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

In this paper, we study robust algorithms for the experts problem under memory constraints.

We study lower bounds on adaptive sensing algorithms for recovering low rank matrices using linear measurements.

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.

We study the complexity of optimizing highly smooth convex functions.

Theorem 1.1 relies on two

Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.