The Generalization Error of Dictionary Learning with Moreau Envelopes

Authors: Alexandros Georgogiannis

ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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 <alexandrosgeorgogiannis@gmail.com>.
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.