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 [1].
Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment
Authors: Tieliang Gong, Yuxin Dong, Hong Chen, Wei Feng, Bo Dong, Chen Li6721-6728
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper concerns the statistical property of regularized modal regression (RMR) within an important dependence structure Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. This section mainly concerns the theoretical property of regularized modal regression for Markov-dependent observations. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Xi an Jiaotong University, Xi an 710049, China 2Key Laboratory of Intelligent Networks and Network Security, Ministry of Education, Xi an 710049, China 3College of Science, Huazhong Agriculture University, Wuhan 430070, China 4School of Continuing Education, Xi an Jiaotong University, Xi an,710049, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental data or mention any specific datasets, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |