论文标题
有效地学习对手的稳健半空间和噪音
Efficiently Learning Adversarially Robust Halfspaces with Noise
论文作者
论文摘要
我们研究在与分布无关的环境中学习对抗性稳健半空间的问题。在可实现的环境中，我们为对抗性扰动集提供了必要和充分的条件，其中半空间有效地可学习。在存在随机标签噪声的情况下，我们就任何$ \ ell_p $ perturbation提供了一个简单的计算有效算法。
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions on the adversarial perturbation sets under which halfspaces are efficiently robustly learnable. In the presence of random label noise, we give a simple computationally efficient algorithm for this problem with respect to any $\ell_p$-perturbation.
