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].
Outlier Robust Mean Estimation with Subgaussian Rates via Stability
Authors: Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of outlier robust high-dimensional mean estimation under a finite covariance assumption, and more broadly under finite low-degree moment assumptions. We consider a standard stability condition from the recent robust statistics literature and prove that, except with exponentially small failure probability, there exists a large fraction of the inliers satisfying this condition. As a corollary, it follows that a number of recently developed algorithms for robust mean estimation, including iterative filtering and non-convex gradient descent, give optimal error estimators with (near-)subgaussian rates. |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Ankit Pensia University of Wisconsin-Madison EMAIL |
| Pseudocode | No | The paper describes steps for algorithms in prose, such as the pre-processing step using the median-of-means principle. However, it does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks with structured code-like formatting. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses properties of distributions and samples, but does not refer to specific, named datasets (e.g., MNIST, CIFAR-10) or provide information about public access to any training data used for experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with data splits, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specifying hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specifying software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific hyperparameters or training configurations. |