A Multi-Objective Approach to Mitigate Negative Side Effects
Authors: Sandhya Saisubramanian, Ece Kamar, Shlomo Zilberstein
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |