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..
Streaming Principal Component Analysis in Noisy Setting
Authors: Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Experimental Results |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, Baltimore, USA. |
| Pseudocode | No | The paper refers to 'Algorithm 2 of (Warmuth & Kuzmin, 2008)' but does not include its own pseudocode or clearly labeled algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | We evaluate empirical performance of our algorithms with missing data (MGDMD, Oja-MD) and partial observations (MGD-PO, Oja PO) on two real datasets, MNIST (Le Cun et al., 1998) and XRMB (Westbury, 1994)... |
| Dataset Splits | Yes | The initial learning rate η0 is chosen using cross validation on a held-out set. |
| Hardware Specification | No | The paper discusses computational complexity and runtime, but does not provide specific details on the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper discusses various algorithms and methods (e.g., MGD, Oja's algorithm) but does not list specific software dependencies with their version numbers required to replicate the experiments. |
| Experiment Setup | Yes | The learning rate for variants of MGD and Oja s algorithm is set to ηt = η0/t, for MGD-PO to ηt = r2η0/t, and for MGDMD to ηt = q2 η0/t. The initial learning rate η0 is chosen using cross validation on a held-out set. |