Dimension-free empirical entropy estimation
Authors: Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 doronv@post.bgu.ac.il Aryeh Kontorovich Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel karyeh@cs.bgu.ac.il Aaron Koolyk Department of Computer Science Hebrew University Jerusalem, Israel aaron.koolyk@mail.huji.ac.il Geoffrey Wolfer JSPS International Research Fellow Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Tokyo, Japan geo-wolfer@m2.tuat.ac.jp |
| 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. |