Optimal Rates for Nonparametric Density Estimation under Communication Constraints
Authors: Jayadev Acharya, Clement Canonne, Aditya Vikram Singh, Himanshu Tyagi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Researcher Affiliation | Academia | Jayadev Acharya Cornell University Ithaca, USA acharya@cornell.edu Clément L. Canonne University of Sydney Sydney, Australia clement.canonne@sydney.edu.au Aditya Vikram Singh Indian Institute of Science Bangalore, India adityavs@iisc.ac.in Himanshu Tyagi Indian Institute of Science Bangalore, India htyagi@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1 Vector quantization; Algorithm 2 Single-level estimator (Players); Algorithm 3 Single-level estimator (Referee); Algorithm 4 Multi-level estimator (Players); Algorithm 5 Multi-level algorithm (Referee). |
| Open Source Code | No | The checklist explicitly states 'N/A' for questions related to including code and data for reproducing experimental results, indicating no open-source code is provided. |
| Open Datasets | No | The paper does not conduct experiments with data, so there is no mention of datasets being publicly available or open. The checklist indicates 'N/A' for all experimental details. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not report experimental results. Therefore, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not report experimental results. Consequently, it does not describe the hardware used for any computations. The checklist states 'N/A' for 'total amount of compute and the type of resources used'. |
| Software Dependencies | No | The paper does not conduct experiments, and thus does not mention any software dependencies with specific version numbers. The checklist states 'N/A' for training details. |
| Experiment Setup | No | The paper does not report experimental results, and therefore does not include details about experimental setup, such as hyperparameters or system-level training settings. The checklist states 'N/A' for training details. |