Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Authors: Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou Ammar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method achieves state-of-the-art regret results against various baselines in experiments on standard hyperparameter optimisation tasks and also outperforms others in the real-world problems of mixed-integer programming tuning, antibody design, and logic synthesis for electronic design automation. |
| Researcher Affiliation | Collaboration | Alexandre Maraval Huawei Noah s Ark Lab EMAIL Matthieu Zimmer Huawei Noah s Ark Lab EMAIL Antoine Grosnit Huawei Noah s Ark Lab Technische Universität Darmstadt EMAIL Haitham Bou Ammar Huawei Noah s Ark Lab, University College London EMAIL |
| Pseudocode | Yes | Algorithm 1 Neural Acquisition Process training. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We experiment on the HPO-B benchmark [44], which contains datasets of (classification) model hyperparameters... We use the open-source SCIP solver [45] and the Benchmark suite from the MIPLib2017 [46]... We collected datasets of CDRH3 sequences and their respective binding energies... from the protein database bank [49]... We collected datasets for 43 different circuits. Each dataset consisted of 500 sequences... from Open ABC [50]. |
| Dataset Splits | Yes | We train our model on data collected from BO traces on 103 MIPs and test on a held-out set of 42 instances... we meta-learn on 109 datasets, validate on 16, and test NAP on 32 new antigens... We train all methods on 30 circuits from Open ABC [50], validate on 4 and test on 9. |
| Hardware Specification | No | The paper mentions running experiments 'on the same GPU' but does not specify any particular GPU model, CPU, or other hardware details. |
| Software Dependencies | No | The paper mentions using 'open-source SCIP solver [45]' and 'open-source ABC library [51]' but does not provide specific version numbers for these or any other software dependencies crucial for reproduction. |
| Experiment Setup | No | The paper mentions '5 initial points' for the experiments (Figure 2 caption) but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings for their proposed NAP model. |