Bayesian Learning

Bayesian learning is a statistical framework that incorporates prior knowledge into machine learning models to improve prediction accuracy and uncertainty quantification, addressing limitations of traditional methods. Current research focuses on scaling Bayesian methods to large datasets and complex models, employing techniques like variational inference, Markov Chain Monte Carlo (MCMC), and novel architectures such as Bayesian neural networks and Bayesian graph neural networks to enhance efficiency and interpretability. This approach is proving valuable across diverse fields, from healthcare diagnostics and drug discovery to robotics and public health, by providing more reliable and trustworthy predictions alongside measures of uncertainty.

Papers

October 24, 2024

A Generalized Framework for Multiscale State-Space Modeling with Nested Nonlinear Dynamics: An Application to Bayesian Learning under Switching Regimes
Nayely Vélez-Cruz, Manfred D. Laubichler
State Space Model Nonlinear Dynamic Bayesian Learning Switch List Generalized Framework Multiscale System Scale Dynamic

October 16, 2024

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

August 12, 2024

Bayesian Learning in a Nonlinear Multiscale State-Space Model
Nayely Vélez-Cruz, Manfred D. Laubichler
Multi Scale State Space Model Bayesian Learning Multiscale System Gibbs Distribution Noise Covariance

August 5, 2024

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability
Masoud Muhammed Hassan
Inherent Interpretability Bayesian Inference Kolmogorov Arnold Network Bayesian Learning Prediction Uncertainty Probabilistic Approach Enhancing Accuracy Imbalanced Medical

July 30, 2024

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damith C. Ranasinghe, Ehsan Abbasnejad
LeArning Abstract Bayesian Neural Network Deep Ensemble Bayesian Perspective Bayesian Learning Practical Approach

July 17, 2024

Scalable Monte Carlo for Bayesian Learning
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
Markov Chain Monte Carlo Bayesian Learning

July 11, 2024

Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning
Dae-Won Kim, Song Ko, Bo-Yeong Kang
Estimation Task Bayesian Learning Evolutionary Optimization Single Point Mutation Mutation Operator Bayesian Structure Distribution Algorithm

June 13, 2024

CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors for test time refinement
Carlos Plou, Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Ana C. Murillo
Egocentric Video Bayesian Learning Temporal Order Temporal Prior EGO4D Challenge

May 23, 2024

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif, Yevgeny Seldin
PAC Bayesian Bayesian Learning Data Driven Prior Information Loss Frequentist Inference

April 17, 2024

Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
Jiayi Huang, Sangwoo Park, Osvaldo Simeone
Bayesian Learning Selective Inference Calibrated Learning Calibrated Out of Distribution Confidence Minimization

April 3, 2024

Adaptive Sampling Policies Imply Biased Beliefs: A Generalization of the Hot Stove Effect
Jerker Denrell
Strong Generalization Adaptive Sampling Bayesian Learning Biased Behavior Lombard Effect

February 7, 2024

A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health
Biyonka Liang, Lily Xu, Aparna Taneja, Milind Tambe, Lucas Janson
Financial Application Online Learning Online Reinforcement Learning Bayesian Approach Bayesian Learning Restless Multi Armed Bandit Restless Bandit Guideline Adherence Adherence Prediction

February 5, 2024

Beyond the Black Box: A Statistical Model for LLM Reasoning and Inference
Siddhartha Dalal, Vishal Misra
Large Language Model Text Generation Bayesian Learning Terminator Economy

December 9, 2023

Sparse Variational Student-t Processes
Jian Xu, Delu Zeng
Gaussian Process Sparse Representation Bayesian Learning Sparse Gaussian Process Sparse Variational Gaussian Process

November 20, 2023

Adaptive Bayesian Learning with Action and State-Dependent Signal Variance
Kaiwen Hou
Action Feature Bayesian Learning Feedback Loop Bayesian Estimation Decision Making Model Dependent Noise Adaptive Bayesian

November 6, 2023

Asynchronous Local Computations in Distributed Bayesian Learning
Kinjal Bhar, He Bai, Jemin George, Carl Busart
Decentralized Learning Bayesian Learning Agent Communication Gossip Algorithm Local Computation Agent Task

October 17, 2023

On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction
Seohyeon Cha, Honggu Kang, Joonhyuk Kang
Graph Neural Network Uncertainty Quantification Conformal Prediction Prediction Set Bayesian Learning Temperature Measurement

October 10, 2023

Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition
Zhongtian Chen, Edmund Lau, Jake Mendel, Susan Wei, Daniel Murfet
Sequential Learning Phase Transition Bayesian Learning First Order Superposition Singular Learning Learning Coefficient

July 4, 2023

Free energy of Bayesian Convolutional Neural Network with Skip Connection
Shuya Nagayasu, Sumio Watanabe
Convolutional Neural Network Free Energy Bayesian Learning Bayesian Convolutional

June 5, 2023

Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
Saghar Adler, Vijay Subramanian
Markov Decision Process Optimal Policy State Space Markov Chain Discrete Time Bayesian Learning

Bayesian Learning

Papers

A Generalized Framework for Multiscale State-Space Modeling with Nested Nonlinear Dynamics: An Application to Bayesian Learning under Switching Regimes

Double-Bayesian Learning

Bayesian Learning in a Nonlinear Multiscale State-Space Model

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks

Scalable Monte Carlo for Bayesian Learning

Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning

CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors for test time refinement

Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss

Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference

Adaptive Sampling Policies Imply Biased Beliefs: A Generalization of the Hot Stove Effect

A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health

Beyond the Black Box: A Statistical Model for LLM Reasoning and Inference

Sparse Variational Student-t Processes

Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

Asynchronous Local Computations in Distributed Bayesian Learning

On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction

Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition

Free energy of Bayesian Convolutional Neural Network with Skip Connection

Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space