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..
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Authors: Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our method yields tight confidence intervals in different settings. |
| Researcher Affiliation | Academia | 1Department of Computer Science, The University of Texas at Austin. |
| Pseudocode | Yes | Algorithm 1 Confidence Bounds for Off-Policy Evaluation; Algorithm 2 Post-hoc Diagnosis for Existing Estimators |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We use OpenAI Gym environments (Brockman et al., 2016). |
| Dataset Splits | No | The paper mentions varying the 'number of transitions n' but does not specify explicit training, validation, or test dataset splits in terms of percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'CVXPY (Diamond & Boyd, 2016; Agrawal et al., 2018)' and 'OpenAI Gym environments (Brockman et al., 2016)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The default parameters (when not varied) are: discounted factor γ = 0.95; horizon length T = 50 for Inverted-Pendulum and T = 100 for Puck-Mountain; number of episodes 20; failure probability δ = 0.10; temperature of the behavior policy τ = 1; and the feature dimension 10. |