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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Efficient First-Order Algorithms for Adaptive Signal Denoising
Authors: Dmitrii Ostrovskii, Zaid Harchaoui
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,045 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,654 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed procedures and their analysis are illustrated on a simulated data benchmark.In Section 5, we present numerical experiments on simulated data which complement our theoretical analysis |
| Researcher Affiliation | Academia | 1SIERRA Project-Team, INRIA Paris, Paris, France 2Department of Statistics, University of Washington, Seattle, USA. |
| Pseudocode | Yes | Algorithm 1 Fast Gradient Method; Algorithm 2 Composite Mirror Prox |
| Open Source Code | Yes | The code reproducing all our experiments is available online at https://github.com/ostrodmit/AlgoRec. |
| Open Datasets | No | The paper describes scenarios ('Random-s' and 'Coherent-s') for generating simulated data rather than using or providing access to a pre-existing public dataset. |
| Dataset Splits | No | The paper describes simulation experiments but does not specify dataset splits such as training, validation, or test sets in the context of machine learning model training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | Yes | The MOSEK optimization toolbox for MATLAB manual. Version 7.0, 2013. |
| Experiment Setup | No | The paper describes the generation of synthetic signals and parameters like 'n = 100' and 'SNR', but it does not specify typical experimental setup details for machine learning models such as learning rates, batch sizes, or optimizer settings. |