Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nonparametric Estimation of Renyi Divergence and Friends
Authors: Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
ICML 2014 | Venue PDF | 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. |