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How Reductions to Sparse Sets Collapse the Polynomialtime Hierarchy: A Primer
, 1993
"... this paper to give simple proofs, in a uniform format, of the major known (pre1992) results relating how polynomialtime reductions of SAT to sparse sets collapse the polynomialtime hierarchy. To help the reader familiar with basic facts of complexity theory follow the main flow of ideas, while ke ..."
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Cited by 14 (0 self)
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this paper to give simple proofs, in a uniform format, of the major known (pre1992) results relating how polynomialtime reductions of SAT to sparse sets collapse the polynomialtime hierarchy. To help the reader familiar with basic facts of complexity theory follow the main flow of ideas, while
Large margin methods for structured and interdependent output variables
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
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Cited by 612 (12 self)
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that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains
Polynomialtime multiselectivity
, 1997
"... We introduce a generalization of Selman's Pselectivity that yields a more flexible notion of selectivity, called (polynomialtime) multiselectivity, in which the selector is allowed to operate on multiple input strings. Since our introduction of this class, it has been used [HJRW96] to prove ..."
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Cited by 2 (1 self)
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and the properties of SH and completely establish, in terms of incomparability and strict inclusion, the relations between our generalized selectivity classes and Ogihara's Pmc (polynomialtime membershipcomparable) classes. Although SH is a strictly increasing infinite hierarchy, we show that the core
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in largescale statistical models.
PolynomialTime Membership Comparable Sets
, 1994
"... This paper studies a notion called polynomialtime membership comparable sets. For a function g, a set A is polynomialtime gmembership comparable if there is a polynomialtime computable function f such that for any x 1 ; \Delta \Delta \Delta ; xm with m g(maxfjx 1 j; \Delta \Delta \Delta ; jx m j ..."
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Cited by 32 (5 self)
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jg), outputs b 2 f0; 1g m such that (A(x 1 ); \Delta \Delta \Delta ; A(xm )) 6= b. The following is a list of major results proven in the paper. 1. Polynomialtime membership comparable sets construct a proper hierarchy according to the bound on the number of arguments. 2. Polynomialtime
Satisfiability Allows No Nontrivial Sparsification Unless The PolynomialTime Hierarchy Collapses
 ELECTRONIC COLLOQUIUM ON COMPUTATIONAL COMPLEXITY, REPORT NO. 38 (2010)
, 2010
"... Consider the following twoplayer communication process to decide a language L: The first player holds the entire input x but is polynomially bounded; the second player is computationally unbounded but does not know any part of x; their goal is to cooperatively decide whether x belongs to L at small ..."
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Cited by 53 (2 self)
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that the polynomialtime hierarchy collapses to its third level. The result even holds when the first player is conondeterministic, and is tight as there exists a trivial protocol for ǫ = 0. Under the hypothesis that coNP is not in NP/poly, our result implies tight lower bounds for parameters of interest in several
On PolynomialTime Bounded TruthTable Reducibility of NP Sets to Sparse Sets
, 1991
"... We prove that if P ≠ NP, then there exists a set in NP that is not polynomial time bounded truthtable reducible (in short, p btt reducible) to any sparse set. In other words, we prove that no sparse p btt hard set exists for NP unless P = NP. By using the technique proving this result, w ..."
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Cited by 46 (4 self)
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We prove that if P ≠ NP, then there exists a set in NP that is not polynomial time bounded truthtable reducible (in short, p btt reducible) to any sparse set. In other words, we prove that no sparse p btt hard set exists for NP unless P = NP. By using the technique proving this result
Superpolynomial Circuits, Almost Sparse Oracles and the Exponential Hierarchy
 In Proceedings of the 12th Conference on the Foundations of Software Technology and Theoretical Computer Science
, 1992
"... Several problems concerning superpolynomial size circuits and superpolynomialtime advice classes are investigated. First we consider the implications of NP (and other fun damental complexity classes) having circuits slightly bigger than polynomial. We prove that if such circuits exist, for examp ..."
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Cited by 19 (6 self)
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in EXP=poly then EXP NP = EXP . Finally, we consider the alternating 2 polylog time hierarchy. The properties of this hierarchy underlie many of the previous results. 1 Introduction In research from the early 1980's to the present, there has been considerable interest in the implications
AverageCase Complexity Theory and PolynomialTime Reductions
, 2001
"... This thesis studies averagecase complexity theory and polynomialtime reducibilities. The issues in averagecase complexity arise primarily from Cai and Selman's extension of Levin's denition of average polynomial time. We study polynomialtime reductions between distributional problems. ..."
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Cited by 2 (0 self)
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This thesis studies averagecase complexity theory and polynomialtime reducibilities. The issues in averagecase complexity arise primarily from Cai and Selman's extension of Levin's denition of average polynomial time. We study polynomialtime reductions between distributional problems
Results 1  10
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2,674