mlsanityData to 7 Oct 2026

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible

arXiv 2101.05360 ↗First posted 13 Jan 2021cs.LG cs.AIcited by 19

On the map: Mixture-of-experts → MoE theory and gating functions

Abstract (arXiv)

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance. Our model exhibits an interpretable gating function that provides information on when human rules should be followed or avoided. The gating function is maximized for using human-based rules, and classification errors are minimized. We propose solving a coupled multi-objective problem with convex subproblems. We develop approximate algorithms and study their performance and convergence. Finally, we demonstrate the utility of Preferential MoE on two clinical applications for the treatment of Human Immunodeficiency Virus (HIV) and management of Major Depressive Disorder (MDD).

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts. Builds on 12 of its 37 references that are in the map. First posted on arXiv 13 Jan 2021.

Builds on

12 of 37 references.

References · 12

  1. 2020Consistent Estimators for Learning to Defer to an Expertcited by 262
  2. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 228
  3. 2016Harnessing Deep Neural Networks with Logic Rulescited by 135
  4. 2020Learning to Complement Humanscited by 123
  5. 2019The Algorithmic Automation Problem: Prediction, Triage, and Human Effortcited by 101
  6. 2019Expert-Augmented Machine Learningcited by 12
  7. 2016Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applicationscited by 10
  8. 2017Learning Credible Modelscited by 8
  9. 2017Gradient descent with nonconvex constraints: local concavity determines convergencecited by 7
  10. 2016Knowledge Enhanced Hybrid Neural Network for Text Matchingcited by 5
  11. 2018Machine Learning with Abstention for Automated Liver Disease Diagnosiscited by 5
  12. 2019KINN: Incorporating Expert Knowledge in Neural Networkscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 7; 2025: 3; 2026: 3.

19 papers.

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  1. 2023Who Should Predict? Exact Algorithms For Learning to Defer to Humanscited by 68
  2. 2022Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensemblescited by 59
  3. 2022Sample Efficient Learning of Predictors that Complement Humanscited by 46
  4. 2023Principled Approaches for Learning to Defer with Multiple Expertscited by 38
  5. 2024Learning to Defer to a Population: A Meta-Learning Approachcited by 26
  6. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  7. 2022Interpretable Mixture of Expertscited by 17
  8. 2025Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defercited by 16
  9. 2025Budgeted Multiple-Expert Deferralcited by 11
  10. 2021Learning-to-defer for sequential medical decision-making under uncertaintycited by 10
  11. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  12. 2024Learning to Complement and to Defer to Multiple Userscited by 7
7 more
  1. 2024How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?cited by 4
  2. 2026Mixture of Concept Bottleneck Expertscited by 2
  3. 2026Optimized Deferral for Imbalanced Settingscited by 2
  4. 2024Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosiscited by 1
  5. 2025MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Modelcited by 1
  6. 2026Calibrating conditional riskcited by 1
  7. 2024Towards Integrating Personal Knowledge into Test-Time Predictionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study
  2. 2026A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning
  3. 2024On Least Square Estimation in Softmax Gating Mixture of Experts
  4. 2025Convergence Rates for Softmax Gating Mixture of Experts
  5. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization
  6. 2019Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
  7. 2023Improving Expert Specialization in Mixture of Experts
  8. 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts