Nonparametric Estimation of Renyi Divergence and Friends
Authors: Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical guarantees with a number of simulations. |
| Researcher Affiliation | Academia | Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA 15213 |
| Pseudocode | No | The paper describes the estimators mathematically but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper mentions drawing samples from distributions for simulations but does not specify the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes splitting samples for estimator training (e.g., 'only train on half of the sample') but does not provide specific train/validation/test dataset splits for model evaluation. |
| Hardware Specification | No | The paper discusses simulations and empirical results but does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Assumption 4 (Parameter Selection). Set the KDE bandwidth h n^-1/(2s+d). For any projection-style estimator, set the number of basis elements m n^(2s0)/(4s0+d) for some s0 < s. |