Hierarchical Bayesian Model

Hierarchical Bayesian models offer a powerful framework for analyzing data with inherent hierarchical structures, enabling efficient information sharing across different levels of the hierarchy and improving prediction accuracy, especially in situations with limited data per level. Current research focuses on applying these models to diverse fields, including multi-task learning in large language models and online advertising, using algorithms like Thompson sampling and incorporating various model architectures such as Gaussian processes and neural networks. This approach is proving valuable for improving the efficiency and robustness of machine learning algorithms across numerous applications, from personalized medicine to resource allocation and scientific inference.

Papers

August 31, 2024

Multi-Task Combinatorial Bandits for Budget Allocation
Lin Ge, Yang Xu, Jianing Chu, David Cramer, Fuhong Li, Kelly Paulson, Rui Song
Multi Task Optimal Allocation Budget Allocation Hierarchical Bayesian Model

July 8, 2024

BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-task Large Language Models
Simen Eide, Arnoldo Frigessi
Multi Task Adaptation Concern Low Rank Adaptation LLM Fine Tuning Hierarchical Bayesian Model Global Normality

May 24, 2024

A hierarchical Bayesian model for syntactic priming
Weijie Xu, Richard Futrell
Syntactic Knowledge Lexical Bias Hierarchical Bayesian Model Syntactic Priming

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 Machining Feature Surface Roughness Hierarchical Bayesian Model Surface Prediction

June 30, 2023

Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting
John Mark Agosta, Mario Inchiosa
Hierarchical Model Joint predictioN Hierarchical Bayesian Predictive Feature Hierarchical Bayesian Model

June 16, 2023

A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning
Minyoung Kim, Timothy Hospedales
Variational Inference Meta Learning Shot Meta Learning Hierarchical Bayesian Model Unsupervised Episode

March 2, 2023

Variational EP with Probabilistic Backpropagation for Bayesian Neural Networks
Kehinde Olobatuyi
Bayesian Neural Network Category Wise Variation Probabilistic Neural Network Expectation Propagation Hierarchical Bayesian Model Sparse Logistic Regression

November 15, 2022

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration
Graph Neural Network Bayesian Posterior Probabilistic Dependency Hierarchical Bayesian Model Hierarchical Inference Mass Convergence Event Export Import Exchange Rate Convergence

June 16, 2022

Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes
Michail Spitieris, Ingelin Steinsland
Gaussian Process Digital Twin Bayesian Approach Single Model Fidelity Model Hierarchical Bayesian Model Independent Model Physical Law Learning

February 3, 2022

Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh
Thompson Sampling Part Whole Hierarchy Hierarchical Bayesian Model