Adversarial training is often formulated as a min-max problem, however,
concentrating only on the worst adversarial examples causes alternating
repetitive confusion of the model, i.e., previously defended or correctly
classified samples are not defensible or accurately classifiable in subsequent
adversarial training. We characterize such non-ignorable samples as "hiders",
which reveal the hidden high-risk regions within the secure area obtained
through adversarial training and prevent the model from finding the real worst
cases. We demand the model to prevent hiders when defending against adversarial
examples for improving accuracy and robustness simultaneously. By rethinking
and redefining the min-max optimization problem for adversarial training, we
propose a generalized adversarial training algorithm called Hider-Focused
Adversarial Training (HFAT). HFAT introduces the iterative evolution
optimization strategy to simplify the optimization problem and employs an
auxiliary model to reveal hiders, effectively combining the optimization
directions of standard adversarial training and prevention hiders. Furthermore,
we introduce an adaptive weighting mechanism that facilitates the model in
adaptively adjusting its focus between adversarial examples and hiders during
different training periods. We demonstrate the effectiveness of our method
based on extensive experiments, and ensure that HFAT can provide higher
robustness and accuracy.

通过重新思考和重新定义对抗性训练的最小最大优化问题，我们提出了一种称为 HFAT 的广义对抗性训练算法。HFAT 引入了迭代演化优化策略来简化优化问题，并采用了辅助模型来揭示隐藏者，有效地结合了标准对抗性训练和隐藏者的优化方向。此外，我们还介绍了一种自适应加权机制，可以在不同的训练阶段帮助模型在对抗性示例和隐藏者之间适应地调整其关注点。我们通过大量的实验证明了我们方法的有效性，并确保 HFAT 具有更高的鲁棒性和准确性。

关注隐藏者：探索加强对抗训练的隐藏威胁

Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial  Training

Meta-learning methods have shown an impressive ability to train models that
rapidly learn new tasks. However, these methods only aim to perform well in
expectation over tasks coming from some particular distribution that is
typically equivalent across meta-training and meta-testing, rather than
considering worst-case task performance. In this work we introduce the notion
of "task-robustness" by reformulating the popular Model-Agnostic Meta-Learning
(MAML) objective [Finn et al. 2017] such that the goal is to minimize the
maximum loss over the observed meta-training tasks. The solution to this novel
formulation is task-robust in the sense that it places equal importance on even
the most difficult and/or rare tasks. This also means that it performs well
over all distributions of the observed tasks, making it robust to shifts in the
task distribution between meta-training and meta-testing. We present an
algorithm to solve the proposed min-max problem, and show that it converges to
an $\epsilon$-accurate point at the optimal rate of $\mathcal{O}(1/\epsilon^2)$
in the convex setting and to an $(\epsilon, \delta)$-stationary point at the
rate of $\mathcal{O}(\max\{1/\epsilon^5, 1/\delta^5\})$ in nonconvex settings.
We also provide an upper bound on the new task generalization error that
captures the advantage of minimizing the worst-case task loss, and demonstrate
this advantage in sinusoid regression and image classification experiments.

本文提出了一种新的元学习方法，通过解决 min-max 问题以最小化观测到的元训练任务的最大损失，可以实现任务健壮性，从而在元训练和元测试之间的任务分布变化时，都能表现出色。

任务鲁棒的模型无关元学习

Task-Robust Model-Agnostic Meta-Learning

The min-max problem, also known as the saddle point problem, is a class of
optimization problems which minimizes and maximizes two subsets of variables
simultaneously. This class of problems can be used to formulate a wide range of
signal processing and communication (SPCOM) problems. Despite its popularity,
most existing theory for this class has been mainly developed for problems with
certain special convex-concave structure. Therefore, it cannot be used to guide
the algorithm design for many interesting problems in SPCOM, where various
kinds of non-convexity arise.
In this work, we consider a block-wise one-sided non-convex min-max problem,
in which the minimization problem consists of multiple blocks and is
non-convex, while the maximization problem is (strongly) concave. We propose a
class of simple algorithms named Hybrid Block Successive Approximation (HiBSA),
which alternatingly perform gradient descent-type steps for the minimization
blocks and gradient ascent-type steps for the maximization problem. A key
element in the proposed algorithm is the use of certain regularization and
penalty sequences, which stabilize the algorithm and ensure convergence. We
show that HiBSA converges to some properly defined first-order stationary
solutions with quantifiable global rates. To validate the efficiency of the
proposed algorithms, we conduct numerical tests on a number of problems,
including the robust learning problem, the non-convex min-utility maximization
problems, and certain wireless jamming problem arising in interfering channels.

本文提出了名为 Hybrid Block Successive Approximation 的简单算法来解决一类分块非凸极小极大问题，其中最小化问题由多个块组成，而最大化问题是凸的。通过使用正则化和惩罚序列，该算法稳定收敛于一些合理定义的一阶稳定解，并在几个不同的问题上进行了数值测试，包括鲁棒学习问题、非凸极小效用最大化问题以及干扰通道中出现的某些无线干扰问题。