Robust Mean Estimation Without Moments for Symmetric Distributions
Authors: Gleb Novikov, David Steurer, Stefan Tiegel
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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: ε-corrupted sample y1, . . . , yn and σ > 0. Output: Location estimate ˆµ. [...] Algorithm E.4 (Filtering Algorithm). Input: ε-corrupted sample y1, . . . , yn and σ > 0. Output: Location estimate ˆµ. |
| 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. |