38,081 research outputs found
Relax and Localize: From Value to Algorithms
We show a principled way of deriving online learning algorithms from a
minimax analysis. Various upper bounds on the minimax value, previously thought
to be non-constructive, are shown to yield algorithms. This allows us to
seamlessly recover known methods and to derive new ones. Our framework also
captures such "unorthodox" methods as Follow the Perturbed Leader and the R^2
forecaster. We emphasize that understanding the inherent complexity of the
learning problem leads to the development of algorithms.
We define local sequential Rademacher complexities and associated algorithms
that allow us to obtain faster rates in online learning, similarly to
statistical learning theory. Based on these localized complexities we build a
general adaptive method that can take advantage of the suboptimality of the
observed sequence.
We present a number of new algorithms, including a family of randomized
methods that use the idea of a "random playout". Several new versions of the
Follow-the-Perturbed-Leader algorithms are presented, as well as methods based
on the Littlestone's dimension, efficient methods for matrix completion with
trace norm, and algorithms for the problems of transductive learning and
prediction with static experts
Community detection in networks via nonlinear modularity eigenvectors
Revealing a community structure in a network or dataset is a central problem
arising in many scientific areas. The modularity function is an established
measure quantifying the quality of a community, being identified as a set of
nodes having high modularity. In our terminology, a set of nodes with positive
modularity is called a \textit{module} and a set that maximizes is thus
called \textit{leading module}. Finding a leading module in a network is an
important task, however the dimension of real-world problems makes the
maximization of unfeasible. This poses the need of approximation techniques
which are typically based on a linear relaxation of , induced by the
spectrum of the modularity matrix . In this work we propose a nonlinear
relaxation which is instead based on the spectrum of a nonlinear modularity
operator . We show that extremal eigenvalues of
provide an exact relaxation of the modularity measure , however at the price
of being more challenging to be computed than those of . Thus we extend the
work made on nonlinear Laplacians, by proposing a computational scheme, named
\textit{generalized RatioDCA}, to address such extremal eigenvalues. We show
monotonic ascent and convergence of the method. We finally apply the new method
to several synthetic and real-world data sets, showing both effectiveness of
the model and performance of the method
Fast model-fitting of Bayesian variable selection regression using the iterative complex factorization algorithm
Bayesian variable selection regression (BVSR) is able to jointly analyze
genome-wide genetic datasets, but the slow computation via Markov chain Monte
Carlo (MCMC) hampered its wide-spread usage. Here we present a novel iterative
method to solve a special class of linear systems, which can increase the speed
of the BVSR model-fitting tenfold. The iterative method hinges on the complex
factorization of the sum of two matrices and the solution path resides in the
complex domain (instead of the real domain). Compared to the Gauss-Seidel
method, the complex factorization converges almost instantaneously and its
error is several magnitude smaller than that of the Gauss-Seidel method. More
importantly, the error is always within the pre-specified precision while the
Gauss-Seidel method is not. For large problems with thousands of covariates,
the complex factorization is 10 -- 100 times faster than either the
Gauss-Seidel method or the direct method via the Cholesky decomposition. In
BVSR, one needs to repetitively solve large penalized regression systems whose
design matrices only change slightly between adjacent MCMC steps. This slight
change in design matrix enables the adaptation of the iterative complex
factorization method. The computational innovation will facilitate the
wide-spread use of BVSR in reanalyzing genome-wide association datasets.Comment: Accepted versio
Joint Trajectory and Communication Design for UAV-Enabled Multiple Access
Unmanned aerial vehicles (UAVs) have attracted significant interest recently
in wireless communication due to their high maneuverability, flexible
deployment, and low cost. This paper studies a UAV-enabled wireless network
where the UAV is employed as an aerial mobile base station (BS) to serve a
group of users on the ground. To achieve fair performance among users, we
maximize the minimum throughput over all ground users by jointly optimizing the
multiuser communication scheduling and UAV trajectory over a finite horizon.
The formulated problem is shown to be a mixed integer non-convex optimization
problem that is difficult to solve in general. We thus propose an efficient
iterative algorithm by applying the block coordinate descent and successive
convex optimization techniques, which is guaranteed to converge to at least a
locally optimal solution. To achieve fast convergence and stable throughput, we
further propose a low-complexity initialization scheme for the UAV trajectory
design based on the simple circular trajectory. Extensive simulation results
are provided which show significant throughput gains of the proposed design as
compared to other benchmark schemes.Comment: Submitted for possible publicatio
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