Bayesian Active Learning

Bayesian Active Learning (BAL) is a machine learning technique aiming to efficiently train models by strategically selecting the most informative data points for labeling, minimizing the need for extensive manual annotation. Current research focuses on improving BAL's efficiency and effectiveness across diverse applications, including image segmentation, large language model preference learning, and regression tasks, often employing Gaussian processes, Bayesian neural networks, and various acquisition functions like BALD and expected predictive information gain. This approach holds significant promise for reducing the cost and time associated with data labeling in various fields, from medical image analysis and precision agriculture to scientific experimentation and reinforcement learning.

Papers

August 3, 2024

Bayesian Active Learning for Semantic Segmentation
Sima Didari, Wenjun Hu, Jae Oh Woo, Heng Hao, Hankyu Moon, Seungjai Min
Semantic Segmentation Semantic Segmentation Model Pixel Level Annotation Pixel Wise Uncertainty Bayesian Active Learning

June 14, 2024

Deep Bayesian Active Learning for Preference Modeling in Large Language Models
Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
Deep Active Learning Bayesian Active Learning Preference Model Preference Label

April 26, 2024

Making Better Use of Unlabelled Data in Bayesian Active Learning
Freddie Bickford Smith, Adam Foster, Tom Rainforth
Active Learning Semi Supervised Learning Bayesian Active Learning Semi Supervised Active Learning

April 3, 2024

Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation
Bart M. van Marrewijk, Charbel Dandjinou, Dan Jeric Arcega Rustia, Nicolas Franco Gonzalez, Boubacar Diallo, Jérôme Dias, Paul Melki, Pieter M. Blok
Active Learning Semantic Segmentation Model Precision Agriculture Use Case Acquisition Function Efficient Annotation Annotated Image Bayesian Active Learning Weed Segmentation

February 19, 2024

Bayesian Active Learning for Censored Regression
Frederik Boe Hüttel, Christoffer Riis, Filipe Rodrigues, Francisco Câmara Pereira
Active Learning Mutual Information Bayesian Perspective Censored Time to Event Bayesian Active Learning

November 14, 2023

Batch Selection and Communication for Active Learning with Edge Labeling
Victor Croisfelt, Shashi Raj Pandey, Osvaldo Simeone, Petar Popovski
Active Learning Timely Communication Bayesian Active Learning Node Label Protocol Design Batch Selection

October 23, 2023

Bayesian Active Learning in the Presence of Nuisance Parameters
Sabina J. Sloman, Ayush Bharti, Julien Martinelli, Samuel Kaski
Speech Presence Active Learning Framework Bayesian Active Learning Bayesian Transfer Nuisance Parameter

August 25, 2023

July 26, 2023

Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities
Ganping Li, Yoshito Otake, Mazen Soufi, Masashi Taniguchi, Masahide Yagi, Noriaki Ichihashi, Keisuke Uemura, Masaki Takao, Nobuhiko Sugano, Yoshinobu Sato
Medical Image Datasets Efficient Hybrid Bayesian Active Learning Uncertainty Based Active Learning Lower Limb Musculoskeletal Segmentation Hybrid Frontier Sampling

April 21, 2023

Self-Correcting Bayesian Optimization through Bayesian Active Learning
Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi
Bayesian Optimization Bayesian Active Learning

April 17, 2023

Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Active Learning Predictive Performance Information Gain Bayesian Active Learning

February 21, 2023

Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset
Anh Duong, Alexandre Almin, Léo Lemarié, B Ravi Kiran
Autonomous Driving Data Augmentation Active Learning Mixed Effect Dataset Distillation Low Temperature Distillation Bayesian Active Learning LiDAR Perception Task

November 17, 2022

Active Learning with Expected Error Reduction
Stephen Mussmann, Julia Reisler, Daniel Tsai, Ehsan Mousavi, Shayne O'Brien, Moises Goldszmidt
Active Learning Error Mitigation Bayesian Active Learning Bayesian Estimation

October 23, 2022

Learning General World Models in a Handful of Reward-Free Deployments
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
Deep Reinforcement Learning World Model Task Agnostic Cascade Model Reward Free Bayesian Active Learning Unsupervised Exploration

May 30, 2022

Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning
Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan, Sergei V. Kalinin
Gaussian Process Bayesian Inference Bayesian Active Learning

May 20, 2022

Bayesian Active Learning with Fully Bayesian Gaussian Processes
Christoffer Riis, Francisco Antunes, Frederik Boe Hüttel, Carlos Lima Azevedo, Francisco Câmara Pereira
Active Learning Gaussian Process Bias Variance Bayesian Active Learning

February 27, 2022

Bayesian Active Learning for Discrete Latent Variable Models
Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow
Active Learning Latent Variable Model Latent State Discrete Latent Variable Bayesian Active Learning Discrete Latent

November 5, 2021

Contextual Bayesian optimization with binary outputs
Tristan Fauvel, Matthew Chalk
Bayesian Optimization Structured Output Bayesian Active Learning Contextual Bayesian Optimization Binary Feedback Stochastic Feedback