f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
Authors: Sina Baharlouei, Shivam Patel, Meisam Razaviyayn
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments provide an extensive examination of various f-divergences and their suitability as regularizers and also show the consistency of our method across all batch sizes in contrast to existing benchmarks. Similar experiments are carried out for robust training on varying amounts of distributional shifts in data. |
| Researcher Affiliation | Academia | University of Southern California (baharlou,razaviya@usc.edu) Department of Electrical Engineering, IIT Bombay (shivamapatel2002@gmail.com) |
| Pseudocode | Yes | Algorithm 1 Stochastic Gradient Descent-Ascent (SGDA) for f-FERM |
| Open Source Code | Yes | An efficient stochastic implementation of f-FERM is publicly available 1. 1https://github.com/optimization-for-data-driven-science/f-FERM |
| Open Datasets | Yes | Adult dataset (Becker & Kohavi, 1996) |
| Dataset Splits | No | The paper mentions training and test data but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) in the main text needed to reproduce the data partitioning. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were provided. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | To run Algorithm 1, we set ηθ and ηα to 10 5 and 10 6 respectively in all experiments. Further, by changing λ, we get different points in the trade-off curve between accuracy and fairness. |