mlsanityData to 14 Oct 2026

Transductive Robust Learning Guarantees

arXiv 2110.10602 ↗First posted 20 Oct 2021cs.LG stat.MLcited by 10

Not on the map. Why?

Abstract (arXiv)

We study the problem of adversarially robust learning in the transductive setting. For classes H of bounded VC dimension, we propose a simple transductive learner that when presented with a set of labeled training examples and a set of unlabeled test examples (both sets possibly adversarially perturbed), it correctly labels the test examples with a robust error rate that is linear in the VC dimension and is adaptive to the complexity of the perturbation set. This result provides an exponential improvement in dependence on VC dimension over the best known upper bound on the robust error in the inductive setting, at the expense of competing with a more restrictive notion of optimal robust error.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 20 Oct 2021.

Builds on

14 of 26 references.

References · 14

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  4. 2019Unlabeled Data Improves Adversarial Robustnesscited by 416
  5. 2018Adversarially Robust Generalization Requires More Datacited by 318
  6. 2019Are Labels Required for Improving Adversarial Robustness?cited by 163
  7. 2018Rademacher Complexity for Adversarially Robust Generalizationcited by 132
  8. 2019VC Classes are Adversarially Robustly Learnable, but Only Improperlycited by 83
  9. 2018Adversarial examples from computational constraintscited by 80
  10. 2020Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examplescited by 44
  11. 2020Black-box Certification and Learning under Adversarial Perturbationscited by 13
  12. 2018Adversarial Risk Bounds via Function Transformationcited by 12
2 more
  1. 2016Minimax Lower Bounds for Realizable Transductive Classificationcited by 8
  2. 2021Towards Adversarial Robustness via Transductive Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

10 papers.

  1. 2023Cross-Entropy Loss Functions: Theoretical Analysis and Applicationscited by 210
  2. 2022Self-Training: A Surveycited by 86
  3. 2022Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterizationcited by 17
  4. 2022A Characterization of Semi-Supervised Adversarially-Robust PAC Learnabilitycited by 13
  5. 2022Adversarially Robust Learning with Tolerancecited by 9
  6. 2022Adversarially Robust PAC Learnability of Real-Valued Functionscited by 8
  7. 2022Robust Empirical Risk Minimization with Tolerancecited by 6
  8. 2025PAC Learning is just Bipartite Matching (Sort of)cited by 1
  9. 2023Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejectioncited by 0
  10. 2026Adversarially Robust PAC Learning with Optimal VC Ratescited by 0