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..
Recovery of Sparse Signals from a Mixture of Linear Samples
Authors: Soumyabrata Pal, Arya Mazumdar
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical, an important future work will be to find interesting use cases. |
| Researcher Affiliation | Academia | 1Computer Science Department at the University of Massachusetts Amherst, Amherst, MA01003, USA. Correspondence to: Arya Mazumdar <EMAIL>, Soumyabrata Pal <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 EM(x, σ, T ) Estimate the means x, β1 , x, β2 for a query x using EM algorithm... Algorithm 8 RECOVER UNKNOWN VECTORS(σ, γ) Recover the unknown vectors β1 and β2 |
| Open Source Code | No | The paper mentions 'Some proof of concept simulation results are also in the appendix.' but does not provide any links to open-source code for the described methodology or experiments. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset. Therefore, it does not mention training data availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for its implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |