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..
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation
Authors: Shashank Singh, Barnabas Poczos
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The main contribution of our work is to provide such a bound for an estimator of R enyi-α divergence for a smooth H older class of densities on the d-dimensional unit cube [0, 1]d. We also illustrate our theoretical results with a numerical experiment. |
| Researcher Affiliation | Academia | Shashank Singh EMAIL Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA Barnab as P oczos EMAIL Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA |
| Pseudocode | No | The paper describes the estimation method mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of source code for the described methodology. |
| Open Datasets | No | We used our estimator to estimate the R enyi α-divergence between two normal distributions in R3 restricted to the unit cube. In particular, for p = N( µ1, Σ), q = N( µ2, Σ). For each n ∈ {1, 2, 5, 10, 50, 100, 500, 1000, 2000, 5000}, n data points were sampled according to each distribution and constrained (via rejection sampling) to lie within [0, 1]3. The paper generates synthetic data and does not use or provide access to a pre-existing public dataset. |
| Dataset Splits | No | For each n ∈ {1, 2, 5, 10, 50, 100, 500, 1000, 2000, 5000}, n data points were sampled according to each distribution and constrained (via rejection sampling) to lie within [0, 1]3. Our estimator was computed from these samples, for α = 0.8, using the Epanechnikov Kernel K(u) = 3 4(1 u2) on [ 1, 1], with constant bandwidth h = 0.25. The true α-divergence was computed directly according to its definition on the (renormalized) distributions on [0, 1]3. The bias and variance of our estimator were then computed in the usual manner based on 100 trials. The paper describes a simulation setup and data generation, but not explicit train/validation/test splits from a dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or versions (e.g., libraries with version numbers) used for the experiments. |
| Experiment Setup | Yes | Our estimator was computed from these samples, for α = 0.8, using the Epanechnikov Kernel K(u) = 3 4(1 u2) on [ 1, 1], with constant bandwidth h = 0.25. |