Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
Authors: Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 papadig@cs.utexas.edu Constantine Caramanis Department of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu Sanjay Shakkottai Department of Electrical and Computer Engineering The University of Texas at Austin sanjay.shakkottai@utexas.edu |
| 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. |