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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |