Tractable Optimality in Episodic Latent MABs
Authors: Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, this significantly outperforms the worst-case guarantees, as well as existing practical methods. Figure 1: Per time-step rewards for increasing lengths of episodes with history-dependent policies returned after the exploration phase |
| Researcher Affiliation | Collaboration | Jeongyeol Kwon University of Wisconsin-Madison jeongyeol.kwon@wisc.edu Yonathan Efroni Meta, New York jonathan.efroni@gmail.com Constantine Caramanis The University of Texas at Austin constantine@utexas.edu Shie Mannor Technion, NVIDIA shie@ee.technion.ac.il, smannor@nvidia.com |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | No | The paper describes a theoretical framework and experiments which appear to be conducted in a simulated environment based on Gaussian reward distributions, but it does not specify a named, publicly available dataset with concrete access information (e.g., link, DOI, or citation) used for training. |
| Dataset Splits | No | The paper states training details are in the supplementary materials, but the main text does not specify dataset splits (e.g., percentages or sample counts for training, validation, or testing). |
| Hardware Specification | No | Did you include the 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 does not provide specific software names with version numbers for reproducibility in the main text. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] : in Supplementary Materials. The main text does not contain specific hyperparameters or training configurations. |