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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Authors: Aviv Rosenberg, Yishay Mansour
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The algorithms are fairly simple, and the main challenge is the analysis of the regret and computational complexity. |
| Researcher Affiliation | Collaboration | Aviv Rosenberg Tel Aviv University, Israel EMAIL Yishay Mansour Tel Aviv University, Israel and Google Research, Israel EMAIL |
| Pseudocode | Yes | The efficient implementation of this algorithm is similar to the one of the original UC-O-REPS algorithm, and is described in details in the supplementary material (together with full pseudo-code). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions pseudocode in supplementary material, but not executable source code. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no dataset splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |