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..
Improving black-box optimization in VAE latent space using decoder uncertainty
Authors: Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate these advantages across several experimental settings in digit generation, arithmetic expression approximation and molecule generation for drug design. |
| Researcher Affiliation | Academia | Pascal Notin Department of Computer Science University of Oxford Oxford, UK EMAIL José Miguel Hernández-Lobato Department of Engineering University of Cambridge Cambridge, UK EMAIL Yarin Gal Department of Computer Science University of Oxford Oxford, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: Importance sampling estimator of MI Algorithm 2: Bayesian Optimization with uncertainty censoring Algorithm 3: Uncertainty-constrained gradient ascent |
| Open Source Code | Yes | We are open sourcing the code in the repository at the following address: https://github.com/pascalnotin/uncertainty_guided_optimization. |
| Open Datasets | Yes | We train a Character VAE (CVAE) [4] on 80, 000 expressions generated by the formal grammar, then perform optimization in the latent space. ... For both architectures, we trained our models on a set of 250k drug-like molecules from the ZINC dataset [29]. |
| Dataset Splits | Yes | Detailed information for datasets and model hyperparameter values used across experiments are detailed in Appendix B-E (one section dedicated to each experimental setting) and G (reproducibility). ... We consider 4 distinct sets of points in latent: embeddings into latent space of a random sample from the train and test sets... |
| Hardware Specification | Yes | Compute Resources: All experiments were run on NVIDIA V100 GPUs. |
| Software Dependencies | Yes | All models were implemented with Python 3.5.2 and PyTorch (version 1.7.1) [35]. |
| Experiment Setup | Yes | Detailed information for datasets and model hyperparameter values used across experiments are detailed in Appendix B-E (one section dedicated to each experimental setting) and G (reproducibility). |