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..
Learning Low-Dimensional Metrics
Authors: Blake Mason, Lalit Jain, Robert Nowak
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experiments To validate our complexity and recovery guarantees, we ran the following simulations. We generate x1, , xn iid N(0, 1 p I), with n = 200, and K = p p d UU T for a random orthogonal matrix U 2 Rp d with unit norm columns. In Figure 2a, K has d nonzero rows/columns. In Figure 2b, K is a dense rank-d matrix. We compare the performance of nuclear norm and 1,2 regularization in each setting against an unconstrained baseline where we only enforce that K be psd. Given a fixed number of samples, each method is compared in terms of the relative excess risk, R(c K) R(K ) R(K ) , and the relative squared recovery error, kc K K k2 F , averaged over 20 trials. The y-axes of both plots have been trimmed for readability. |
| Researcher Affiliation | Academia | Lalit Jain University of Michigan Ann Arbor, MI 48109 EMAIL Blake Mason University of Wisconsin Madison, WI 53706 EMAIL Robert Nowak University of Wisconsin Madison, WI 53706 EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper states 'We generate x1, , xn iid N(0, 1 p I), with n = 200' for their simulations, indicating a self-generated dataset for which no public access information (link, DOI, citation) is provided. |
| Dataset Splits | No | The paper describes simulations but does not provide specific details regarding training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper describes running simulations but does not provide specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We generate x1, , xn iid N(0, 1 p I), with n = 200, and K = p p d UU T for a random orthogonal matrix U 2 Rp d with unit norm columns. In Figure 2a, K has d nonzero rows/columns. In Figure 2b, K is a dense rank-d matrix. ... averaged over 20 trials. ... we compute the number of samples averaged over 20 runs to achieve a relative excess risk of less than 0.1 in Figure 3. First, we fix p = 100 and increment d from 1 to 10. Then we fix d = 10 and increment p from 10 to 100 to clearly show the linear dependence of the sample complexity on d and p as demonstrated in Corollary 2.1.1. |