Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations

2020/07/21 by Kevin Miller, Hao Li, Miller, Kevin +4
Computer Science · #Algorithms and Data Compression #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2007.11126

openalex publication_date 2020/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distributions to adapt previously Gaussian-based acquisition functions to these more general cases. We develop an efficient rank-one update for applying "look-ahead" based methods as well as model retraining. We also introduce a novel "model change" acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.

Related

• Bayesian Semi-supervised Learning with Graph Gaussian Processes
• Making Better Use of Unlabelled Data in Bayesian Active Learning
• A-Optimal Active Learning
• Improved Graph-based semi-supervised learning Schemes
• Weighted Ensembles for Active Learning with Adaptivity