While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Among existing baselines, replay-based methods show competitive results but requires extra memory for storing exemplars, while exemplar-free (i.e., data need not be stored for replay in production) methods are resource-friendly but often lack accuracy.

We give sharper bounds for uniformly stable randomized algorithms in a PAC-Bayesian framework, which improve the existing results by up to a factor of n (ignoring a log factor), where n is the sample size. The key idea is to bound the moment generating function of the generalization gap using concentration of weakly dependent random variables due to Bousquet et al (2020). We introduce an assumption of sub-exponential stability parameter, which allows a general treatment that we instantiate in two applications: stochastic gradient descent and randomized coordinate descent. Our results eliminate the requirement of strong convexity from previous results, and hold for non-smooth convex problems.

Is the Rat War Over? In New York, a rat czar and new methods have brought down complaints. We may even be ready to appreciate the creatures. Rats were leaving Manhattan, hurrying across the bridges in single-file lines. Some went to Westchester, some to Brooklyn. It was the pandemic, and the rats, which had been living off the nourishing trash of New York's densest borough for generations, were as panicked about the closure of restaurants as we were. People were eating three meals a day at home, and the rats were hungry. At least that was the story going around.

Neural Information Processing Systems http://nips.cc/