mlsanityData to 7 Oct 2026

TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness

arXiv 2605.30601 ↗First posted 28 May 2026cs.LGcited by 0

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Abstract (arXiv)

Modern deep networks remain fragile under distribution shift and adversarial perturbations, often due to excessive or poorly structured input sensitivity. We introduce TASER (Task-Aware Stein Regularisation), a training-time regularisation framework derived from Langevin Stein operators. By penalising pointwise Stein residuals under the training distribution, TASER encourages geometric compatibility between predictors and data density, inducing anisotropic, data-aware smoothness. We provide theoretical links between Stein regularisation and reduced first-order shift sensitivity, develop scalable implementation variants compatible with modern architectures, and demonstrate improved robustness and stability across regression and vision benchmarks. Across CIFAR-10 experiments, TASER consistently improves the adversarial robustness of established training methods without incurring statistically significant clean-accuracy degradation.

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References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
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  12. 2017Parseval Networks: Improving Robustness to Adversarial Examplescited by 288
8 more
  1. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  2. 2021Improving Robustness using Generated Datacited by 233
  3. 2018Adversarial Risk and the Dangers of Evaluating Against Weak Attackscited by 221
  4. 2015Measuring Sample Quality with Stein's Methodcited by 94
  5. 2018Improving DNN Robustness to Adversarial Attacks using Jacobian Regularizationcited by 88
  6. 2015Analysis of classifiers' robustness to adversarial perturbationscited by 76
  7. 2026Probabilistic Inference and Learning with Stein's Methodcited by 3
  8. 2026TASTE: Task-Aware Out-of-Distribution Detection via Stein Operatorscited by 1

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