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..
A Multi-Objective Approach to Mitigate Negative Side Effects
Authors: Sandhya Saisubramanian, Ece Kamar, Shlomo Zilberstein
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation of our approach shows that the proposed framework can successfully mitigate NSE and that different feedback mechanisms introduce different biases, which influence the identification of NSE. |
| Researcher Affiliation | Collaboration | 1University of Massachusetts, Amherst, MA 2Microsoft Research, Redmond, WA |
| Pseudocode | Yes | Algorithm 1 Slack Estimation ( M, Ω) |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper describes custom domains ('Boxpushing', 'Driving') with generated instances rather than referencing publicly available datasets with access information. |
| Dataset Splits | No | The paper mentions 'three-fold cross validation is performed' for hyperparameter tuning but does not specify a training/validation/test dataset split for the main experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running experiments. |
| Software Dependencies | No | Random forest regression from sklearn Python package is used for model learning. (The version number for the scikit-learn package is not specified). |
| Experiment Setup | Yes | The slack is computed using Algorithm 1 and γ = 0.95. Values averaged over 100 trials of planning and execution, along with their standard errors, are reported for the following domains. |