Optimal Shrinkage of Singular Values Under Random Data Contamination
Authors: Danny Barash, Matan Gavish
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations were performed to verify the correctness of our main results. Figure 2: Left: empirical validation of the predicted critical signal level (Simulation 1). Right: Empirical validation of the optimal shrinker shape (Simulation 3). |
| Researcher Affiliation | Academia | Danny Barash School of Computer Science and Engineering Hebrew University Jerusalem, Israel danny.barash@mail.huji.ac.il Matan Gavish School of Computer Science and Engineering Hebrew University Jerusalem, Israel gavish@cs.huji.ac.il |
| Pseudocode | No | The paper describes the proposed algorithms mathematically and textually, but it does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The full Matlab code that generated the figures in this paper and in the Supporting Information is permanently available at https://purl.stanford.edu/kp113fq0838. |
| Open Datasets | No | The paper describes generating synthetic data matrices for simulations ("several independent data matrices were generated") based on a signal model, but it does not use a publicly available or open dataset for which access information is provided. |
| Dataset Splits | No | The paper describes a simulation-based approach and evaluation of theoretical predictions, but it does not specify explicit train/validation/test dataset splits or cross-validation methods for experimental reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments or simulations. |
| Software Dependencies | No | The paper mentions 'Matlab code' but does not provide specific version numbers for Matlab or any other ancillary software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | Figure 1: Left: Optimal shrinker for additive noise and missing-at-random contamination. Right: Phase plane for critical signal levels, see Section 6, Simulation 2. (β=0.3 β=0.6 β=1 shown in graph) and Figure 2, left panel, shows the amount of data singular values yi above xcritical(λ ), as a function of the fraction of missing values κ. |