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 [1].
Truncated Variance Reduced Value Iteration
Authors: Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Researcher Affiliation | Academia | Yujia Jin Stanford University EMAIL Ishani Karmarkar Stanford University EMAIL Aaron Sidford Stanford University EMAIL Jiayi Wang Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: Sample(u, p, M, η) ... Algorithm 2: Apx Utility(u, M, η) ... Algorithm 3: TVRVI(v(0), π(0), x, α, δ) ... Algorithm 4: Offline TVRVI(ε, δ) ... Algorithm 5: Sample TVRVI(ε, δ) ... Algorithm 6: Problem Dependent TVRVI(ε, δ, V ) |
| Open Source Code | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Open Datasets | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Dataset Splits | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Hardware Specification | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Software Dependencies | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |
| Experiment Setup | No | The paper focuses on theoretical results and mathematical analysis and does not include experiments. |