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..
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Authors: Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As this is a theoretical contribution, we do not envision that our direct results will have a tangible societal impact. |
| Researcher Affiliation | Collaboration | Chi Jin Princeton University EMAIL Sham M. Kakade University of Washington Microsoft Research, NYC EMAIL Akshay Krishnamurthy Microsoft Research, NYC EMAIL Qinghua Liu Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Observable Operator Model with Upper Con๏ฌdence Bound (OOM-UCB) |
| Open Source Code | No | The paper is a theoretical contribution and does not mention releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is a theoretical work focusing on algorithm design and theoretical guarantees (sample complexity), not on empirical evaluation using datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is a theoretical work and does not describe empirical experiments. Therefore, no information about dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is a theoretical work and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is a theoretical work and does not describe specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is a theoretical work and does not describe empirical experiments or their setup details such as hyperparameters or training configurations. |