Pool Based Active Learning

Pool-based active learning aims to efficiently train machine learning models by strategically selecting a subset of unlabeled data for labeling, maximizing model performance with minimal annotation effort. Current research focuses on improving the scalability and efficiency of algorithms, particularly for large, imbalanced datasets and multi-class problems, often employing techniques like generative flow networks and novel uncertainty sampling methods. This approach is crucial for applications where labeled data is scarce or expensive, impacting fields ranging from scientific computing and medical diagnosis to natural language processing and entity matching by reducing annotation costs and improving model accuracy.

Papers

September 11, 2024

FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
Youguang Chen, George Biros
Multiclass Classification Multinomial Logistic Active Learning Algorithm Pool Based Active Learning

April 18, 2024

Neural Active Learning Beyond Bandits
Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He
Active Learning Neural Network Approximation Pool Based Active Learning Neural Active Learning

April 8, 2024

AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
Pietro Lesci, Andreas Vlachos
Active Learning Imbalanced Datasets Imbalanced Classification Efficient Active Learning Pool Based Active Learning

December 23, 2023

Optimal Decision Tree with Noisy Outcomes
Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi
Decision Tree Noisy Data Linear Classifier Pool Based Active Learning Optimal Decision Tree

October 2, 2023

Pool-Based Active Learning with Proper Topological Regions
Lies Hadjadj, Emilie Devijver, Remi Molinier, Massih-Reza Amini
Active Learning Topological Data Analysis Pool Based Active Learning Multi Class Classification Task

September 11, 2023

Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss
Sebastian Schmidt, Stephan Günnemann
Deep Learning Autonomous Vehicle Active Learning Perception Aware Temporal Feature Pool Based Active Learning Prediction Loss

July 6, 2023

Understanding Uncertainty Sampling
Shang Liu, Xiaocheng Li
High Uncertainty Anticipation Convex Loss Active Learning Algorithm Uncertainty Measure Pool Based Active Learning

June 26, 2023

BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
Active Learning Generative Flow Network Pool Based Active Learning Batch Active Learning

May 23, 2023

Active Learning Principles for In-Context Learning with Large Language Models
Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu
Context Learning Active Learning Shot Learning Context Example Pool Based Active Learning

November 1, 2022

Entity Matching by Pool-based Active Learning
Youfang Han, Chunping Li
Entity Matching Pool Based Active Learning

June 25, 2022

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
Active Learning Deep Network Neural Tangent Kernel Sequential Learning Pool Based Active Learning

February 11, 2022

Fast Rates in Pool-Based Batch Active Learning
Claudio Gentile, Zhilei Wang, Tong Zhang
Active Learning Greedy Algorithm Test Oracle Fast Rate Pool Based Active Learning Label Complexity

February 8, 2022

A Lagrangian Duality Approach to Active Learning
Juan Elenter, Navid NaderiAlizadeh, Alejandro Ribeiro
Active Learning Primal Dual Lagrangian System Active Sampling Constrained Learning Pool Based Active Learning

January 29, 2022

Towards Robust Deep Active Learning for Scientific Computing
Simiao Ren, Yang Deng, Willie J. Padilla, Jordan Malof
Deep Learning Active Learning Deep Active Learning Scientific Computing Pool Based Active Learning

December 21, 2021

Practical Active Learning with Model Selection for Small Data
Maryam Pardakhti, Nila Mandal, Anson W. K. Ma, Qian Yang
Active Learning Model Selection Small Datasets Pool Based Active Learning Active Machine Learning