Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Authors: Qianqian Ma, Alex Olshevsky
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show our algorithm for this problem, based on rank-one matrix completion with perturbations, outperforms all other state-of-the-art methods in such an adversarial scenario.1 |
| Researcher Affiliation | Academia | Qianqian Ma Boston University maqq@bu.edu Alex Olshevsky Boston University alexols@bu.edu |
| Pseudocode | Yes | Algorithm 1 M-MSR |
| Open Source Code | Yes | 1The code is available on https://github.com/maqqbu/MMSR |
| Open Datasets | Yes | We implemented similar experiments on 17 publicly available data sets that are commonly used to evaluate the crowdsourcing algorithms. |
| Dataset Splits | No | The information is insufficient. The paper mentions using synthetic and real-world datasets for experiments but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup) in the main text. |
| Hardware Specification | No | The information is insufficient. The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The information is insufficient. The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | No | The information is insufficient. The paper mentions varying parameters of an adversarial model and refers to supplementary sections for details, but does not provide specific hyperparameter values, training configurations, or other system-level setup details in the main text. |