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..
The Generalization Error of Dictionary Learning with Moreau Envelopes
Authors: Alexandros Georgogiannis
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical study on the sample complexity of dictionary learning with general type of reconstruction losses. |
| Researcher Affiliation | Academia | 1School of Electrical and Computer Engineering, Technical University of Crete, Greece. Correspondence to: Alexandros Georgogiannis <EMAIL>. |
| Pseudocode | No | The paper is theoretical and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it state that code is released. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments, therefore it does not use or provide access information for any specific dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, thus no specific dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, so no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |