Consistent regression when oblivious outliers overwhelm
Authors: Tommaso D’Orsi, Gleb Novikov, David Steurer
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that consistent estimation is possible with nearly linear sample size and inverse-polynomial inlier fraction. Concretely, we show that the Huber loss estimator is consistent for every sample size n = ω(d/α2) and achieves an error rate of O(d/α2n)1/2 (both bounds are optimal up to constant factors). Our results extend to designs far beyond the Gaussian case and only require the column span of X to not contain approximately sparse vectors... |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Z urich, Switzerland. |
| Pseudocode | Yes | Algorithm 1 Multivariate linear regression iteration via median |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not report on experiments conducted using datasets. Therefore, no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with data. Thus, there is no information about training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or system-level training settings. |