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..
The Noisy Power Method: A Meta Algorithm with Applications
Authors: Moritz Hardt, Eric Price
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a new robust convergence analysis of the well-known power method for computing the dominant singular vectors of a matrix that we call the noisy power method. Our result characterizes the convergence behavior of the algorithm when a significant amount noise is introduced after each matrix-vector multiplication. The noisy power method can be seen as a meta-algorithm that has recently found a number of important applications in a broad range of machine learning problems including alternating minimization for matrix completion, streaming principal component analysis (PCA), and privacy-preserving spectral analysis. Our general analysis subsumes several existing ad-hoc convergence bounds and resolves a number of open problems in multiple applications |
| Researcher Affiliation | Industry | Moritz Hardt IBM Research Almaden Eric Price IBM Research Almaden |
| Pseudocode | Yes | Figure 1: Noisy Power Method (NPM); Figure 2: Streaming Power Method (SPM); Figure 3: Private Power Method (PPM). |
| Open Source Code | No | The paper does not provide any concrete access to source code. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not describe experiments on publicly available datasets or provide access information for any datasets used in experiments. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe training, validation, or test splits for empirical experiments. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and algorithm design, not empirical experimentation, and therefore does not include specific experimental setup details like hyperparameters or training configurations. |