Halt Fast! Early Stopping for Certified Robustness

• URL: http://arxiv.org/abs/2606.27694v1
• Date: Fri, 26 Jun 2026 03:41:58 GMT
• Title: Halt Fast! Early Stopping for Certified Robustness
• Abstract summary: Randomized Smoothing (RS) provides rigorous guarantees for neural networks without architectural constraints.<n>Standard RS requires tens of thousands of model evaluations per input and forces practitioners to commit to fixed sample sizes a priori.<n>We present a novel meta-learning framework for anytime-valid certified robustness that adaptively deploys computational resources.
• Score: 22.167611337327028
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs. Standard RS requires tens of thousands of model evaluations per input and forces practitioners to commit to fixed sample sizes a priori. In this work, we present a novel meta-learning framework for anytime-valid certified robustness that adaptively deploys computational resources. By using a lightweight meta-learner to predict image-specific priors for a sequential E-process, we achieve a 20-fold reduction in sample complexity compared to traditional methods while maintaining rigorous statistical guarantees. Beyond raw efficiency, we demonstrate how anytime-validity enables adaptively allocating compute based upon application-specific risk thresholds, a form of resource triage impossible under classic certification frameworks. That this is achievable while also providing similar certification performance demonstrates that our approach provides a pathway for real-time, safety-critical certification deployments.

Related papers

• Higher-Order Certified Robustness for Regression [8.948785997740378]
  We present a novel framework for certified robust regression.<n>We derive a prediction-centered certificate that guarantees the stability of the smoothed model's prediction.<n>In particular, we demonstrate on the MNIST rotation task that utilizing gradient information yields significantly tighter robustness certificates.
  arXiv  Detail & Related papers  (2026-07-06T18:17:13Z)
• Hybrid Robustness Verification for Spatio-Temporal Neural Networks [19.026662893450425]
  Existing verification methods rely on overly conservative approximations or incur prohibitive computational costs.<n>In practice, adversarial perturbations exhibit structured spatial and temporal correlations, constrained to lower-dimensional, semantically meaningful subspaces.<n>We study robustness verification 3D CNNs processing video inputs, targeting applications in action recognition (UCF-101), autonomous driving (Udacity), medical imaging (MedMNIST)
  arXiv  Detail & Related papers  (2026-06-08T17:06:51Z)
• Efficient Credal Prediction through Decalibration [27.17944683306638]
  We propose an efficient method for credal prediction that is grounded in the notion of relative likelihood.<n>We show that our method yields credal sets with strong performance across diverse tasks.<n> Notably, we demonstrate credal prediction on models such as TabPFN and CLIP.
  arXiv  Detail & Related papers  (2026-03-09T15:30:10Z)
• Towards Anytime-Valid Statistical Watermarking [63.02116925616554]
  We develop the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference.<n>Our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-19T18:32:26Z)
• Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning [78.92934995292113]
  We propose a Confidence-Aware Asymmetric Learning (CAL) framework, which balances confidence across known and novel forgery types.<n>CAL consistently outperforms previous methods, achieving new state-of-the-art performance on both known and novel forgery attribution.
  arXiv  Detail & Related papers  (2025-12-14T12:31:28Z)
• Risk-Averse Certification of Bayesian Neural Networks [70.44969603471903]
  We propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN.<n>Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN.<n>We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method.
  arXiv  Detail & Related papers  (2024-11-29T14:22:51Z)
• Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing [12.513566361816684]
  Implicit models such as Deep Equilibrium Models (DEQs) have emerged as promising alternative approaches for building deep neural networks. Existing certified defenses for DEQs employing deterministic certification methods can not certify on large-scale datasets. We provide the first randomized smoothing certified defense for DEQs to solve these limitations.
  arXiv  Detail & Related papers  (2024-11-01T06:14:11Z)
• Automatically Adaptive Conformal Risk Control [49.95190019041905]
  We propose a methodology for achieving approximate conditional control of statistical risks by adapting to the difficulty of test samples.<n>Our framework goes beyond traditional conditional risk control based on user-provided conditioning events to the algorithmic, data-driven determination of appropriate function classes for conditioning.
  arXiv  Detail & Related papers  (2024-06-25T08:29:32Z)
• Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings [0.24578723416255752]
  We develop scalable formal verification algorithms without shifting the problem to unrealistic assumptions. In a pursuit of developing scalable formal verification algorithms without shifting the problem to unrealistic assumptions, we employ the concept of barrier certificates. We show how to solve the resulting program efficiently using sum-of-squares optimization and a Gaussian process envelope.
  arXiv  Detail & Related papers  (2024-03-15T17:32:02Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Input-Specific Robustness Certification for Randomized Smoothing [76.76115360719837]
  We propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification. ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius.
  arXiv  Detail & Related papers  (2021-12-21T12:16:03Z)
• Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness [15.38718018477333]
  We derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart. We also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability.
  arXiv  Detail & Related papers  (2020-02-17T20:54:34Z)

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.