Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Authors: Jingqiu Ding, Samuel Hopkins, David Steurer
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical Experiments: We test our algorithms on synthetic data random matrices (and tensors) with hundreds of rows and columns empirically demonstrating the improvement over vanilla PCA. |
| Researcher Affiliation | Academia | Jingqiu Ding ETH Zurich jding@ethz.ch Samuel B. Hopkins UC Berkeley hopkins@berkeley.edu David Steurer ETH Zurich dsteurer@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1: Algorithm for evaluating self-avoiding walk matrix |
| Open Source Code | No | The paper describes the algorithms and their evaluation but does not provide any specific links or explicit statements about releasing the source code for the described methodology. |
| Open Datasets | No | The paper states: 'We test our algorithms on synthetic data random matrices (and tensors)'. It does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses 'synthetic data' but does not specify any training, validation, or test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper mentions running 'Numerical Experiments' but does not specify any particular hardware (e.g., GPU, CPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper describes algorithmic concepts and refers to techniques like 'PCA' and 'color coding' but does not provide specific software names with version numbers that were used for implementation or experiments. |
| Experiment Setup | No | The paper mentions 'Numerical Experiments' but does not provide specific details such as hyperparameter values, optimization settings, or other configurations of the experimental setup in the main text. |