Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal subsampling for high-dimensional partially linear models via machine learning methods
Authors: Yujing Shao, Lei Wang, Heng Lian, Haiying Wang
JMLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 68,583 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,674 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation studies and an empirical analysis of the Physicochemical Properties of Protein Tertiary Structure dataset demonstrate the superior performance of our subsample estimators. |
| Researcher Affiliation | Academia | Yujing Shao EMAIL Lei Wang EMAIL School of Statistics and Data Science KLMDASR, LEBPS and LPMC Nankai University, China; Heng Lian EMAIL Department of Mathematics City University of Hong Kong, China; Haiying Wang EMAIL Department of Statistics University of Connecticut, U.S.A. |
| Pseudocode | Yes | Algorithm 1 Two-step Neyman-orthogonal score subsampling algorithm for PLMs |
| Open Source Code | No | The text discusses the source code of a third-party tool or platform that the authors used (e.g., R packages glmnet, gbm, and randomForest), but does not provide their own implementation code for the methodology described in this paper. |
| Open Datasets | Yes | Physicochemical Properties of Protein Tertiary Structure (PTS) dataset, available from the UCI Machine Learning Repository at https://archive.ics.uci.edu/dataset/265/physicochemical+properties+of+protein+tertiary+structure. |
| Dataset Splits | Yes | the full-data DML estimator ˆθF with two-fold random partition; D p,1 and D p,2 are two non-overlapping chunks of equal size r0/2 from D p, the penalized ML estimators mp k and lp k are acquired using D p\D p,k according to (6)-(7) for k = 1, 2, respectively. |
| Hardware Specification | No | The paper discusses computational time but does not provide any specific hardware details such as GPU/CPU models or processor types. |
| Software Dependencies | Yes | In this paper, we consider three ML methods: Lasso, Gradient boosted machines (Gbm), and Random forest (Rf), which are implemented in R packages glmnet (Friedman et al., 2010), gbm (Greenwell et al., 2022), and random Forest (Liaw and Wiener, 2002), respectively. |
| Experiment Setup | Yes | The full data size is set to n = 10^6, with the true parameter θ0 = (1, 1, 1, 1)T, p = 4, and q = 200 or 600. We set r0 = 600 and r = 600, 800, 1000, 1200. We consider the following three forms of g0( ): ... where γ0 = (γ01, . . . , γ0s, 0, . . . , 0)T Rq with γ0j = 0.4(1 + j/2s) and s = 10. All the simulation results are based on 500 replications. |