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..
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Authors: Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | numerical simulations show that the convergence is surprisingly fast. and The empirical performance of AMP in solving SLOPE is illustrated in Figure 1 and Table 1, which suggest the superiority of AMP over ISTA and FISTA |
| Researcher Affiliation | Academia | Department of Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA. Email: EMAIL Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA. Email: EMAIL Department of Statistics, Columbia University, New York, NY 10027, USA. Email: EMAIL Department of Statistics, University of Pennsylvania, Philadelphia, PA 19104, USA. Email: EMAIL |
| Pseudocode | Yes | Algorithm 1 Calibration from λ α |
| Open Source Code | No | No explicit statement or link providing access to source code for the methodology was found. |
| Open Datasets | No | The paper describes generating synthetic data for its simulations rather than using a pre-existing public dataset. |
| Dataset Splits | No | The paper uses synthetically generated data and does not specify traditional train/validation/test splits with percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | Setting of Figure 1 and Table 1: Design X is 500 1000 and has i.i.d. N(0, 1/500) entries. True signal β is elementwise i.i.d. Gaussian Bernoulli: N(0, 1) with probability 0.1 and 0 otherwise. Noise variance σ2 w = 0. A careful calibration between the thresholds θt in AMP and λ is SLOPE is used. Details in Section 2. |