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Differential privacy for learning vector quantization

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Figure

Figure 1: Sample-Aggregate Framework. U i are random subsets from {1, . . . , |D|} of size
Figure 2: Two settings with potential privacy leaks: for the toy settings pictured in 2a and 2b, we compare GLVQ results on the data sets comprised of only the dark points to results on the data sets augmented by the light gray points.
Table 1: Mean and std. dev. in brackets for test error rates. As a baseline, the results of a non-private training with SGD and a BFGS optimizer are given
Figure 3: Averaged GLVQ and GMLVQ test error rates for SGD and in vanilla form on three benchmarks
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