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..
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Authors: Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we provide extensive simulations to demonstrate the robustness of our policy. |
| Researcher Affiliation | Collaboration | 1Department of Management Science & Engineering, Stanford University 2IBM research and Stern School of Business, New York University. |
| Pseudocode | Yes | Algorithm 1 Distributionally Robust Policy Evaluation |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of source code. |
| Open Datasets | No | The paper describes a simulation environment for data generation but does not provide access information (link, DOI, citation) to a publicly available or open dataset. For example: 'The feature vectors Xi R10 are independently and uniformly drawn from [0, 1]10.' |
| Dataset Splits | Yes | We first test the convergence of different estimators for δ = 0.2 and three different sizes of dataset: n = 103, 104, 105. |
| Hardware Specification | No | No explicit hardware specifications (e.g., GPU/CPU models, memory details) were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | We fix δ = 0.1 and the size of training set is n = 3000, and the policy class is depth-3 trees. |