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..
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator
Authors: Lior Danon, Dan Garber
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to give some demonstration for the empirical performance of our Frank-Wolfe-based algorithms, we conducted two types of experiments that closely follow those in [28] (Chapter 3) with some minor changes, which consider a Gaussian distribution with outlier contamination, and a heavy-tailed multivariate t-distribution. [...] In Figure 1 we report the distance in spectral norm (in log scale) of the iterates of the different methods form Q , and in Figure 2 we report the approximation error f(Qt) f(Q ) of the iterates (also in log scale). |
| Researcher Affiliation | Academia | Lior Danon Technion Israel Institute of Technology Haifa, Israel 3200003 EMAIL Dan Garber Technion Israel Institute of Technology Haifa, Israel 3200003 EMAIL |
| Pseudocode | Yes | Algorithm 1 Frank-Wolfe variants for approximating Tyler s M-estimator |
| Open Source Code | No | The paper states in the internal checklist that code is included or details allow reproduction, but does not provide an explicit statement or link in the main text for open-source code of their methodology. |
| Open Datasets | No | The paper describes a data generation process ('Gaussian distribution with outliers contamination' and 'heavy-tailed multivariate t-distribution') and references [28] for the experiments, but does not explicitly state that a public dataset was used with concrete access information. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits. It describes data generation and then directly evaluates performance metrics. |
| Hardware Specification | No | The paper explicitly states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]' in its checklist. |
| Software Dependencies | No | The paper mentions using 'Python s SCIPY.SPARSE.LINALG.EIGSH procedure' but does not provide specific version numbers for Python, SciPy, or any other software dependencies. |
| Experiment Setup | Yes | In both experiments we set p = 50, n = 2500, and we take the true unknown covariance to be a Toeplitz matrix with the elements Qi,j = 0.85|i j|. [...] All methods are initialized from the sample covariance (normalized to have trace equals p) [...] For all experiments we compute Tyler s estimator to high accuracy by running 250 iterations of the Fixed-point method and we denote the resulting matrix by Q . |