5 research outputs found
Sum-of-Squares Certificates for Maxima of Random Tensors on the Sphere
For an -variate order- tensor , define to be the maximum value taken by the
tensor on the unit sphere. It is known that for a random tensor with i.i.d entries, w.h.p. We study the
problem of efficiently certifying upper bounds on via the natural
relaxation from the Sum of Squares (SoS) hierarchy. Our results include:
- When is a random order- tensor, we prove that levels of SoS
certifies an upper bound on that satisfies Our upper bound improves a result of Montanari and Richard
(NIPS 2014) when is large.
- We show the above bound is the best possible up to lower order terms,
namely the optimum of the level- SoS relaxation is at least
- When is a random order- tensor, we prove that levels of SoS
certifies an upper bound on that satisfies For growing , this improves upon the bound
certified by constant levels of SoS. This answers in part, a question posed by
Hopkins, Shi, and Steurer (COLT 2015), who established the tight
characterization for constant levels of SoS
Machinery for Proving Sum-of-Squares Lower Bounds on Certification Problems
In this paper, we construct general machinery for proving Sum-of-Squares
lower bounds on certification problems by generalizing the techniques used by
Barak et al. [FOCS 2016] to prove Sum-of-Squares lower bounds for planted
clique. Using this machinery, we prove degree Sum-of-Squares
lower bounds for tensor PCA, the Wishart model of sparse PCA, and a variant of
planted clique which we call planted slightly denser subgraph.Comment: 134 page