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..
Streaming Algorithms for High-Dimensional Robust Statistics
Authors: Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our computationally efficient algorithm makes a single pass over the data, uses near-optimal space, and matches the error guarantees of previous polynomial-time algorithms for the problem. As a corollary, we obtain streaming algorithms with near-optimal space complexity for several more complex tasks, including robust covariance estimation, robust regression, and more generally robust stochastic optimization. |
| Researcher Affiliation | Academia | 1University of Wisconsin-Madison 2University of California, San Diego. |
| Pseudocode | Yes | Algorithm 1 Robust Mean Estimation in polylog iterations and Algorithm 5 Robust Mean Estimation In Single-Pass Streaming Model |
| Open Source Code | No | No statement or link indicating open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical algorithm design and analysis, and does not involve empirical evaluation on publicly available datasets for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical algorithms and does not report on experiments with a specific experimental setup, including hyperparameters or training configurations. |