Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Authors: Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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: zbu@sas.upenn.edu Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA. Email: jason.klusowski@rutgers.edu Department of Statistics, Columbia University, New York, NY 10027, USA. Email: cynthia.rush@columbia.edu Department of Statistics, University of Pennsylvania, Philadelphia, PA 19104, USA. Email: suw@wharton.upenn.edu |
| 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. |