Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Authors: Yuchen Zhang, Martin Wainwright, Michael Jordan
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the practical effectiveness of the proposed algorithm with some numerical experiments. |
| Researcher Affiliation | Academia | Yuchen Zhang YUCZHANG@EECS.BERKELEY.EDU University of California, Berkeley, CA 94720, USA |
| Pseudocode | Yes | Algorithm 1 Evaluation of Chebyshev Polynomial Algorithm 2 Randomized Algorithm for Rank Estimation |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating its own synthetic data for experiments ( |
| Dataset Splits | No | The paper describes generating synthetic data and evaluating the mean squared error over 100 independent runs of the algorithm. It does not mention or specify any train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | In order to generate the data, we choose the parameters r = 100, λ = 0.4 and σ2 = 0.1. These choices motivate the thresholds c1 = λ + σ2 = 0.5 and c2 = σ2 = 0.1 in Algorithm 2. We illustrate the behavior of the algorithm for three different choices of the degree parameter p — specifically, p ∈ {0, 1, 5} — and for a range of repetitions T ∈ {1, 2, . . . , 30}. |