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 [1].
Dimension-free empirical entropy estimation
Authors: Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we demonstrate that our empirical bounds are significantly sharper than the state-of-the-art bounds, for various natural distributions and non-trivial sample regimes. In Section 6, we compare the rates implied by Corollary 1 to the state of the art on various distributions. |
| Researcher Affiliation | Academia | Doron Cohen Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel EMAIL Aryeh Kontorovich Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel EMAIL Aaron Koolyk Department of Computer Science Hebrew University Jerusalem, Israel EMAIL Geoffrey Wolfer JSPS International Research Fellow Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Tokyo, Japan EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include links to a code repository for the described methodology. |
| Open Datasets | No | The paper evaluates its bounds on theoretical distributions like mixtures of uniform distributions and zeta distributions, rather than real-world public datasets. |
| Dataset Splits | No | The paper does not describe the use of empirical datasets, and therefore no specific dataset split information (like train/validation/test percentages or counts) is provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for the numerical computations that generated the results. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the results. |
| Experiment Setup | No | The paper is primarily theoretical, deriving and comparing bounds for entropy estimation. It does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings for empirical evaluation of a model. |