End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Authors: Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou Ammar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 alexandre.maraval1@huawei.com Matthieu Zimmer Huawei Noah s Ark Lab matthieu.zimmer@huawei.com Antoine Grosnit Huawei Noah s Ark Lab Technische Universität Darmstadt antoine.grosnit2@huawei.com Haitham Bou Ammar Huawei Noah s Ark Lab, University College London haitham.ammar@huawei.com |
| 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. |