Statistical Query Lower Bounds for List-Decodable Linear Regression

Authors: Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart

NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 ilias@cs.wisc.edu Daniel M. Kane University of California, San Diego dakane@cs.ucsd.edu Ankit Pensia University of Wisconsin-Madison ankitp@cs.wisc.edu Thanasis Pittas University of Wisconsin-Madison pittas@wisc.edu Alistair Stewart Web 3 Foundation stewart.al@gmail.com
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.