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Fair and Accurate Regression: Strong Formulations and Algorithms.

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Τίτλος: Fair and Accurate Regression: Strong Formulations and Algorithms.
Συγγραφείς: Deza, Anna, Gómez, Andrés, Atamtürk, Alper
Πηγή: INFORMS Journal on Optimization; Summer2026, Vol. 8 Issue 3, p206-229, 24p
Θεματικοί όροι: Regression analysis, Mixed integer linear programming, Branch & bound algorithms, Least squares, Convex sets, Logistic regression analysis, Optimization algorithms
Περίληψη: This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially solvable single-factor and single-observation subproblems as building blocks and derive their closed convex hull descriptions. Strong formulations obtained for the general fair regression problem in this manner are utilized to solve the problem with a branch-and-bound algorithm exactly or as a relaxation to produce fair and accurate models rapidly. Moreover, to handle large-scale instances, we develop a coordinate descent algorithm motivated by the convex-hull representation of the single-factor fair regression problem to improve a given solution efficiently. Numerical experiments conducted on fair least squares and fair logistic regression problems show competitive statistical performance with state-of-the-art methods while significantly reducing training times. Funding: This research was supported, in part, by the National Science Foundation AI Institute for Advances in Optimization [Award 2112533, AFOSR Award FA9550-24-1-0086, and ONR Award N00014-24-1-2149]. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Optimization is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 195992533
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  Data: Fair and Accurate Regression: Strong Formulations and Algorithms.
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  Data: INFORMS Journal on Optimization; Summer2026, Vol. 8 Issue 3, p206-229, 24p
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  Data: <searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Branch+%26+bound+algorithms%22">Branch & bound algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+sets%22">Convex sets</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink>
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  Data: This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially solvable single-factor and single-observation subproblems as building blocks and derive their closed convex hull descriptions. Strong formulations obtained for the general fair regression problem in this manner are utilized to solve the problem with a branch-and-bound algorithm exactly or as a relaxation to produce fair and accurate models rapidly. Moreover, to handle large-scale instances, we develop a coordinate descent algorithm motivated by the convex-hull representation of the single-factor fair regression problem to improve a given solution efficiently. Numerical experiments conducted on fair least squares and fair logistic regression problems show competitive statistical performance with state-of-the-art methods while significantly reducing training times. Funding: This research was supported, in part, by the National Science Foundation AI Institute for Advances in Optimization [Award 2112533, AFOSR Award FA9550-24-1-0086, and ONR Award N00014-24-1-2149]. [ABSTRACT FROM AUTHOR]
– Name: Abstract
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  Group: Ab
  Data: <i>Copyright of INFORMS Journal on Optimization is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1287/ijoo.2025.0063
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 206
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        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Branch & bound algorithms
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      – SubjectFull: Least squares
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      – SubjectFull: Convex sets
        Type: general
      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
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      – TitleFull: Fair and Accurate Regression: Strong Formulations and Algorithms.
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            – D: 01
              M: 07
              Text: Summer2026
              Type: published
              Y: 2026
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