Active Machine Learning

Active machine learning (AML) focuses on efficiently training machine learning models by strategically selecting the most informative data points for labeling and training, minimizing the need for extensive datasets. Current research emphasizes integrating AML with various applications, including 6G network optimization, automated microscopy, and industrial design, often employing Gaussian processes, support vector machines, and reinforcement learning algorithms within human-in-the-loop frameworks. This targeted data acquisition approach significantly accelerates model development and improves performance across diverse fields, offering substantial benefits in terms of reduced computational cost and enhanced efficiency in data-intensive tasks.

Papers

October 15, 2024

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K Gilson, Rose Yu
Active Learning Drug Discovery Molecular Docking Multi Fidelity Surrogate Active Machine Learning

October 11, 2024

ALVIN: Active Learning Via INterpolation
Michalis Korakakis, Andreas Vlachos, Adrian Weller
Active Learning Active Machine Learning Class Alignment

June 5, 2024

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
Omar Alhussein, Ning Zhang, Sami Muhaidat, Weihua Zhuang
Active Learning Data Generation Annotation Rather Channel Acquisition ActIve Learning algORithm Active Machine Learning

October 8, 2023

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan
Machine Learning Future Reasoning Human in the Loop Microscopy Image Machine Learning Workflow Active Machine Learning

September 6, 2023

Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization
Shadi Ghiasi, Guido Pazzi, Concettina Del Grosso, Giovanni De Magistris, Giacomo Veneri
Surrogate Model Surrogate Modeling Design Optimization Select and Combine Active Machine Learning Compressor Based Machine

August 15, 2023

Active Inverse Learning in Stackelberg Trajectory Games
William Ward, Yue Yu, Jacob Levy, Negar Mehr, David Fridovich-Keil, Ufuk Topcu
Stackelberg Game Active Machine Learning Inverse Game

July 5, 2023

Active Class Selection for Few-Shot Class-Incremental Learning
Christopher McClurg, Ali Ayub, Harsh Tyagi, Sarah M. Rajtmajer, Alan R. Wagner
Incremental Learning Class Incremental Learning Autonomous Agent Long Horizon Shot Class Active Machine Learning

April 4, 2023

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, A. Al-Najjar, Christina Doty, Sarah Akers, Nageswara S. Rao, Joshua C. Agar, Steven R. Spurgeon
Deep Learning Electron Microscopy Transmission Electron Active Machine Learning

February 22, 2023

Novel Intent Detection and Active Learning Based Classification (Student Abstract)
Ankan Mullick
Active Learning Semi Supervised Conversational Agent Intent Detection Active Machine Learning

December 6, 2022

Active Classification of Moving Targets with Learned Control Policies
Álvaro Serra-Gómez, Eduardo Montijano, Wendelin Böhmer, Javier Alonso-Mora
Reinforcement Learning Single Drone Control Policy MPC Controller Active Machine Learning Moving Target Dynamic Movement

February 4, 2022

Active metric learning and classification using similarity queries
Namrata Nadagouda, Austin Xu, Mark A. Davenport
Classification Code Active Learning Metric Learning Label Efficient Active Machine 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

November 9, 2021

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Julian Katz-Samuels, Blake Mason, Kevin Jamieson, Rob Nowak
Gaussian Process Real Power Kernel Method Practical Application Interactive Learning Active Machine Learning Agnostic Model

Active Machine Learning

Papers

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning

ALVIN: Active Learning Via INterpolation

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy

Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization

Active Inverse Learning in Stackelberg Trajectory Games

Active Class Selection for Few-Shot Class-Incremental Learning

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Novel Intent Detection and Active Learning Based Classification (Student Abstract)

Active Classification of Moving Targets with Learned Control Policies

Active metric learning and classification using similarity queries

Practical Active Learning with Model Selection for Small Data

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers