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..
Robust Mean Estimation Without Moments for Symmetric Distributions
Authors: Gleb Novikov, David Steurer, Stefan Tiegel
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of robustly estimating the mean or location parameter without moment assumptions. Known computationally efficient algorithms rely on strong distributional assumptions, such as sub-Gaussianity, or (certifiably) bounded moments. Moreover, the guarantees that they achieve in the heavy-tailed setting are weaker than those for sub-Gaussian distributions with known covariance. In this work, we show that such a tradeoff, between error guarantees and heavy-tails, is not necessary for symmetric distributions. We show that for a large class of symmetric distributions, the same error as in the Gaussian setting can be achieved efficiently. |
| Researcher Affiliation | Academia | Gleb Novikov Department of Computer Science ETH Zurich David Steurer Department of Computer Science ETH Zurich Stefan Tiegel Department of Computer Science ETH Zurich |
| Pseudocode | Yes | Algorithm D.3 (Filtering Algorithm). Input: ฮต-corrupted sample y1, . . . , yn and ฯ > 0. Output: Location estimate หยต. [...] Algorithm E.4 (Filtering Algorithm). Input: ฮต-corrupted sample y1, . . . , yn and ฯ > 0. Output: Location estimate หยต. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies involving datasets or training. No information about dataset availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies. It does not mention any dataset splits (training, validation, or testing). |
| Hardware Specification | No | The paper is theoretical and does not report on experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments. It does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |