We explore an active learning approach for dynamic fair resource allocation
problems. Unlike previous work that assumes full feedback from all agents on
their allocations, we consider feedback from a select subset of agents at each
epoch of the online resource allocation process. Despite this restriction, our
proposed algorithms provide regret bounds that are sub-linear in number of
time-periods for various measures that include fairness metrics commonly used
in resource allocation problems and stability considerations in matching
mechanisms. The key insight of our algorithms lies in adaptively identifying
the most informative feedback using dueling upper and lower confidence bounds.
With this strategy, we show that efficient decision-making does not require
extensive feedback and produces efficient outcomes for a variety of problem
classes.

我们探索了一种用于动态公平资源分配问题的主动学习方法，该方法假设在在线资源分配过程的每个时期，仅从选择的代理人子集中获取反馈。尽管存在这种限制，我们提出的算法在包括资源分配问题中常用的公平度量和匹配机制中的稳定性考虑等各种度量中，提供了与时间周期数次线性相关的后悔界限。我们算法的关键洞察在于通过利用对决性的上限和下限置信区间来自适应地识别最具信息量的反馈。通过这种策略，我们证明高效的决策不需要大量的反馈，并为各种问题类别产生高效的结果。

公平稳定的在线分配的主动学习

Active Learning for Fair and Stable Online Allocations

In this paper we aim to determine the location and orientation of a
ground-level query image by matching to a reference database of overhead (e.g.
satellite) images. For this task we collect a new dataset with one million
pairs of street view and overhead images sampled from eleven U.S. cities. We
explore several deep CNN architectures for cross-domain matching --
Classification, Hybrid, Siamese, and Triplet networks. Classification and
Hybrid architectures are accurate but slow since they allow only partial
feature precomputation. We propose a new loss function which significantly
improves the accuracy of Siamese and Triplet embedding networks while
maintaining their applicability to large-scale retrieval tasks like image
geolocalization. This image matching task is challenging not just because of
the dramatic viewpoint difference between ground-level and overhead imagery but
because the orientation (i.e. azimuth) of the street views is unknown making
correspondence even more difficult. We examine several mechanisms to match in
spite of this -- training for rotation invariance, sampling possible rotations
at query time, and explicitly predicting relative rotation of ground and
overhead images with our deep networks. It turns out that explicit orientation
supervision also improves location prediction accuracy. Our best performing
architectures are roughly 2.5 times as accurate as the commonly used Siamese
network baseline.

利用深层卷积神经网络对地面和卫星俯视图进行交叉检索，实现图像地理定位，提出了一种新的损失函数，并采用多种匹配机制和旋转不变训练来提高图片匹配精度和效率。