New algorithms tackle robust multiclass linear classification

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-18 17:09

Two new research papers published on arXiv introduce novel algorithms for multiclass linear classification under Gaussian distributions. The first paper focuses on achieving polynomial-time robust learning with dimension-independent error guarantees, addressing limitations in prior work for three or more classes. The second paper presents an efficient and noise-tolerant PAC learning algorithm for multiclass linear classifiers, even with maliciously corrupted data, offering improvements over existing methods. AI

影响 These papers introduce theoretical advancements in machine learning algorithms for multiclass classification, potentially improving efficiency and robustness in future applications.

排序理由 Two academic papers published on arXiv present new algorithms for a specific machine learning task.

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arXiv cs.LG TIER_1 English(EN) · Mingchen Ma · 2026-05-20 17:19

Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals

We study the task of agnostic learning of multiclass linear classifiers under the Gaussian distribution. Given labeled examples $(x, y)$ from a distribution over $\mathbb{R}^d \times [k]$, with Gaussian $x$-marginal, the goal is to output a hypothesis whose error is comparable to…
arXiv cs.LG TIER_1 English(EN) · Shiwei Zeng · 2026-05-18 17:09

Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers

Noise-tolerant PAC learning of linear models has been of central interests in machine learning community since the last century. In recent years, many computationally-efficient algorithms have been proposed for the problem of learning linear threshold functions under multiple noi…

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Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals

Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers

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