Unlocking the Potential of Global Human Expertise
Authors: Elliot Meyerson, Olivier Francon, Darren Sargent, Babak Hodjat, Risto Miikkulainen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | RHEA is first illustrated through a formal synthetic example below, demonstrating how this process can result in improved decision-making. RHEA is then put to work in a large-scale international experiment on developing non-pharmaceutical interventions for the COVID-19 pandemic. The results show that broader and better policy strategies can be discovered in this manner, beyond those that would be available through AI or human experts alone. |
| Researcher Affiliation | Collaboration | Elliot Meyerson1 Olivier Francon1 Darren Sargent1 Babak Hodjat1 Risto Miikkulainen1,2 1Cognizant AI Labs 2The University of Texas at Austin |
| Pseudocode | No | The paper describes procedural steps and includes flow diagrams but does not contain formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Code for the illustrative domain was implemented outside of the proprietary framework and can be found at https://github.com/cognizant-ai-labs/rhea-demo. |
| Open Datasets | Yes | The data collected from the XPRIZE Pandemic Response Challenge (in the Define and Gather phases) and used to distill models that were then Evolved can be found on AWS S3 at https://s3.us-west-2.amazonaws.com/covid-xprize-anon (i.e., in the public S3 bucket named covid-xprize-anon , so it is also accessible via the AWS command line). |
| Dataset Splits | Yes | resulting in 212,400 training samples for each prescriptor, a random 20% of which was used for validation for early stopping. |
| Hardware Specification | Yes | Each training run of RHEA for the Pandemic Response Challenge experiments takes 9 hours on a 16-core m5a.4xlarge EC2 instance. |
| Software Dependencies | Yes | Gaussian Kernel Density Estimation (KDE; Fig. 3d), using the scipy implementation with default parameters [75]. ... SciPy 1.0: fundamental algorithms for scientific computing in python. |
| Experiment Setup | Yes | Distilled models were implemented in Keras [7] and trained with Adam [35] using L1 loss (since policy actions were on an ordinal scale). ...Evolution from the distilled models was run for 100 generations in 10 independent trials to produce the final RHEA models. ...The population size was 200; in RHEA, 169 of the 200 random NNs in the initial population were replaced with distilled models. |