description of our method

Algorithm 2 Procedure for estimating the weights 1: procedure ESTIMATEWEIGHTS( Teacher,Student,V,D) 2:.V is the validation dataset and D is the teacher-labeled dataset 3: U, k d12 p |V|e 4: for every (x,y) V do 5: X (Confidence(Teacher(x)),Confidence(Student(x))) 6: if arg max(Teacher(x)) = arg max(y) then: 7: (p,distortion) (0,1) 8: else: B.1 The student's test-accuracy-trajectory In this section we provide extended experimental results that show the student's test accuracy over the training trajectory corresponding to experiments we mentioned in Section 3.1. Notice that in the vast majority of cases our method significantly outperforms the conventional approach almost throughout the training process. The student's test accuracy over the training trajectory using harddistillation corresponding to the experiments of Figure 4. See Section 3.1.2 The student's test accuracy over the training trajectory corresponding to the experiments of Figure 5. See Section 3.1.2 The student's test accuracy over the training trajectory corresponding to the experiments of Figure 7. See Section 3.1.3 The student's test accuracy over the training trajectory using hard-distillation (first row) and soft-distillation (second row) corresponding to the experiments of Figure 8. See Section 3.1.4 Indeed, it is known (see e.g.