Bayesian Inference Framework

Bayesian inference frameworks are increasingly used to address uncertainty in diverse machine learning applications, aiming to provide more reliable and interpretable predictions. Current research focuses on integrating Bayesian methods with various model architectures, including neural networks (both classical and quantum-classical hybrids), Gaussian processes, and autoencoders, often within distributed or privacy-preserving settings. This approach enhances the trustworthiness of models across numerous fields, from medical diagnosis and autonomous robotics to climate modeling and large language model applications, by providing not only predictions but also quantifiable uncertainty estimates. The resulting probabilistic interpretations improve decision-making in situations with incomplete or noisy data, leading to more robust and reliable systems.

Papers

November 1, 2024

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
Rui Yang, Jie Wang, Guoping Wu, Bin Li
Native Robustness Bayesian Inference Robust Offline Reinforcement Learning Bayesian Inference Framework Data Corruption Robust Agent

October 16, 2024

Double-Bayesian Learning
Stefan Jaeger
Bayesian Learning Bayes Rule Probabilistic Learning Bayesian Inference Framework Bayesian Decision

June 10, 2024

Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset
Alona Sakhnenko, Julian Sikora, Jeanette Miriam Lorenz
Medical Datasets Clinical Datasets Classification Uncertainty Bayesian Inference Framework

April 18, 2024

BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models
Yu Feng, Ben Zhou, Weidong Lin, Dan Roth
Large Language Model Bird Specie Deduction System Probability Estimation Bayesian Inference Framework

March 12, 2024

Fusing Climate Data Products using a Spatially Varying Autoencoder
Jacob A. Johnson, Matthew J. Heaton, William F. Christensen, Lynsie R. Warr, Summer B. Rupper
Supervised Autoencoder Interpretable Embeddings Precipitation Data Bayesian Inference Framework

December 16, 2023

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
Gaussian Process Uncertainty Quantification Treatment Effect Heterogeneous Treatment Effect Prior Distribution Semi Parametric Heterogeneous Treatment Effect Estimation Bayesian Inference Framework

March 15, 2023

Bayesian Learning for the Robust Verification of Autonomous Robots
Xingyu Zhao, Simos Gerasimou, Radu Calinescu, Calum Imrie, Valentin Robu, David Flynn
Autonomous Robot Bayesian Learning Continuous Time Markov Chain Bayesian Inference Framework Autonomous Mission

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

August 29, 2022

Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
Majid Mohammadi
Mixture Model Bayesian Model Multi Criterion Decision Bayesian Inference Framework

May 25, 2022

These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots
Parker Ewen, Adam Li, Yuxin Chen, Steven Hong, Ram Vasudevan
Mobile Robot Robot Navigation Risk Aware Digital Map Terrain Topology Bayesian Inference Framework Human Walking Terrain Estimation

March 31, 2022

VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian
Activity Recognition Bayesian Neural Network Variational Objective Bayesian Inference Framework Dynamic Selection

March 11, 2022

Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications
Max Sklar
Bayesian Inference Logistic Regression Practical Application Supervised Machine Learning Statistical Inference Sampling Bias Bayesian Inference Framework

March 3, 2022

Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches
Saumik Dana
Deep Learning Consistent Comparison LSTM Network Bayesian Inference Time Series Analysis Bayesian Inference Framework LSTM Autoencoder Bidirectional Displacement

January 13, 2022

An Overview of Uncertainty Quantification Methods for Infinite Neural Networks
Florian Juengermann, Maxime Laasri, Marius Merkle
Neural Network Gaussian Process Predictive Uncertainty Infinite Width Neural Network Bayesian Inference Framework

November 29, 2021

Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
Faria Huq, Adrish Dey, Sahra Yusuf, Dena Bazazian, Tolga Birdal, Nina Miolane
Shape Aware Functional Map Shape Correspondence Bayesian Inference Framework

Bayesian Inference Framework

Papers

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions

Double-Bayesian Learning

Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset

BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models

Fusing Climate Data Products using a Spatially Varying Autoencoder

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Bayesian Learning for the Robust Verification of Autonomous Robots

Differentially Private Distributed Bayesian Linear Regression with MCMC

Unified Bayesian Frameworks for Multi-criteria Decision-making Problems

These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots

VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition

Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications

Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches

An Overview of Uncertainty Quantification Methods for Infinite Neural Networks

Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks