Summary of Fair and Accurate Regression: Strong Formulations and Algorithms, by Anna Deza et al.
Fair and Accurate Regression: Strong Formulations and Algorithms
by Anna Deza, Andrés Gómez, Alper Atamtürk
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes mixed-integer optimization methods for solving regression problems that incorporate fairness metrics. The authors develop an exact formulation for training fair regression models, leveraging polynomially-solvable subproblems as building blocks. They use a branch-and-bound algorithm to solve the problem exactly or as a relaxation, producing fair and accurate models quickly. Additionally, they design a coordinate descent algorithm to improve solutions efficiently in large-scale instances. Numerical experiments demonstrate competitive statistical performance with state-of-the-art methods while reducing training times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces new ways to make machine learning models fair and accurate. It develops special mathematical techniques to solve complex problems that combine fairness and accuracy. The authors use these techniques to train models quickly and efficiently, even for large datasets. They test their methods on various problems and show they perform well compared to other approaches. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regression
