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..
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
Authors: Yujia Jin, Aaron Sidford
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate these findings with preliminary empirical experiments. |
| Researcher Affiliation | Academia | Yujia Jin Stanford Universty EMAIL Aaron Sidford Stanford Universty EMAIL |
| Pseudocode | Yes | Algorithm 1: ISPCP(A,v,λ,γ,ϵ,δ) and Algorithm 2: Asy SVRG(M,ˆv,z0,ϵ,δ) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | Datasets. Similar to that in previous work [7, 8], we set λ = 0.5,n = 2000,d = 50 and form a matrix A=UΛ1/2V R2000 50. Here, U and V are random orthonormal matrices, and Σ contains randomly chosen singular values σi = λi. Referring to [0,λ(1 γ)] [λ(1+γ),1] as the away-fromλ region, and λ(1 γ) [0.9,1] λ(1+γ) [1,1.1] as the close-to-λ region, we generate λi differently to simulate the following three different cases: i. Eigengap-Uniform Case: generate all λi uniformly in the away-from-λ region. ii. Eigengap-Skewed Case: generate half the λi uniformly in the away-from-λ and half uniformly in the close-to-λ regions. iii. No-Eigengap-Skewed Case: uniformly generate half in [0,1], and half in the close-to-λ region. |
| Dataset Splits | No | The paper describes synthetic data generation but does not specify any training, validation, or test splits. It only states the overall dimensions of the generated data (n=2000, d=50). |
| Hardware Specification | No | The paper discusses numerical experiments but does not specify the hardware used (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions implementing algorithms (e.g., polynomial, chebyshev, lanczos, rational) but does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We set λ = 0.5,n = 2000,d = 50 and form a matrix A=UΛ1/2V R2000 50. Here, U and V are random orthonormal matrices, and Σ contains randomly chosen singular values σi = λi. |