Quantizers can be ranked by privacy using r_Q, a rate constant built from the loss gap and variance of low-loss quantized checkpoints along the training trajectory.
Fundamental limits of membership inference attacks on machine learning models
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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Quantizers can be ranked by privacy using r_Q, a rate constant built from the loss gap and variance of low-loss quantized checkpoints along the training trajectory.