mlsanityData to 7 Oct 2026

Robust Conformal Prediction with a Single Binary Certificate

arXiv 2503.05239 ↗First posted 7 Mar 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability. Robust CP extends this guarantee to worst-case (adversarial) inputs. Existing baselines achieve robustness by bounding randomly smoothed conformity scores. In practice, they need expensive Monte-Carlo (MC) sampling (e.g. ~104 samples per point) to maintain an acceptable set size. We propose a robust conformal prediction that produces smaller sets even with significantly lower MC samples (e.g. 150 for CIFAR10). Our approach binarizes samples with an adjustable (or automatically adjusted) threshold selected to preserve the coverage guarantee. Remarkably, we prove that robustness can be achieved by computing only one binary certificate, unlike previous methods that certify each calibration (or test) point. Thus, our method is faster and returns smaller robust sets. We also eliminate a previous limitation that requires a bounded score function.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 7 Mar 2025.

Builds on

22 of 30 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  5. 2019Conformal Prediction Under Covariate Shiftcited by 511
  6. 2020Classification with Valid and Adaptive Coveragecited by 390
  7. 2022Conformal prediction beyond exchangeabilitycited by 348
  8. 2022Conformal Risk Controlcited by 290
  9. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  10. 2020Randomized Smoothing of All Shapes and Sizescited by 146
  11. 2021Learning Optimal Conformal Classifierscited by 108
  12. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
10 more
  1. 2019Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifierscited by 65
  2. 2020Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and Morecited by 56
  3. 2021Improved, Deterministic Smoothing for L_1 Certified Robustnesscited by 39
  4. 2020Certifying Confidence via Randomized Smoothingcited by 36
  5. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  6. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  7. 2024Provably Robust Conformal Prediction with Improved Efficiencycited by 27
  8. 2023Probabilistically robust conformal predictioncited by 25
  9. 2024Robust Yet Efficient Conformal Prediction Setscited by 16
  10. 2024Conformal Inductive Graph Neural Networkscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Efficient Robust Conformal Prediction via Lipschitz-Bounded Networkscited by 4
  2. 2025One Sample is Enough to Make Conformal Prediction Robustcited by 4
  3. 2025Extending Prediction-Powered Inference through Conformal Predictioncited by 1
  4. 2026Robust Conformal Intrusion Detection via Traffic-Aware Calibration and Attack-Orbit Invariancecited by 0