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].
Lattice partition recovery with dyadic CART
Authors: OSCAR HERNAN MADRID PADILLA, Yi Yu, Alessandro Rinaldo
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical findings and the effectiveness of DCART for partition recovery in simulations. In this section, we demonstrate in simulation the numerical performances of the two-step estimator for the task of partition recovery. |
| Researcher Affiliation | Academia | Oscar Hernan Madrid Padilla Department of Statistics University California, Los Angeles EMAIL Yi Yu Department of Statistics University of Warwick EMAIL Alessandro Rinaldo Department of Statistics & Data Science Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes algorithmic procedures but does not include structured pseudocode or algorithm blocks with numbered steps or code-like formatting. |
| Open Source Code | Yes | Our code can be found in https://github. com/hernanmp/Partition_recovery. |
| Open Datasets | No | The paper states, 'In each instance, the data are generated as y N(θ , σ2ILd,n).' It does not specify the use of a publicly available dataset or provide access details for the generated data. |
| Dataset Splits | No | The paper describes generating data for simulations but does not provide specific dataset split information (e.g., percentages, sample counts, or a detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | all of our experiments are done in a 2.3 GHz 8-Core Intel Core i9 machine. |
| Software Dependencies | No | The paper mentions that the code is 'by courtesy of the authors of [12]' but does not provide specific ancillary software details such as library names with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The implementation details regarding choice of tuning parameters are discussed in S6.2. λ1, η > 0 are tuning parameters. For each scenario considered, we vary the noise level as σ {0.5, 1, 1.5} and set (d, n) = (2, 27). |