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..

Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs

Authors: Long-Fei Li, Peng Zhao, Zhi-Hua Zhou

NeurIPS 2024 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This is a theoretical paper and does not include experiments.
Researcher Affiliation Academia Long-Fei Li, Peng Zhao, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China
Pseudocode Yes Algorithm 1 OOPE, Algorithm 2 Comp Conf Set
Open Source Code No The paper does not provide an explicit statement about releasing source code or a link to a code repository. The NeurIPS checklist also indicates 'NA' for this question, implying no code is provided.
Open Datasets No This is a theoretical paper and does not include experiments or datasets.
Dataset Splits No This is a theoretical paper and does not include experiments or dataset splits.
Hardware Specification No This is a theoretical paper and does not include experiments; therefore, no hardware specifications are mentioned.
Software Dependencies No This is a theoretical paper and does not include experiments; therefore, no specific software dependencies with version numbers are mentioned.
Experiment Setup No This is a theoretical paper and does not include experiments; therefore, no experimental setup details like hyperparameters or training settings are provided.