Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Authors: Ramji Venkataramanan, Kevin Kögler, Marco Mondelli
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal in some settings), but obtained with a much lower complexity, as the proposed algorithm does not require a computationally expensive singular value decomposition. |
| Researcher Affiliation | Academia | 1University of Cambridge, United Kingdom 2ISTA, Austria. |
| Pseudocode | No | The algorithm steps are described textually and through equations (4)-(10), but there is no distinct block explicitly labeled 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | No explicit statement about providing source code for the proposed RI-GAMP method was found. The paper only refers to code for VAMP, a baseline method, available at 'https://sourceforge.net/projects/gampmatlab/'. |
| Open Datasets | No | The paper refers to 'synthetic data' and 'images' which include 'the sparse grayscale image considered in (Schniter & Rangan, 2014)' and 'the Haar wavelet transform of the RGB image in Figure 8a'. However, no concrete access information (e.g., specific link, DOI, or formal citation with authors and year for a publicly available dataset) is provided for these images or synthetic data generation parameters. |
| Dataset Splits | No | The paper mentions running experiments for '10 independent runs' but does not specify any training, validation, or test dataset splits or partitioning methodologies. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Pywavelets: A python package for wavelet analysis' and references MATLAB in the context of VAMP implementation, but does not provide specific version numbers for any software dependencies required for reproduction. |
| Experiment Setup | Yes | We implement the RI-GAMP given in (4)-(5), with initialization s1 = y and x1 = ATs1. The denoisers ft and ht+1, for t 1, are given by (28)-(29). The parameter λ is taken to be 1/6, which is close to the actual sparsity of the image given by 8645/50625; the parameter σ2 is taken to be 1/λ, which gives E{X2 } = 1. |