Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2551–2560 of 3073 papers

Title	Date	Tasks	Status
A Simple yet Brisk and Efficient Active Learning Platform for Text Classification	Jan 31, 2021	Active LearningBIG-bench Machine Learning	CodeCode Available
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning	Aug 17, 2023	Active LearningDiversity	CodeCode Available
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection	Nov 8, 2023	Active LearningAdversarial Attack	CodeCode Available
Mitigating shortage of labeled data using clustering-based active learning with diversity exploration	Jul 6, 2022	Active LearningClustering	CodeCode Available
Data-efficient Neural Text Compression with Interactive Learning	Jun 1, 2019	Active LearningHeadline Generation	CodeCode Available
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models	May 27, 2025	Active Learning	CodeCode Available
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation	Nov 6, 2024	Active LearningBayesian Optimization	CodeCode Available
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning	May 30, 2020	Active Learningreinforcement-learning	CodeCode Available
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications	Sep 14, 2022	Active LearningManagement	CodeCode Available
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified