakane Mongwe, Mandela Houngnibo, Nathaniel Newlands, Tshilidzi Marwala
Knowledge Enhanced Conditional Imputation Bias Correction

November 10, 2022

Fairness and bias correction in machine learning for depression prediction: results from four study populations
Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir
New Machine Procedural Fairness Absolute Stance Bias Bias Correction Depression Risk

August 24, 2022

Identifying and Overcoming Transformation Bias in Forecasting Models
Sushant More
Forecasting Model Bias Correction Space Bias Tweedie Distribution

June 14, 2022

ABCinML: Anticipatory Bias Correction in Machine Learning Applications
Abdulaziz A. Almuzaini, Chidansh A. Bhatt, David M. Pennock, Vivek K. Singh
Algorithmic Fairness Robust Learning Machine Learning Application Dynamic Learning Bias Correction

March 30, 2022

Remember to correct the bias when using deep learning for regression!
Christian Igel, Stefan Oehmcke
Deep Learning Deep Learning Model Absolute Stance Bias Novel Regression Least Square Bias Correction

December 13, 2021

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not
Chelsea Murray, James U. Allingham, Javier Antorán, José Miguel Hernández-Lobato
Active Learning Generalization Error Depth Network Bias Correction Bias Variance Decomposition Addressing Bias