mlsanityData to 14 Oct 2026

Learning to Pivot with Adversarial Networks

arXiv 1611.01046 ↗First posted 3 Nov 2016stat.ML cs.LG cs.NEcited by 55

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Abstract (arXiv)

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain label. Similar problems occur in a scientific context where there may be a continuous family of plausible data generation processes associated to the presence of systematic uncertainties. Robust inference is possible if it is based on a pivot -- a quantity whose distribution does not depend on the unknown values of the nuisance parameters that parametrize this family of data generation processes. In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, equivalently, fairness with respect to continuous attributes) on a predictive model. The method includes a hyperparameter to control the trade-off between accuracy and robustness. We demonstrate the effectiveness of this approach with a toy example and examples from particle physics.

Key facts. Cited by 55 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2016.

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Citing papers per year: 2021: 9; 2022: 19; 2023: 11; 2024: 8; 2025: 5; 2026: 3.

55 papers.

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  1. 2021Trustworthy AI: A Computational Perspectivecited by 93
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  3. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 68
  4. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  5. 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
  6. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  7. 2023Prompt Optimization via Adversarial In-Context Learningcited by 33
  8. 2021Bump Hunting in Latent Spacecited by 19
  9. 2022Verification and search algorithms for causal DAGscited by 18
  10. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  11. 2022Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utilitycited by 12
  12. 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approachcited by 12
38 more
  1. 2023FAIRER: Fairness as Decision Rationale Alignmentcited by 12
  2. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  3. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  4. 2022neos: End-to-End-Optimised Summary Statistics for High Energy Physicscited by 10
  5. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  6. 2021Quantum-inspired event reconstruction with Tensor Networks: Matrix Product Statescited by 9
  7. 2022On the Alignment of Group Fairness with Attribute Privacycited by 9
  8. 2022Interpretable Uncertainty Quantification in AI for HEPcited by 8
  9. 2022A Fair Pricing Model via Adversarial Learningcited by 7
  10. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  11. 2022Improving Robustness of Jet Tagging Algorithms with Adversarial Trainingcited by 6
  12. 2023Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groupscited by 6
  13. 202410 Years of Fair Representations: Challenges and Opportunitiescited by 6
  14. 2022Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attackscited by 5
  15. 2023Retiring DeltaDP: New Distribution-Level Metrics for Demographic Paritycited by 4
  16. 2024FairICP: Encouraging Equalized Odds via Inverse Conditional Permutationcited by 4
  17. 2022Bias and Priors in Machine Learning Calibrations for High Energy Physicscited by 3
  18. 2024Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in MLcited by 3
  19. 2021Content Preserving Image Translation with Texture Co-occurrence and Spatial Self-Similarity for Texture Debiasing and Domain Adaptationcited by 2
  20. 2022Gradient Based Activations for Accurate Bias-Free Learningcited by 2
  21. 2022Distraction is All You Need for Fairnesscited by 2
  22. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  23. 2024Combining Machine Learning Defenses without Conflictscited by 2
  24. 2025Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimationcited by 2
  25. 2022Invariance Learning based on Label Hierarchycited by 1
  26. 2022Decorrelation with conditional normalizing flowscited by 1
  27. 2022Bias Mitigation Framework for Intersectional Subgroups in Neural Networkscited by 1
  28. 2024Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learningcited by 1
  29. 2024Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inferencecited by 1
  30. 2025Amulet: a Python Library for Assessing Interactions Among ML Defenses and Riskscited by 1
  31. 2026Unknown Unknowns: Model Misspecification in Machine Learning for Physicscited by 1
  32. 2021A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairnesscited by 0
  33. 2022Exploring the Universality of Hadronic Jet Classificationcited by 0
  34. 2022Bridging Machine Learning and Sciences: Opportunities and Challengescited by 0
  35. 2023Decorrelation using Optimal Transportcited by 0
  36. 2023Elucidate Gender Fairness in Singing Voice Transcriptioncited by 0
  37. 2023Designing Observables for Measurements with Deep Learningcited by 0
  38. 2024OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learningcited by 0

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