Robust Tensor Decomposition with Gross Corruption
Authors: Quanquan Gu, Huan Gui, Jiawei Han
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show through numerical experiments that our theory can precisely predict the scaling behavior in practice. Experiments on synthetic datasets validate our theoretical results. |
| Researcher Affiliation | Academia | Quanquan Gu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544 qgu@princeton.edu Huan Gui Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 {huangui2,hanj}@illinois.edu |
| Pseudocode | No | Section 5 'Algorithm' describes the steps of the ADMM algorithm using mathematical equations for iterative updates, but it does not present them in a structured pseudocode block or algorithm listing format. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or providing a link to it. |
| Open Datasets | No | The paper states: 'We randomly generate low-rank tensors of dimensions n(1) = (50, 50, 20) ( results are shown in Figure 1(a, b, c)) and n(2) = (100, 100, 50)( results are shown in Figure 1(d, e, f)) for various rank (r1, r2, ..., rk).', indicating synthetic data generation without public access information. |
| Dataset Splits | No | The paper describes generating synthetic data and repeating experiments but does not provide specific details on training, validation, or test dataset splits or a cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers. |
| Experiment Setup | No | The paper mentions using ADMM and regularization parameters but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings in the main text. |