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].
The Complete Lasso Tradeoff Diagram
Authors: Hua Wang, Yachong Yang, Zhiqi Bu, Weijie Su
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present extensive simulation studies to confirm the sharpness of the complete Lasso tradeoff diagram. and To better illustrate our theoretical results and understand the proof of Theorem 1(b), we present the following simulations where we fix the signals β and vary the sampling ratio and the magnitude of the noise z. |
| Researcher Affiliation | Academia | University of Pennsylvania EMAIL |
| Pseudocode | No | The paper describes theoretical concepts and proofs but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The R codes for the simulations and for plotting Lasso tradeoff Diagrams are available at https://github.com/Hua Wang-wharton/Complete Lasso Diagram. |
| Open Datasets | No | In the simulations, we fix p = 1000, k = 300, and set n = 1000, 700, and 500, respectively. Consequently, the sparsity ratio ϵ is fixed to 0.3 while the sampling ratio δ is 1, 0.7, and 0.5. We note that these are the same parameters as we used in Figure 1 and Figure 2. Across all simulations, we fix β to be a 300-sparse vector with 5 different levels of magnitudes: βj = 0.01, 0.1, 1, 10 or 100, and each level contains k/5 = 60 variables. The paper describes how data is generated for simulation, not that a publicly available dataset is used or provided with access information. |
| Dataset Splits | No | The paper describes the parameters used to generate the simulation data (e.g., p, k, n, β characteristics) and mentions averaging over 100 independent trials, but it does not provide specific train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments, only mentioning 'simulations'. |
| Software Dependencies | No | The paper mentions 'The R codes for the simulations', but it does not specify specific version numbers for R or any associated libraries or software dependencies. |
| Experiment Setup | Yes | In the simulations, we fix p = 1000, k = 300, and set n = 1000, 700, and 500, respectively. ... We fix β to be a 300-sparse vector with 5 different levels of magnitudes: βj = 0.01, 0.1, 1, 10 or 100, and each level contains k/5 = 60 variables. ... We plot the Lasso tradeoff curves for 8 levels of σ in Figure 3. ... The FDP was obtained by averaging over 100 independent trials. |