Iterative Least Trimmed Squares for Mixed Linear Regression
Authors: Yanyao Shen, Sujay Sanghavi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we analyze ILTS in the setting of mixed linear regression with corruptions (MLR-C). We first establish deterministic conditions (on the features etc.) under which the ILTS iterate converges linearly to the closest mixture component. We also evaluate it for the widely studied setting of isotropic Gaussian features, and establish that we match or better existing results in terms of sample complexity. |
| Researcher Affiliation | Academia | Yanyao Shen ECE Department University of Texas at Austin Austin, TX 78712 shenyanyao@utexas.edu Sujay Sanghavi ECE Department University of Texas at Austin Austin, TX 78712 sanghavi@mail.utexas.edu |
| Pseudocode | Yes | Algorithm 1 ILTS (for recovering a single component) ... Algorithm 2 GLOBAL-ILTS (for recovering all components ) |
| Open Source Code | No | The paper does not provide any explicit statements about making source code available or links to a code repository. |
| Open Datasets | No | The paper assumes a theoretical data distribution (e.g., 'xi N(0, Id)') for its analysis rather than using or referring to any publicly available or open datasets. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe any experimental training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not provide details about an experimental setup, hyperparameters, or training configurations. |