Batch List-Decodable Linear Regression via Higher Moments
Authors: Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Sihan Liu, Thanasis Pittas
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is predominantly theoretical and does not present any immediate societal or ethical considerations requiring special attention. Its contribution lies in advancing foundational understanding rather than influencing direct real-world applications or policy. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Wisconsin Madison, Madison, United States 2Department of Compuetr Science and Engineering, University of California San Diego, San Diego, United States 3Microsoft Research, Cambridge, England. Correspondence to: Thanasis Pittas <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Batch-List-Decode-LRegression (Informal) Require: Batch sample access to the linear regression instance, σ, R as specified in Theorem 1.3. ... Algorithm 2 Batch-List-Decode-LRegression Require: Batch sample access to the linear regression instance, and α, σ, R, k as specified in Theorem 1.3. |
| Open Source Code | No | The paper does not contain an unambiguous statement of code release or a direct link to a source-code repository for the methodology described. |
| Open Datasets | No | The paper does not explicitly state that the dataset used in the experiments is publicly available or an open dataset. It defines a theoretical distribution Dβ from which samples are drawn, rather than using a pre-existing named dataset. |
| Dataset Splits | No | The paper describes a theoretical framework and does not conduct experiments on specific datasets requiring explicit training/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithmic guarantees and proofs, thus it does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and discusses algorithms and mathematical frameworks; it does not provide details of software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithmic design and proofs, not on empirical experimental setups or hyperparameter tuning. |