102 research outputs found
Random Tensors and Planted Cliques
The r-parity tensor of a graph is a generalization of the adjacency matrix,
where the tensor's entries denote the parity of the number of edges in
subgraphs induced by r distinct vertices. For r=2, it is the adjacency matrix
with 1's for edges and -1's for nonedges. It is well-known that the 2-norm of
the adjacency matrix of a random graph is O(\sqrt{n}). Here we show that the
2-norm of the r-parity tensor is at most f(r)\sqrt{n}\log^{O(r)}n, answering a
question of Frieze and Kannan who proved this for r=3. As a consequence, we get
a tight connection between the planted clique problem and the problem of
finding a vector that approximates the 2-norm of the r-parity tensor of a
random graph. Our proof method is based on an inductive application of
concentration of measure
Mixture Selection, Mechanism Design, and Signaling
We pose and study a fundamental algorithmic problem which we term mixture
selection, arising as a building block in a number of game-theoretic
applications: Given a function from the -dimensional hypercube to the
bounded interval , and an matrix with bounded entries,
maximize over in the -dimensional simplex. This problem arises
naturally when one seeks to design a lottery over items for sale in an auction,
or craft the posterior beliefs for agents in a Bayesian game through the
provision of information (a.k.a. signaling).
We present an approximation algorithm for this problem when
simultaneously satisfies two smoothness properties: Lipschitz continuity with
respect to the norm, and noise stability. The latter notion, which
we define and cater to our setting, controls the degree to which
low-probability errors in the inputs of can impact its output. When is
both -Lipschitz continuous and -stable, we obtain an (additive)
PTAS for mixture selection. We also show that neither assumption suffices by
itself for an additive PTAS, and both assumptions together do not suffice for
an additive FPTAS.
We apply our algorithm to different game-theoretic applications from
mechanism design and optimal signaling. We make progress on a number of open
problems suggested in prior work by easily reducing them to mixture selection:
we resolve an important special case of the small-menu lottery design problem
posed by Dughmi, Han, and Nisan; we resolve the problem of revenue-maximizing
signaling in Bayesian second-price auctions posed by Emek et al. and Miltersen
and Sheffet; we design a quasipolynomial-time approximation scheme for the
optimal signaling problem in normal form games suggested by Dughmi; and we
design an approximation algorithm for the optimal signaling problem in the
voting model of Alonso and C\^{a}mara
Optimal detection of sparse principal components in high dimension
We perform a finite sample analysis of the detection levels for sparse
principal components of a high-dimensional covariance matrix. Our minimax
optimal test is based on a sparse eigenvalue statistic. Alas, computing this
test is known to be NP-complete in general, and we describe a computationally
efficient alternative test using convex relaxations. Our relaxation is also
proved to detect sparse principal components at near optimal detection levels,
and it performs well on simulated datasets. Moreover, using polynomial time
reductions from theoretical computer science, we bring significant evidence
that our results cannot be improved, thus revealing an inherent trade off
between statistical and computational performance.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1127 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from observed
interactions. Guaranteed community detection has so far been mostly limited to
models with non-overlapping communities such as the stochastic block model. In
this paper, we remove this restriction, and provide guaranteed community
detection for a family of probabilistic network models with overlapping
communities, termed as the mixed membership Dirichlet model, first introduced
by Airoldi et al. This model allows for nodes to have fractional memberships in
multiple communities and assumes that the community memberships are drawn from
a Dirichlet distribution. Moreover, it contains the stochastic block model as a
special case. We propose a unified approach to learning these models via a
tensor spectral decomposition method. Our estimator is based on low-order
moment tensor of the observed network, consisting of 3-star counts. Our
learning method is fast and is based on simple linear algebraic operations,
e.g. singular value decomposition and tensor power iterations. We provide
guaranteed recovery of community memberships and model parameters and present a
careful finite sample analysis of our learning method. As an important special
case, our results match the best known scaling requirements for the
(homogeneous) stochastic block model
Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint
We study the problem of efficient exact partitioning of the hypergraphs
generated by high-order planted models. A high-order planted model assumes some
underlying cluster structures, and simulates high-order interactions by placing
hyperedges among nodes. Example models include the disjoint hypercliques, the
densest subhypergraphs, and the hypergraph stochastic block models. We show
that exact partitioning of high-order planted models (a NP-hard problem in
general) is achievable through solving a computationally efficient convex
optimization problem with a tensor nuclear norm constraint. Our analysis
provides the conditions for our approach to succeed on recovering the true
underlying cluster structures, with high probability
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