Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
List-Decodable Mean Estimation in Nearly-PCA Time
Authors: Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a new algorithm for bounded covariance distributions with optimal sample complexity and near-optimal error guarantee, running in nearly-PCA time. and This work does not present any direct foreseeable societal consequence, as it is a result primarily of theoretical interest to the community. |
| Researcher Affiliation | Collaboration | Ilias Diakonikolas Department of Computer Science University of Wisconsin, Madison Madison, WI 53706 EMAIL; Daniel M. Kane Department of Computer Science University of California, San Diego La Jolla, CA 92093 EMAIL; Daniel Kongsgaard Department of Mathematics University of California, San Diego La Jolla, CA 92093 EMAIL; Jerry Li Microsoft Research Redmond, WA 98052 EMAIL; Kevin Tian Department of Computer Science Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | Yes | Algorithm 1 SIFT(T, δ) |
| Open Source Code | No | The paper states 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]' and does not provide any links or statements about open-source code availability. |
| Open Datasets | No | The paper does not mention any specific publicly available or open datasets used for training or evaluation, nor does it provide access information. |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments with data, thus it does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]', indicating no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments, thus it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments, thus it does not provide specific experimental setup details like hyperparameters or training configurations. |