Supervised learning through the lens of compression
Authors: Ofir David, Shay Moran, Amir Yehudayoff
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work continues the study of the relationship between sample compression schemes and statistical learning... We prove that in this case learnability is equivalent to compression of logarithmic sample size... This work studies statistical learning theory using the point of view of compression. The main theme in this work is establishing equivalences between learnability and compressibility... |
| Researcher Affiliation | Academia | Ofir David Department of Mathematics Technion Israel Institute of Technology ofirdav@tx.technion.ac.il Shay Moran Department of Computer Science Technion Israel Institute of Technology shaymrn@cs.technion.ac.il Amir Yehudayoff Department of Mathematics Technion Israel Institute of Technology amir.yehudayoff@gmail.com |
| Pseudocode | No | The paper is theoretical, presenting theorems and proofs without the need for pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not perform empirical studies, therefore, it does not use or refer to datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not include empirical experiments, thus no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is purely theoretical and does not implement or rely on specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |