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 [1].
Cost Effective Active Search
Authors: Shali Jiang, Roman Garnett, Benjamin Moseley
NeurIPS 2019 | Venue PDF | 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 EMAIL Benjamin Moseley Tepper School of Business, CMU and Relational AI Pittsburgh, PA 15213 EMAIL Roman Garnett CSE, WUSTL St. Louis, MO 63130 EMAIL |
| 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. |