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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Consistent regression when oblivious outliers overwhelm
Authors: Tommaso DβOrsi, Gleb Novikov, David Steurer
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,695 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,216 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 = Ο(d/Ξ±2) and achieves an error rate of O(d/Ξ±2n)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. |