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..
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
Authors: Daniel Russo
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper develops a very different proof strategy and provides a worst-case regret bound for RLSVI applied to tabular ο¬nite-horizon MDPs. |
| Researcher Affiliation | Academia | Daniel Russo Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1: RLSVI for Tabular, Finite Horizon, MDPs |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on tabular finite-horizon MDPs as a problem formulation, rather than using specific publicly available datasets for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations with specific versioned dependencies. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithmic analysis and proofs, therefore it does not describe an experimental setup with hyperparameters or training configurations. |