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 [1].
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. |