The KSLCL-DCEE algorithm for auto-optimization

Bayesian Active Learning for Remote Sensing

Quantile set inversion [arXiv:2211.01008] — Numerical experiments

This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.

This is a repository associated with the paper "Near-optimal sampling strategies for multivariate function on general domains" by Ben Adcock and Juan M. Cardenas available at https://epubs.siam.org/doi/10.1137/19M1279459 and https://arxiv.org/abs/1908.01249

Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NO x production

Language Agnostic Syllabification with Active Learning

The demo partially associated with the following papers: "Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images" and "Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification".

A toolbox for Weighted Sparse Simplex Representation (WSSR).

Matlab source code of the iRDM algorithm in the paper: Z. Liu, X. Jiang, H. Luo, W. Fang, J. Liu and D. Wu*, "Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)," Pattern Recognition Letters, 142:11-19, 2021.

Project source code and data for risk estimation with an imperfect Machine learning model

Gaussian Processes for Cyclic Voltammetry

Matlab code of the IRD algorithm in the paper: 刘子昂, 蒋雪, 伍冬睿, "基于池的无监督线性回归主动学习," 自动化学报, 2020. Or the English version here: https://arxiv.org/pdf/2001.05028.pdf

Matlab source code of the paper: D. Wu*, C-T Lin and J. Huang*, "Active Learning for Regression Using Greedy Sampling," Information Sciences, vol. 474, pp. 90-105, 2019.

Matlab source code of the paper: D. Wu, "Pool-based sequential active learning for regression," IEEE Trans. on Neural Networks and Learning Systems, 30(5), pp. 1348-1359, 2019.

The Matlab code of "A Variance Maximization Criterion for Active Learning"

The code of ''Single Shot Active Learning using Pseudo Annotators"

This repository contains the code to reproduce all of the results in our paper: Nuclear discrepancy for single-shot batch active learning, Tom J Viering, Jesse H Krijthe, Marco Loog, in Machine Learning 2019.

Active versus Passive exploration

Active Learning combining Online Semi-Supervised Dictionary Learning