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 Generalized Lasso with Nonlinear Observations and Generative Priors
Authors: Zhaoqiang Liu, Jonathan Scarlett
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theory paper primarily targeted at the research community. |
| Researcher Affiliation | Academia | Zhaoqiang Liu National University of Singapore EMAIL; Jonathan Scarlett National University of Singapore EMAIL |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and analyses; it 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. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no public datasets or access information are mentioned. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments or provide code; therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments; therefore, no experimental setup details like hyperparameters or training settings are provided. |