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].
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
Authors: Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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 | Orestis Papadigenopoulos Department of Computer Science The University of Texas at Austin EMAIL Constantine Caramanis Department of Electrical and Computer Engineering The University of Texas at Austin EMAIL Sanjay Shakkottai Department of Electrical and Computer Engineering The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1: Randomize-Then-Interleave |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setup involving data splits, thus no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and states that no experiments were conducted, therefore no hardware specifications are provided: 'If you ran experiments... (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]' |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup section or specify hyperparameters for any experiments. |