Robust Conformal Prediction with a Single Binary Certificate

• URL: http://arxiv.org/abs/2503.05239v1
• Date: Fri, 07 Mar 2025 08:41:53 GMT
• Title: Robust Conformal Prediction with a Single Binary Certificate
• Authors: Soroush H. Zargarbashi, Aleksandar Bojchevski,
• Abstract summary: Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability.<n>We propose a robust conformal prediction that produces smaller sets even with significantly lower MC samples.
• Score: 58.450154976190795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. $\sim10^4$ 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.

Related papers

• Sparse Activations as Conformal Predictors [19.298282860984116]
  We find a novel connection between conformal prediction and sparse softmax-like transformations.<n>We introduce new non-conformity scores for classification that make the calibration process correspond to the widely used temperature scaling method.<n>We show that the proposed method achieves competitive results in terms of coverage, efficiency, and adaptiveness.
  arXiv  Detail & Related papers  (2025-02-20T17:53:41Z)
• Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
  It is impossible to achieve exact, distribution-free conditional coverage in finite samples.<n>We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
  arXiv  Detail & Related papers  (2025-01-17T12:01:56Z)
• Robust Yet Efficient Conformal Prediction Sets [53.78604391939934]
  Conformal prediction (CP) can convert any model's output into prediction sets guaranteed to include the true label. We derive provably robust sets by bounding the worst-case change in conformity scores.
  arXiv  Detail & Related papers  (2024-07-12T10:59:44Z)
• Provably Robust Conformal Prediction with Improved Efficiency [29.70455766394585]
  Conformal prediction is a powerful tool to generate uncertainty sets with guaranteed coverage. adversarial examples are able to manipulate conformal methods to construct prediction sets with invalid coverage rates. We propose two novel methods, Post-Training Transformation (PTT) and Robust Conformal Training (RCT), to effectively reduce prediction set size with little overhead.
  arXiv  Detail & Related papers  (2024-04-30T15:49:01Z)
• PAC Prediction Sets Under Label Shift [52.30074177997787]
  Prediction sets capture uncertainty by predicting sets of labels rather than individual labels. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting. We evaluate our approach on five datasets.
  arXiv  Detail & Related papers  (2023-10-19T17:57:57Z)
• Practical Adversarial Multivalid Conformal Prediction [27.179891682629183]
  We give a generic conformal prediction method for sequential prediction. It achieves target empirical coverage guarantees against adversarially chosen data. It is computationally lightweight -- comparable to split conformal prediction.
  arXiv  Detail & Related papers  (2022-06-02T14:33:00Z)
• Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations [78.23408201652984]
  Top-k predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. Our work is based on randomized smoothing, which builds a provably robust classifier via randomizing an input. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image.
  arXiv  Detail & Related papers  (2020-11-15T21:34:44Z)
• Uncertainty Sets for Image Classifiers using Conformal Prediction [112.54626392838163]
  We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling.
  arXiv  Detail & Related papers  (2020-09-29T17:58:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.