Discovering Potential Correlations via Hypercontractivity
Authors: Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we provide numerical experiments demonstrating that this proposed estimator discovers potential correlations among various indicators of WHO datasets, is robust in discovering gene interactions from gene expression time series data, and is statistically more powerful than the estimators for other correlation measures in binary hypothesis testing of canonical examples of potential correlations. |
| Researcher Affiliation | Academia | University of Illinois at Urbana Champaign1 and University of Washington2 {hyejikim,wgao9}@illinois.edu,ksreeram@uw.edu,{swoh,pramodv}@illinois.edu |
| Pseudocode | No | The paper describes the estimation and optimization process in text and mathematical formulas but does not include a formal pseudocode block or algorithm figure. |
| Open Source Code | Yes | Code is available at https://github.com/wgao9/hypercontractivity |
| Open Datasets | Yes | We compute the hypercontractivity coefficient, MIC, and Pearson correlation of 1600 pairs of indicators for 202 countries in the World Health Organization (WHO) dataset [5]... We replicate the genetic pathway detection experiment from [7], and show that hypercontractivity correctly discovers the genetic pathways from smaller number of samples. |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world datasets, but it does not specify explicit train/validation/test dataset splits typical for machine learning model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducing the experiments. |
| Experiment Setup | No | The paper describes general aspects of experimental setup, such as sample sizes (e.g., n = 320) and noise levels (σ2), and initialization for the estimator (wi = 1 + N(0, σ2) for σ2 = 0.01), but it does not provide comprehensive details on all hyperparameters, training configurations, or system-level settings required for full reproducibility of the experiments. |