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..
On The Statistical Complexity of Offline Decision-Making
Authors: Thanh Nguyen-Tang, Raman Arora
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of the behavior policy that strictly subsumes all the previous notions of data coverage in the offline decision-making literature. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, Baltimore 21218, USA. |
| Pseudocode | Yes | Algorithm 1 Hedge for Offline Decision-Making (Of DM-Hedge) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments using a specific dataset. Therefore, it does not provide concrete access information for a publicly available or open dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not provide details about the hardware used. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not describe any specific experimental setup details like hyperparameters or system-level training settings. |