Fairness in Forecasting and Learning Linear Dynamical Systems
Authors: Quan Zhou, Jakub Marecek, Robert N. Shorten11134-11142
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and the encouraging effects of exploiting sparsity on run time. |
| Researcher Affiliation | Academia | Quan Zhou,1 Jakub Marecek,2 Robert Shorten 1,3 1 University College Dublin, Ireland 2 Czech Technical University in Prague, the Czech Republic 3 Imperial College London, UK quan.zhou@ucdconnect.ie, jakub.marecek@fel.cvut.cz, r.shorten@imperial.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available on-line at https://github.com/Quan Zhou/Fairness-in-Learning-of-LDS. |
| Open Datasets | Yes | Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and the encouraging effects of exploiting sparsity on run time. (Angwin et al. 2016) |
| Dataset Splits | No | The paper mentions 'training data' but does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits) for reproducibility. |
| Hardware Specification | Yes | on a laptop equipped by Intel Core i7 8550U at 1.80 Ghz. |
| Software Dependencies | No | The paper mentions 'ncpol2sdpa of (Wittek 2015)' and 'sdpa of (Yamashita, Fujisawa, and Kojima 2003)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The three models (25)-(27) are applied in each experiment with λ of 1, 3, and 5, respectively, as chosen by iterating over integers 1 to 10. The initial states m(s) 0 of each subgroups are 5 and 7. We set the time window to be 20 across 3 trajectories in the advantaged subgroup and 2 in disadvantaged one, i.e., |T +| = 20, |I(a)| = 3 and |I(d)| = 2. |