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].
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. |