3,840 research outputs found
Some Applications of Coding Theory in Computational Complexity
Error-correcting codes and related combinatorial constructs play an important
role in several recent (and old) results in computational complexity theory. In
this paper we survey results on locally-testable and locally-decodable
error-correcting codes, and their applications to complexity theory and to
cryptography.
Locally decodable codes are error-correcting codes with sub-linear time
error-correcting algorithms. They are related to private information retrieval
(a type of cryptographic protocol), and they are used in average-case
complexity and to construct ``hard-core predicates'' for one-way permutations.
Locally testable codes are error-correcting codes with sub-linear time
error-detection algorithms, and they are the combinatorial core of
probabilistically checkable proofs
Quantum Algorithms for Learning and Testing Juntas
In this article we develop quantum algorithms for learning and testing
juntas, i.e. Boolean functions which depend only on an unknown set of k out of
n input variables. Our aim is to develop efficient algorithms:
- whose sample complexity has no dependence on n, the dimension of the domain
the Boolean functions are defined over;
- with no access to any classical or quantum membership ("black-box")
queries. Instead, our algorithms use only classical examples generated
uniformly at random and fixed quantum superpositions of such classical
examples;
- which require only a few quantum examples but possibly many classical
random examples (which are considered quite "cheap" relative to quantum
examples).
Our quantum algorithms are based on a subroutine FS which enables sampling
according to the Fourier spectrum of f; the FS subroutine was used in earlier
work of Bshouty and Jackson on quantum learning. Our results are as follows:
- We give an algorithm for testing k-juntas to accuracy that uses
quantum examples. This improves on the number of examples used
by the best known classical algorithm.
- We establish the following lower bound: any FS-based k-junta testing
algorithm requires queries.
- We give an algorithm for learning -juntas to accuracy that
uses quantum examples and
random examples. We show that this learning algorithms is close to optimal by
giving a related lower bound.Comment: 15 pages, 1 figure. Uses synttree package. To appear in Quantum
Information Processin
Inferring Rankings Using Constrained Sensing
We consider the problem of recovering a function over the space of
permutations (or, the symmetric group) over elements from given partial
information; the partial information we consider is related to the group
theoretic Fourier Transform of the function. This problem naturally arises in
several settings such as ranked elections, multi-object tracking, ranking
systems, and recommendation systems. Inspired by the work of Donoho and Stark
in the context of discrete-time functions, we focus on non-negative functions
with a sparse support (support size domain size). Our recovery method is
based on finding the sparsest solution (through optimization) that is
consistent with the available information. As the main result, we derive
sufficient conditions for functions that can be recovered exactly from partial
information through optimization. Under a natural random model for the
generation of functions, we quantify the recoverability conditions by deriving
bounds on the sparsity (support size) for which the function satisfies the
sufficient conditions with a high probability as .
optimization is computationally hard. Therefore, the popular compressive
sensing literature considers solving the convex relaxation,
optimization, to find the sparsest solution. However, we show that
optimization fails to recover a function (even with constant sparsity)
generated using the random model with a high probability as . In
order to overcome this problem, we propose a novel iterative algorithm for the
recovery of functions that satisfy the sufficient conditions. Finally, using an
Information Theoretic framework, we study necessary conditions for exact
recovery to be possible.Comment: 19 page
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