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..
Truly Batch Apprenticeship Learning with Deep Successor Features
Authors: Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our model s superior performance in batch settings with both classical control tasks and a real-world clinical task of sepsis management in the ICU. |
| Researcher Affiliation | Academia | Donghun Lee , Srivatsan Srinivasan and Finale Doshi-Velez SEAS, Harvard University EMAIL, finale@seas.harvard.edu, |
| Pseudocode | Yes | Algorithm 1 Batch Max-Margin IRL |
| Open Source Code | Yes | Link to our code repository can be found in Section 4 of the Appendix. |
| Open Datasets | Yes | The input data was obtained from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-III v1.4) database [Johnson et al., 2016] and included a cohort of 17,898 patients fulfilling Sepsis-3 criteria. |
| Dataset Splits | Yes | We followed a 70 30 train-validation split in our batch data. AND We then performed a 60-20-20 train-val-test split on our dataset. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like DDQN and OpenAI Gym environments but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Parametric details of all the models are in Appendix Table 2. |