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..
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Authors: Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contributions are mathematical in nature. We establish the notion of pseudo-Lipschitzness, along with a concentration inequality for random pseudo-Lipschitz functions, and random matrices, and we use our results to further the theoretical understanding of the non-convex optimization landscape arising in compressed sensing with deep generative priors. |
| Researcher Affiliation | Academia | Constantinos Daskalakis MIT EMAIL Dhruv Rohatgi MIT EMAIL Manolis Zampetakis MIT EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that would involve using a specific publicly available dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameter values or training configurations. |