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..
Efficient Symmetric Norm Regression via Linear Sketching
Authors: Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical Evaluation. In Section E of the supplementary material, we test our algorithms on real datasets. Our empirical results quite clearly demonstrate the practicality of our methods. |
| Researcher Affiliation | Academia | Zhao Song University of Washington, Ruosong Wang Carnegie Mellon University, Lin F. Yang University of California, Los Angeles, Hongyang Zhang Toyota Technological Institute at Chicago, Peilin Zhong Columbia University |
| Pseudocode | Yes | Figure 1: Algorithm for Orlicz norm regression |
| Open Source Code | No | The paper states that empirical evaluation was performed (Section E of supplementary material) but does not provide any explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions testing algorithms on 'real datasets' in Section E of the supplementary material, but does not specify the datasets or provide concrete access information (link, DOI, citation with authors/year, or specific names of well-known public datasets) in the main paper. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, such as concrete hyperparameter values, training configurations, or system-level settings, in the main text. |