This paper proposes to leverage the emerging~learning techniques and devise a
multi-agent online source {seeking} algorithm under unknown environment. Of
particular significance in our problem setups are: i) the underlying
environment is not only unknown, but dynamically changing and also perturbed by
two types of non-stochastic disturbances; and ii) a group of agents is deployed
and expected to cooperatively seek as many sources as possible.
Correspondingly, a new technique of discounted Kalman filter is developed to
tackle with the non-stochastic disturbances, and a notion of confidence bound
in polytope nature is utilized~to aid the computation-efficient cooperation
among~multiple agents. With standard assumptions on the unknown environment as
well as the disturbances, our algorithm is shown to achieve sub-linear regrets
under the two~types of non-stochastic disturbances; both results are comparable
to the state-of-the-art. Numerical examples on a real-world pollution
monitoring application are provided to demonstrate the effectiveness of our
algorithm.

本文提出了一种利用新兴学习技术的多智能体在线源搜索算法，解决了环境动态不确定性和两种非随机扰动的问题，并展示了该算法在真实世界应用中的有效性。

学习寻找：面对非随机干扰的多智能体在线资源寻找

Learning to Seek: Multi-Agent Online Source Seeking Against  Non-Stochastic Disturbances

We propose an approach to estimate the number of samples required for a model
to reach a target performance. We find that the power law, the de facto
principle to estimate model performance, leads to large error when using a
small dataset (e.g., 5 samples per class) for extrapolation. This is because
the log-performance error against the log-dataset size follows a nonlinear
progression in the few-shot regime followed by a linear progression in the
high-shot regime. We introduce a novel piecewise power law (PPL) that handles
the two data regimes differently. To estimate the parameters of the PPL, we
introduce a random forest regressor trained via meta learning that generalizes
across classification/detection tasks, ResNet/ViT based architectures, and
random/pre-trained initializations. The PPL improves the performance estimation
on average by 37% across 16 classification and 33% across 10 detection
datasets, compared to the power law. We further extend the PPL to provide a
confidence bound and use it to limit the prediction horizon that reduces
over-estimation of data by 76% on classification and 91% on detection datasets.

本文提出了一种用分段幂律（Piecewise Power Law，PPL）来估算模型达到目标性能所需样本数量的方法，并通过随机森林回归器进行元学习来估算 PPL 的参数，从而显著提高了对模型性能的估计效果，同时提出了一种置信区间使得预测更加准确。

一种元学习方法用于预测性能和数据需求

A Meta-Learning Approach to Predicting Performance and Data Requirements

In this paper, we consider the problem of Gaussian process (GP) optimization
with an added robustness requirement: The returned point may be perturbed by an
adversary, and we require the function value to remain as high as possible even
after this perturbation. This problem is motivated by settings in which the
underlying functions during optimization and implementation stages are
different, or when one is interested in finding an entire region of good inputs
rather than only a single point. We show that standard GP optimization
algorithms do not exhibit the desired robustness properties, and provide a
novel confidence-bound based algorithm StableOpt for this purpose. We
rigorously establish the required number of samples for StableOpt to find a
near-optimal point, and we complement this guarantee with an
algorithm-independent lower bound. We experimentally demonstrate several
potential applications of interest using real-world data sets, and we show that
StableOpt consistently succeeds in finding a stable maximizer where several
baseline methods fail.

考虑高斯过程优化问题，在最大化函数时增加健壮性，提出基于置信界限的 StableOpt 算法，该算法在真实数据集上的实验表明可以成功找到稳定的极大值点。