Bayesian Regression

Bayesian regression is a statistical method that estimates model parameters and quantifies uncertainty by incorporating prior knowledge into the analysis, aiming for more robust and reliable predictions than frequentist approaches. Current research emphasizes improving the accuracy and trustworthiness of uncertainty quantification, particularly in high-dimensional settings and with complex models like neural networks, often employing techniques like Hamiltonian Monte Carlo, Approximate Message Passing, and Bayesian meta-learning. These advancements are crucial for reliable decision-making in diverse fields, including drug discovery, economic modeling, and safety-critical control systems, where accurate uncertainty estimates are paramount.

Papers

October 21, 2024

Hotel Booking Cancellation Prediction Using Applied Bayesian Models
Md Asifuzzaman Jishan, Vikas Singh, Ayan Kumar Ghosh, Md Shahabub Alam, Khan Raqib Mahmud, Bijan Paul
Bayesian Model Travel Mode Bayesian Regression Logistic Model Hotel Demand

July 27, 2024

Bayesian meta learning for trustworthy uncertainty quantification
Zhenyuan Yuan, Thinh T. Doan
Meta Learning Bayesian Perspective Trust Inference Bayesian Regression Meta Learning Prior

July 19, 2024

Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
Hannah Rosa Friesacher, Ola Engkvist, Lewis Mervin, Yves Moreau, Adam Arany
Decision Making Drug Discovery Neural Topic Drug Discovery Process Bayesian Uncertainty Bayesian Regression Probability Calibration Activity Based

April 3, 2024

A Bayesian Regression Approach for Estimating the Impact of COVID-19 on Consumer Behavior in the Restaurant Industry
H. Hinduja, N. Mandal
Global Impact Covid 19 Predictive Model Covid 19 Pandemic Global Pandemic Bayesian Regression Consumer Behavior

February 2, 2024

Parameter uncertainties for imperfect surrogate models in the low-noise regime
Thomas D Swinburne, Danny Perez
Surrogate Model Generalization Error Posterior Distribution Parameter Uncertainty Surrogate Modelling Bayesian Regression

November 14, 2023

Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
Michael Celentano, Zhou Fan, Licong Lin, Song Mei
Variational Inference Mean Field Posterior Inference Free Energy Linear Model Statistical Property Bayesian Regression Posterior Uncertainty

October 9, 2023

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno, Chandula T. Wickramarachchi, Elizabeth J. Cross, Timothy J. Rogers, Keith Worden, Nikolaos Dervilis, Aidan J. Hughes
Industrial Machine Information Sharing Bayesian Regression Surface Roughness Machining Feature Hierarchical Bayesian Model Surface Prediction

February 21, 2023

Improved uncertainty quantification for neural networks with Bayesian last layer
Felix Fiedler, Sergio Lucia
Neural Network Bayesian Neural Network Bayesian Perspective Last Layer Bayesian Regression

January 31, 2023

Differentially Private Distributed Bayesian Linear Regression with MCMC
Barış Alparslan, Sinan Yıldırım, Ş. İlker Birbil
Markov Chain Monte Carlo Private Linear Regression Bayesian Estimation Bayesian Regression Differential Privacy Noise Bayesian Inference Framework

November 4, 2022

Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
Taejoo Ahn, Licong Lin, Song Mei
Bayesian Regression False Discovery Rate Conditional Randomization

October 10, 2022

Sampling-based inference for large linear models, with application to linearised Laplace
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato
Application Proficiency Scientific Inference Large Model Tomographic Reconstruction Bayesian Regression Scalable Bayesian Laplace Explainer

September 23, 2022

Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Boris Ivanovic, James Harrison, Marco Pavone
Meta Learning Autonomous System Online Adaptation Behavior Prediction Bayesian Regression Speech Envelope

April 8, 2022

Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
Online Learning Control Barrier Function Safety Critical Control Uncertain Dynamic Bayesian Regression Uncertain System

March 30, 2022

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
Data Augmentation High Uncertainty Anticipation Bayesian Neural Network Aleatoric Uncertainty Approximate Inference Bayes Classifier Bayesian Regression Parallel Tempering

January 6, 2022

Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics
Bohdan M. Pavlyshenko
Time Series Time Series Prediction Building PCC Time Series Model Integrated Moving Average Stacking Ensemble Bayesian Regression

December 6, 2021

Bounding Wasserstein distance with couplings
Niloy Biswas, Lester Mackey
Markov Chain Monte Carlo Wasserstein Distance Markov Chain Coupling Effect Bayesian Regression

November 19, 2021

Posterior concentration and fast convergence rates for generalized Bayesian learning
Lam Si Tung Ho, Binh T. Nguyen, Vu Dinh, Duy Nguyen
Learning Rate Convergence Rate Heavy Tailed Bayesian Regression Posterior Concentration Generalized Bayes

Bayesian Regression

Papers

Hotel Booking Cancellation Prediction Using Applied Bayesian Models

Bayesian meta learning for trustworthy uncertainty quantification

Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models

A Bayesian Regression Approach for Estimating the Impact of COVID-19 on Consumer Behavior in the Restaurant Industry

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Improved uncertainty quantification for neural networks with Bayesian last layer

Differentially Private Distributed Bayesian Linear Regression with MCMC

Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control

Sampling-based inference for large linear models, with application to linearised Laplace

Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning

Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics

Bounding Wasserstein distance with couplings

Posterior concentration and fast convergence rates for generalized Bayesian learning