2. We're not measuring progress meaningfully. Many of the benchmarks adopted in private ML come from the non-private setting. For example, pre-training on ImageNet and fine-tuning on CIFAR-10. Good starting point, but may not reflect settings we'd want to use private ML. 8/n
Private ML Progress Measurement Benchmarks Need Reassessment
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