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].
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
Authors: Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the IHS with refreshed Haar/SRHT sketches against refreshed Gaussian sketches. |
| Researcher Affiliation | Academia | Jonathan Lacotte Department of Electrical Engineering Stanford University, Sifan Liu Department of Statistics Stanford University, Edgar Dobriban Department of Statistics University of Pennsylvania, Mert Pilanci Department of Electrical Engineering Stanford University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories for the described methodology. |
| Open Datasets | Yes | Second, we carry out a similar experiment with the CIFAR10 dataset, for which we consider one-vs-all classification. |
| Dataset Splits | No | The paper mentions using a 'synthetic data matrix' and 'CIFAR10 dataset', but does not explicitly state specific training, validation, or test splits by percentages, counts, or by referencing predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | For the SRHT, we use the step size µt = θ1,h/θ2,h prescribed in Theorem 4.1, where we replace ξ and γ by their finite sample approximations ξ m n. |