In this work, we argue that the order of play in strategic classification is fundamentally determined by the relative frequencies at which the decision-maker and the agents adapt to each other's actions.

Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.

This appendix is organized as follows. In Section A (referred by CheckList), we discuss the societal impact. In Section B.1 (referred by Section 4.1), we summarize the statistics of the datasets used in In Section B.2 (referred by Section 4), we introduce the pre-training and model adaptation In Section B.3, we provide zero-shot retrieval comparison by introducing knowledge. In Section B.4, we provide quantitative analysis on how external knowledge benefit transfer. In Section B.5 (referred by Section 4.2 and 4.3), we provide more visualizations of success In Section B.6, we provide more object detection results by training on larger dataset for We do not anticipate a specific negative impact, but, as with any Machine Learning method, we recommend to exercise caution.

A.1 Algorithmic Description of CSSL Algorithm 1 provides the pseudo-code of the batch-wise loss calculation in CSSL.Algorithm 1 CSSL with adaptive precisiation α Require: For CT Augment (and later RandAugment as considered in Section A.4.2), we use the same operations Figure 1 shows the learning curves of the runs considered in the efficiency study in Section 4.3 As ground-truth, we define the true probability of the positive class by a sigmoidal shaped function. In this setting, self-training of a simple neural network with deterministic labeling leads to a flat (instead of sigmoidal) function most of the time, because the learner tends to go with the majority in the labeled training data. With probabilistic labels, the results become a bit better: the learned functions tend to be increasing but still deviates a lot from the ground-truth sigmoid. Table 3 shows the results. In the following, we call this variant UPSMatch .

Effective intersection control can play an important role in reducing traffic congestion and associated vehicular emissions.