Fast Parallel Algorithms for Statistical Subset Selection Problems
Authors: Sharon Qian, Yaron Singer
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically evaluate the performance of DASH, we conducted several experiments on feature selection and Bayesian experimental design. |
| Researcher Affiliation | Academia | Sharon Qian Harvard University sharonqian@g.harvard.edu Yaron Singer Harvard University yaron@seas.harvard.edu |
| Pseudocode | Yes | Algorithm 1 DASH (N, r, ) |
| Open Source Code | No | The paper does not provide any specific links to source code or statements about its availability. |
| Open Datasets | No | We generated the synthetic feature space from a multivariate normal distribution... We also select features on a clinical dataset n = 385 (D2) and classify location of cancer in a biological dataset n = 2500 (D4). We use D1, D2 for linear regression and Bayesian experimental design, and D3, D4 for logistic regression experiments. (See Appendix I.2 for details.) |
| Dataset Splits | No | The paper mentions running experiments on datasets but does not explicitly provide details about training, validation, or test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper discusses computational efficiency and speedups but does not provide specific hardware details such as GPU/CPU models, processor types, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages or libraries, that would be needed to replicate the experiments. |
| Experiment Setup | Yes | We implemented DASH with 5 samples at every round. |