ALEF
github.com
Spec sheet
- Purpose
- ALEF provides classes and scripts for active learning of stochastic models such as Gaussian Processes.Read 14 Nov 2026 · github.com
- Security
- The opened repository page does not provide a product security or compliance statement.Read 14 Nov 2026 · github.com
- Support
- For code questions, the README names Matthias Bitzer and Cen-You Li as contacts.Read 14 Nov 2026 · github.com
- License
- The software is open-sourced under the AGPL-3.0 license.Read 14 Nov 2026 · github.com
- Availability
- The repository page lists no releases or packages.Read 14 Nov 2026 · github.com
- Bayesian optimization
- The Bayesian optimization module implements query-based optimization with acquisition functions such as GP-UCB and EI.Read 14 Nov 2026 · github.com
- Design methods
- Its experimental-design methods include pool-based, oracle-based, and safe active learning, as well as Bayesian optimization.Read 14 Nov 2026 · github.com
- Examples
- The main usage of the repository's building blocks is illustrated in Jupyter notebooks.Read 14 Nov 2026 · github.com
- Models and kernels
- Implemented models include standard and marginalized-hyperparameter GPs and Deep GPs; kernels include Deep Kernels and Neural Kernel Network.Read 14 Nov 2026 · github.com
- Pool-based learning
- The pool-based learner can use acquisition functions including Entropy and Variance and validation metrics including RMSE and NLL.Read 14 Nov 2026 · github.com
- Safe learning
- Safe active learning uses a safety model, a data pool with safety labels, and a safety threshold.Read 14 Nov 2026 · github.com
- Setup
- The repository instructs users to create its Conda environment and install the package in editable mode with pip.Read 14 Nov 2026 · github.com
Plans and prices
ALEF (AGPL-3.0 open-source software) Free Not stated AGPL-3.0 license Read 14 Nov 2026 · github.com
Where Specifiction places it
- Active Learning Tools#4 of 167.7
