Data Amplification: A Unified and Competitive Approach to Property Estimation
Authors: Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the estimator s practical advantages by comparing it to existing estimators for a wide variety of properties and distributions. We evaluated the new estimator f by comparing its performance to several recent estimators [13 15, 22, 27]. To ensure robustness of the results, we performed the comparisons for all the symmetric properties described in the introduction... The results for the first three properties are shown in Figures 1 3... |
| Researcher Affiliation | Collaboration | Yi HAO Dept. of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 yih179@eng.ucsd.edu Alon Orlitsky Dept. of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 alon@eng.ucsd.edu Ananda T. Suresh Google Research, New York New York, NY 10011 theertha@google.com Yihong Wu Dept. of Statistics and Data Science Yale University New Haven, CT 06511 yihong.wu@yale.edu |
| Pseudocode | No | The paper describes the estimator's construction mathematically but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We tested the five properties on the following distributions: uniform distribution; a distribution randomly generated from Dirichlet prior with parameter 2; Zipf distribution with power 1.5; Binomial distribution with success probability 0.3; Poisson distribution with mean 3,000; geometric distribution with success probability 0.99. |
| Dataset Splits | No | The paper does not explicitly provide details about train/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We tested the five properties on the following distributions... The number of samples, n, ranged from 1,000 to 100,000... Each experiment was repeated 100 times... We chose the amplification parameter t as log1 α n + 1, where α {0.0, 0.1, 0.2, ..., 0.6} was selected based on independent data, and similarly for s0. |