mlsanityData to 7 Oct 2026

Learning Convex Optimization Models

arXiv 2006.04248 ↗First posted 7 Jun 2020cs.LG stat.MLcited by 16

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Abstract (arXiv)

A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2020.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 4; 2025: 5; 2026: 2.

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  3. 2022Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]cited by 13
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  6. 2025Deep Unfolding: Recent Developments, Theory, and Design Guidelinescited by 7
  7. 2025Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizerscited by 4
  8. 2024Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functionscited by 2
  9. 2023Learning the Efficient Frontiercited by 1
  10. 2023Integrating Symbolic Reasoning into Neural Generative Models for Design Generationcited by 1
  11. 2024OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learningcited by 1
  12. 2025Leveraging Analytic Gradients in Provably Safe Reinforcement Learningcited by 1
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  3. 2026DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programmingcited by 0
  4. 2026Disciplined Bilevel Programmingcited by 0