V ariable batch size According to our budget decay scheme, the total budget (200 human annotations + 800 GPT -3.5 annotations) allocated for human annotation is 100, 50, 25, 13 (rounded up from

This gave birth to learning-augmented algorithms, which use these predictions to go beyond the standard long-standing worst-case limitations. The design of such algorithms requires establishing good tradeoffs between consistency and robustness, i.e. having improved performance when the predictions are accurate, and not behaving poorly

Subject screening To gain entry into the study, subjects were required to first perform a "demo" task consisting of 100 We refer to measures of human choice probability that are lapse-rate correct in this manner as "Normalized" (e.g., Supp. The typically observed lapse rates were quite low (median over subjects: 0%; mean 4.9%), indicating Figure 3: Human disruption rates are largely stable across stimulus presentation times. At shorter viewing times, we observed modest or no increases in disruption rate. Source images were captured with a smartphone camera. ImageNet classes, as previously defined in robustness library [2].

The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations.

Machine learning is now an integral part of decision-making processes across various domains, e.g., medical applications [