Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
Authors: Kyra Gan, Su Jia, Andrew Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data, demonstrating that our algorithms outperform previously proposed heuristic policies by large margins. |
| Researcher Affiliation | Academia | Kyra Gan , Su Jia , Andrew A. Li Carnegie Mellon University Pittsburgh, PA 15213 {kyragan,sujia,aali1}@cmu.edu |
| Pseudocode | Yes | Algorithm 1 Partially Adaptive Algorithm: Rn B(B, ) |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing its code or a link to a code repository for its methodology. |
| Open Datasets | Yes | We use genetic mutation data from real cancer patients: the publicly-available catalogue of somatic mutations in cancer (COSMIC) [40, 16], which includes the de-identiļ¬ed gene-screening panels for 1,350,015 patients. |
| Dataset Splits | No | The paper describes the number of instances and replications for synthetic data, and the processing of real-world data, but does not provide specific train, validation, or test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | The threshold for entering Phase 2 policy in NJ Adaptive was set to be 0.1. [...] The threshold for entering Phase 2 policy, r, in NJ Adaptive was set to be 0.3. |