Hence, the above formulation of set function is submodular and is an instance of concave over modular function. Inour setting, we use labeled target dataDt asthe validation set. The completed-SNE loss is defined as a combination of Land cross-entropy loss on source and targetdomain. Table 8 shows the training times for this setting. Again, we see that all instantiationsofORIENTachieve 2.5 speed-upcomparedtoFull.

The recent success of deep learning frameworks in applications such as image classification [9], speech recognition [20], and object detection [13] stems primarily from the availability of large amounts of labeled data.

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Weappreciate this point and will revisit the word choice. What is given to the turkers? We will provide the full prompt in revision along with other details (we used 327 annotators) and discussion. For overall trustworthiness for instance, we asked "Does the article read like it comes28 from a trustworthy source?" Nevertheless, BERT is worse at neural fake news discrimination compared with Grover.

Bottleneck Models (CBMs) have gained popularity since their introduction.

The problem is challenging because the loss distribution and threshold value of each arm are unknown. We study this novel setting by establishing its'equivalence' to Multiple-Play Multi-Armed Bandits (MP-MAB) andCombinatorial Semi-Bandits.

However,givenlimited data, classical GANs have struggled, and strategies like output-regularization, data-augmentation, use of pre-trained models and pruning have been shown to lead to improvements. Notably, the applicability of these strategies is 1) often constrained to particular settings, e.g., availability of a pretrained GAN; or 2) increases training time, e.g., when using pruning.