Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data

Yujun Zhou, Yufei Han, Haomin Zhuang, Hongyan Bao, Xiangliang Zhang
2/12/2026

No confident code implementation found for this paper. 4 possible matches can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Regularization-free Diffeomorphic Temporal Alignment Nets

93% match1 repo212026

Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds

92% match2026

Augmentation-free Dense Contrastive Distillation for Efficient Semantic Segmentation

91% match1 repo192026

Integration-free Kernels for Equivariant Gaussian Process Modelling

91% match1 repo12026

Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning

91% match2026

Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning

90% match2026

Cite this paper

@article{zhou2026attackfree,
  title  = {Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data},
  author = {Yujun Zhou and Yufei Han and Haomin Zhuang and Hongyan Bao and Xiangliang Zhang},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:5cbe0d3a62a64d6f2b5ae9086dbc7686e966e276},
  journal = {ICML 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/5cbe0d3a62a64d6f2b5ae9086dbc7686e966e276.svg)](https://codeofpaper.com/paper/5cbe0d3a62a64d6f2b5ae9086dbc7686e966e276)

Discussion