Pool-based active learning in Python
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Apr 8, 2026 - Python
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uncertainty-sampling
Here are 14 public repositories matching this topic...
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
monte-carlo probability stochastic monte-carlo-simulation stochastic-process uncertainty-quantification probabilistic uncertainty-propagation latin-hypercube uncertainty-sampling latin-hypercube-sampling
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Jun 2, 2026 - Python
Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
monte-carlo particle-filter gpu-acceleration monte-carlo-simulation probability-distributions robust-optimization gpu-computing physical-quantities uncertainty-propagation error-propagation numeric-types uncertainties monte-carlo-sampling error-analysis uncertainty-sampling
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May 1, 2026 - Julia
Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For the blog:
machine-learning deep-learning egl active-learning uncertainty-sampling core-set adversarial-active-learning bayesian-active-learning
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Sep 26, 2019 - Python
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
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Dec 25, 2017 - Java
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no c…
classification dynamic-programming semi-supervised datastream uncertainty-sampling classifier-confidence
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Dec 25, 2017 - Java
Spring 2021 - Automation of Scientific Research - course project
logistic-regression active-learning modal-library random-forest-classifier uncertainty-sampling iwal algorithm-implementation type-1-active-learning density-based-sampling query-by-committee max-disagreement-sampling
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Nov 10, 2021 - Jupyter Notebook
PHD Thesis at CERFACS: Uncertainty Quantification for High Dimensional Problems
uncertainty-quantification sensitivity-analysis computational-fluid-dynamics uncertainty-sampling uncertainty-visualisation
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Feb 4, 2021 - TeX
Open Active Learning for Deep Models
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Aug 23, 2019 - Python
This library proposes a plug-in approach to active learning utilizing bagging techniques. Bagging, or bootstrap aggregating, is an ensemble learning method designed to improve the stability and accuracy of machine learning algorithms.
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Jul 30, 2024 - Python
A lab exercise to build, evaluate, and compare end-to-end Active Learning (AL) pipelines using AWS and MLFlow.
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Oct 20, 2025 - Jupyter Notebook
Active Learning framework using BERT to efficiently detect hate speech and offensive language with minimal labeled data.
nlp machine-learning deep-learning text-classification bert active-learning offensive-language social-media-analysis uncertainty-sampling hate-speech-detection
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Oct 21, 2025 - Jupyter Notebook
Active Learning for Job Posting Classification
ai text-classification ml active-learning uncertainty-sampling query-by-committee diversity-sampling
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Jun 9, 2026 - Python
Label-efficiency benchmark — margin sampling hits target accuracy with 38% fewer labels than random, while least-confidence/entropy underperform it. The honest 'which strategy, not whether'
python machine-learning annotation scikit-learn ml active-learning uncertainty-sampling sample-selection data-centric-ai label-efficiency
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Jul 25, 2026 - Python
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