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..
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Authors: Sanghack Lee, Elias Bareinboim
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate several properties of the class of mixed policies and provide an efficient and effective characterization, including optimality and non-redundancy. Specifically, we introduce a graphical criterion to identify unnecessary contexts for a set of actions, leading to a natural characterization of non-redundancy of mixed policies. We then derive sufficient conditions under which one strategy can dominate the other with respect to their maximum achievable expected rewards (optimality). This characterization leads to a fundamental understanding of the space of mixed policies and a possible refinement of the agent s strategy so that it converges to the optimum faster and more robustly. |
| Researcher Affiliation | Academia | Sanghack Lee Elias Bareinboim Causal Artificial Intelligence Laboratory Columbia University EMAIL |
| Pseudocode | Yes | We provide an efficient algorithm for obtaining a unique, maximal, non-redundant MPS (nr-mps, Alg. 2) of a given MPS in Appendix E [28]. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets for training or provide information about their availability. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setups involving training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers that would be needed to replicate experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setups, hyperparameters, or training configurations. |