Scalable Bayesian

Scalable Bayesian methods aim to overcome the computational challenges of applying Bayesian inference to large datasets and complex models, such as deep neural networks, enabling the quantification of uncertainty in predictions. Current research focuses on developing efficient algorithms like variational inference and Markov Chain Monte Carlo methods, often implemented within frameworks leveraging automatic differentiation libraries (e.g., JAX, PyTorch), and explores novel architectures like normalizing flows and active subspaces to reduce computational complexity. These advancements are crucial for improving the reliability and robustness of machine learning models across diverse applications, from scientific modeling to real-time decision-making systems.

Papers

October 25, 2024

FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
Chao Li, Zhicheng Xu, Bo Wen, Ruibin Mao, Can Li, Thomas Kämpfe, Kai Ni, Xunzhao Yin
High Efficiency Bayesian Inference Memory Computing Computing in Memory Scalable Bayesian

August 29, 2024

Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities
Dongyeop Woo, Sungsoo Ahn
Normalizing Flow Open Sampling Flow Matching Boltzmann Distribution Scalable Bayesian

May 31, 2024

Scalable Bayesian Learning with posteriors
Samuel Duffield, Kaelan Donatella, Johnathan Chiu, Phoebe Klett, Daniel Simpson
Posterior Inference Bayesian Perspective Posterior Approximation Posterior Mode Scalable Bayesian

April 29, 2024

Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
Javier Antoran
Neural Network Deep Learning Deep Neural Network Deep Learning Model Gaussian Process New Era Model Uncertainty Posterior Sampling Neural Network Inference Scalable Bayesian

February 16, 2024

BlackJAX: Composable Bayesian inference in JAX
Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf, Antoine Carnec, Kaustubh Chaudhari, Reuben Cohn-Gordon, Jeremie Coullon, Wei Deng, Sam Duffield, Gerardo Durán-Martín, Marcin Elantkowski, Dan Foreman-Mackey, Michele Gregori, Carlos Iguaran, Ravin Kumar, Martin Lysy, Kevin Murphy, Juan Camilo Orduz, Karm Patel, Xi Wang, Rob Zinkov
Variational Inference Variational Method State of the Art JAX Bayesian Computation Scalable Bayesian

November 27, 2023

Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock, Guillaume Prenat, Lorena Anghel, Mehdi B. Tahoori
Deep Neural Network High Uncertainty Anticipation Bayesian Neural Network Scalable Bayesian Dropout Based

September 6, 2023

Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon
Deep Neural Network Uncertainty Quantification Variational Inference Bayesian Deep Learning Active Subspace Scalable Bayesian

May 1, 2023

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
Aliaksandr Hubin, Geir Storvik
Full Model Variational Inference Bayesian Neural Network Model Uncertainty Structure Learning Parameter Uncertainty Scalable Variational Scalable Bayesian

March 31, 2023

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Yilang Zhang, Bingcong Li, Shijian Gao, Georgios B. Giannakis
Meta Learning Scalable Bayesian Inner Optimization Optimization Trajectory Implicit Gradient

February 9, 2023

Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
Robust Version Change Point Detection Scalable Bayesian Bayesian Online Change Point Detection Generalised Bayesian Score Decomposed Diffusion Model

January 26, 2023

Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Louis Sharrock, Christopher Nemeth
Bayesian Inference Universal Rate Particle Method Particle Based Variational Inference Scalable Bayesian

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

August 10, 2022

Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian Vasile
Reward Report User Interest Scalable Model Probabilistic Ranking Scalable Bayesian Slate Recommendation

December 25, 2021

Reactive Message Passing for Scalable Bayesian Inference
Dmitry Bagaev, Bert de Vries
Message Passing Belief Propagation Scalable Bayesian