700 research outputs found
Sampling-based proofs of almost-periodicity results and algorithmic applications
We give new combinatorial proofs of known almost-periodicity results for
sumsets of sets with small doubling in the spirit of Croot and Sisask, whose
almost-periodicity lemma has had far-reaching implications in additive
combinatorics. We provide an alternative (and L^p-norm free) point of view,
which allows for proofs to easily be converted to probabilistic algorithms that
decide membership in almost-periodic sumsets of dense subsets of F_2^n.
As an application, we give a new algorithmic version of the quasipolynomial
Bogolyubov-Ruzsa lemma recently proved by Sanders. Together with the results by
the last two authors, this implies an algorithmic version of the quadratic
Goldreich-Levin theorem in which the number of terms in the quadratic Fourier
decomposition of a given function is quasipolynomial in the error parameter,
compared with an exponential dependence previously proved by the authors. It
also improves the running time of the algorithm to have quasipolynomial
dependence instead of an exponential one.
We also give an application to the problem of finding large subspaces in
sumsets of dense sets. Green showed that the sumset of a dense subset of F_2^n
contains a large subspace. Using Fourier analytic methods, Sanders proved that
such a subspace must have dimension bounded below by a constant times the
density times n. We provide an alternative (and L^p norm-free) proof of a
comparable bound, which is analogous to a recent result of Croot, Laba and
Sisask in the integers.Comment: 28 page
05291 Abstracts Collection -- Sublinear Algorithms
From 17.07.05 to 22.07.05, the Dagstuhl Seminar
05291 ``Sublinear Algorithms\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Fourier Policy Gradients
We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits of EPG in a broad range of settings. For the critic, we treat
trigonometric and radial basis functions, two function families with the
universal approximation property. The choice of policy can be almost arbitrary,
including mixtures or hybrid continuous-discrete probability distributions.
Moreover, we derive a general family of sample-based estimators for stochastic
policy gradients, which unifies existing results on sample-based approximation.
We believe that this technique has the potential to shape the next generation
of policy gradient approaches, powered by analytical results
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