Code for the EMNLP 2021 Paper "Active Learning by Acquiring Contrastive Examples" & the ACL 2022 Paper "On the Importance of Effectively Adapting Pretrained Language Models for Active Learning"
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Updated
May 24, 2022 - Python
Code for the EMNLP 2021 Paper "Active Learning by Acquiring Contrastive Examples" & the ACL 2022 Paper "On the Importance of Effectively Adapting Pretrained Language Models for Active Learning"
Bayesian Optimization algorithms with various recent improvements
Bayesian Optimization for Categorical and Continuous Inputs
This is the companion code for the paper Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization by Lukas P. Fröhlich et al., AISTATS 2020
An improved version of Turbo algorithm for the Black-box optimization competition organized by NeurIPS 2020
Python package for generating experimental designs tailored for uncertainty quantification and featuring parallel implementations
This code runs Bayesian optimization with the exploration enhanced expected improvement (E3I) acquisition function
Black-Box Optimisation (BBO) Capstone Project consisting of Bayesian Optimisation research. Developed as part of the Professional Certificate in Machine Learning & AI programme at Imperial College London.
Comparing acquisition functions in Bayesian Optimization. Implementations, visual comparisons, and analysis of different acquisition strategies.
leADS: improved metabolic pathway inference based on active dataset subsampling
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