mlsanityData to 14 Oct 2026

Improving Learning-to-Defer Algorithms Through Fine-Tuning

arXiv 2112.10768 ↗First posted 18 Dec 2021cs.LG cs.AI cs.HCcited by 11

Not on the map. Why?

Abstract (arXiv)

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 18 Dec 2021.

Builds on

10 of 21 references.

References · 10

  1. 2016Wide Residual Networkscited by 3,070
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  3. 2020Consistent Estimators for Learning to Defer to an Expertcited by 271
  4. 2019Human uncertainty makes classification more robustcited by 235
  5. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 233
  6. 2020Learning to Complement Humanscited by 124
  7. 2019The Algorithmic Automation Problem: Prediction, Triage, and Human Effortcited by 101
  8. 2021Differentiable Learning Under Triagecited by 76
  9. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 72
  10. 2020Classification Under Human Assistancecited by 42

Built on by

Cited by year (2026 to 14 Oct)

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

11 papers.

  1. 2023Who Should Predict? Exact Algorithms For Learning to Defer to Humanscited by 70
  2. 2022Sample Efficient Learning of Predictors that Complement Humanscited by 46
  3. 2023Principled Approaches for Learning to Defer with Multiple Expertscited by 38
  4. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  5. 2023Learning to Defer with Limited Expert Predictionscited by 25
  6. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  7. 2026Consistent Learning-to-Defer with Expert-Conditional Advicecited by 3
  8. 2026Optimized Deferral for Imbalanced Settingscited by 2
  9. 2025Bayesian Inference for Correlated Human Experts and Classifierscited by 1
  10. 2025Learning To Defer To A Population With Limited Demonstrationscited by 0
  11. 2026A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferralcited by 0