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].
Convergence rates of a partition based Bayesian multivariate density estimation method
Authors: Linxi Liu, Dangna Li, Wing Hung Wong
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also validate the theoretical results on a variety of simulated data sets. The results are further validated in Section 4 by several experiments. 4 Simulation |
| Researcher Affiliation | Academia | Linxi Liu Department of Statistics Columbia University EMAIL Dangna Li ICME Stanford University EMAIL Wing Hung Wong Department of Statistics Stanford University EMAIL |
| Pseudocode | No | The paper describes the methodology in prose and does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to a source code repository. |
| Open Datasets | No | The paper uses 'simulated data sets' (Section 4) generated according to specified distributions rather than external, publicly available datasets with concrete access information. |
| Dataset Splits | No | The paper describes running experiments with varying sample sizes and repetitions (e.g., 'sample size increase from 10^2 to 10^5', 'repeat the experiment 10 times') but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries or tools used in the experiments. |
| Experiment Setup | No | The paper describes the setup for data generation in the simulation section but does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or optimizer settings for the density estimation method. |