Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Authors: Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate these theoretical performance bounds by numerical experiments on real-world matrices representing social networks. and In this section we present numerical experiments illustrating the performance of the row-order Schatten p-norm estimator described in Section 3.2. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, USA. 2Department of Computer Science & Applied Mathematics, The Weizmann Institute of Science, Israel. |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | The data was obtained from the Stanford Large Network Dataset Collection (Leskovec and Krevl, 2014) which was in-turn obtained from (Leskovec et al., 2007). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes how the input matrices were sparsified for experiments but not data partitioning for model training or validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes experimental parameters like the number of walks and the value of 'p' but does not provide specific details on hyperparameters, optimizer settings, or system-level training configurations typically found in machine learning experimental setups. |