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cs.LG2020
On the role of data in PAC-Bayes bounds
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh +2
The dominant term in PAC-Bayes bounds is often the Kullback--Leibler divergence between the posterior and prior. For so-called linear PAC-Bayes risk bounds based on the empirical r…
cs.LG2018
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest…