我们提出了一个强大的框架，以执行线性回归，而功能中缺少条目。通过考虑椭圆形数据分布，特别是多元正常模型，我们能够为缺失条目制定分布并提出一个强大的框架，这最大程度地减少了由于缺失数据的不确定性而造成的最严重的情况。我们表明，所提出的公式自然考虑了不同变量之间的依赖性，最终减少了凸面程序，可以为其提供自定义和可扩展的求解器。除了提供此类求解器的详细分析外，我们还渐近地分析了所提出的框架的行为，并进行了技术讨论以估算所需的输入参数。我们通过对合成，半合成和真实数据进行的实验进行补充，并展示提出的配方如何提高预测准确性和鲁棒性，并优于竞争技术。

Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. In order to obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target's location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by means of a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.

我们研究了一个顺序决策问题，其中学习者面临$ k $武装的随机匪徒任务的顺序。对手可能会设计任务，但是对手受到限制，以在$ m $ and的较小（但未知）子集中选择每个任务的最佳组。任务边界可能是已知的（强盗元学习设置）或未知（非平稳的强盗设置）。我们设计了一种基于Burnit subsodular最大化的减少的算法，并表明，在大量任务和少数最佳武器的制度中，它在两种情况下的遗憾都比$ \ tilde {o}的简单基线要小。 \ sqrt {knt}）$可以通过使用为非平稳匪徒问题设计的标准算法获得。对于固定任务长度$ \ tau $的强盗元学习问题，我们证明该算法的遗憾被限制为$ \ tilde {o}（nm \ sqrt {m \ tau}+n^{2/3} m \ tau）$。在每个任务中最佳武器的可识别性的其他假设下，我们显示了一个带有改进的$ \ tilde {o}（n \ sqrt {m \ tau}+n^{1/2} {1/2} \ sqrt的强盗元学习算法{m k \ tau}）$遗憾。