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..
Fast recovery from a union of subspaces
Authors: Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with experiments demonstrating that our framework also leads to improved time and sample complexity empirically. |
| Researcher Affiliation | Academia | Chinmay Hegde Iowa State University |
| Pseudocode | Yes | Algorithm 1 Approximate Subspace-IHT |
| Open Source Code | No | The paper does not provide any explicit statement or link to the source code for the methodology described. |
| Open Datasets | No | The paper uses “an image of the MIT logo” and “a symmetric matrix of size 2048 × 2048” but does not provide concrete access information (link, DOI, formal citation with authors/year) for these datasets or their public availability. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (percentages, sample counts, or detailed methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions external tools like PROPACK [16] and block Krylov SVD [17] but does not provide specific ancillary software details (library or solver names with version numbers) used for its experiments. |
| Experiment Setup | Yes | SVP / IHT combined with a single iteration of a block Krylov SVD achieves the same phase transition as SVP with an exact SVD. |