Cost Effective Active Search
Authors: Shali Jiang, Roman Garnett, Benjamin Moseley
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on drug and materials discovery datasets and demonstrate that our proposed method is superior to a popular (greedy) baseline. |
| Researcher Affiliation | Collaboration | Shali Jiang CSE, WUSTL St. Louis, MO 63130 jiang.s@wustl.edu Benjamin Moseley Tepper School of Business, CMU and Relational AI Pittsburgh, PA 15213 moseleyb@andrew.cmu.edu Roman Garnett CSE, WUSTL St. Louis, MO 63130 garnett@wustl.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Matlab implementations of our method and the baselines are available here: https://github.com/ shalijiang/efficient_nonmyopic_active_search.git |
| Open Datasets | Yes | We conduct comprehensive experiments on drug and materials discovery datasets and demonstrate that our proposed method is superior to a popular (greedy) baseline. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab implementations' but does not provide specific version numbers for Matlab or any other key software dependencies. |
| Experiment Setup | Yes | We consider two schemes: setting r to a constant or proportional to the remaining target. For example, ENCES-10 means we always set r = 10 if the remaining target is greater than 10, otherwise we use the actual remaining target; and ENCES-0.2 means we set r to be 20% of the actual remaining target. |