Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Tractable Optimality in Episodic Latent MABs
Authors: Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,665 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,319 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Yonathan Efroni Meta, New York EMAIL Constantine Caramanis The University of Texas at Austin EMAIL Shie Mannor Technion, NVIDIA EMAIL, EMAIL |
| 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. |