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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Statistical Query Lower Bounds for List-Decodable Linear Regression
Authors: Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a Statistical Query (SQ) lower bound of dpoly(1/α) for this problem. Our SQ lower bound qualitatively matches the performance of previously developed algorithms, providing evidence that current upper bounds for this task are nearly best possible.The proof of Theorem 2.5 is the bulk of the technical work of this paper and is deferred to Section 3. |
| Researcher Affiliation | Collaboration | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Ankit Pensia University of Wisconsin-Madison EMAIL Thanasis Pittas University of Wisconsin-Madison EMAIL Alistair Stewart Web 3 Foundation EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for a methodology described. This is a theoretical paper focused on lower bounds. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets, thus no information about training datasets or their public availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies, therefore, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper focused on mathematical proofs and lower bounds, and as such, it does not include details about an experimental setup or hyperparameters. |