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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Online Learning of Eigenvectors
Authors: Dan Garber, Elad Hazan, Tengyu Ma
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,967 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,418 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we present new algorithms that avoid both issues. On one hand they do not require any expensive matrix decompositions and on the other, they guarantee regret rates with a mild dependence on the dimension at most. We extend our results to also handle non-symmetric matrices. Our main result is an online algorithm that takes the so called oracle approach and is based on the Follow the Perturbed Leader meta-algorithm. We also consider a somewhat easier stochastic setting, in which we assume that the sequence of matrices is sampled from a ๏ฌxed and unknown distribution. We present an algorithm that takes the so called iterative approach and is analogues to the Power algorithm for the of๏ฌine setting, i.e. it computes a single matrix-vector product on each iteration. |
| Researcher Affiliation | Academia | Dan Garber EMAIL Technion Israel Institute of Technology Elad Hazan EMAIL Princeton University Tengyu Ma EMAIL Princeton University |
| Pseudocode | Yes | Algorithm 1 Asymmetric to Symmetric Conversion Algorithm, Algorithm 2 Epoch Power Method, Algorithm 3 Follow the Perturbed Leader |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodologies, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments using specific datasets. Therefore, it does not provide information about public dataset availability or access. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Consequently, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup, including hyperparameters or system-level training settings. |