1,182 research outputs found
A Complete Characterization of Statistical Query Learning with Applications to Evolvability
Statistical query (SQ) learning model of Kearns (1993) is a natural
restriction of the PAC learning model in which a learning algorithm is allowed
to obtain estimates of statistical properties of the examples but cannot see
the examples themselves. We describe a new and simple characterization of the
query complexity of learning in the SQ learning model. Unlike the previously
known bounds on SQ learning our characterization preserves the accuracy and the
efficiency of learning. The preservation of accuracy implies that that our
characterization gives the first characterization of SQ learning in the
agnostic learning framework. The preservation of efficiency is achieved using a
new boosting technique and allows us to derive a new approach to the design of
evolutionary algorithms in Valiant's (2006) model of evolvability. We use this
approach to demonstrate the existence of a large class of monotone evolutionary
learning algorithms based on square loss performance estimation. These results
differ significantly from the few known evolutionary algorithms and give
evidence that evolvability in Valiant's model is a more versatile phenomenon
than there had been previous reason to suspect.Comment: Simplified Lemma 3.8 and it's application
Lower Bounds for (Non-Monotone) Comparator Circuits
Comparator circuits are a natural circuit model for studying the concept of bounded fan-out computations, which intuitively corresponds to whether or not a computational model can make "copies" of intermediate computational steps. Comparator circuits are believed to be weaker than general Boolean circuits, but they can simulate Branching Programs and Boolean formulas. In this paper we prove the first superlinear lower bounds in the general (non-monotone) version of this model for an explicitly defined function. More precisely, we prove that the n-bit Element Distinctness function requires ?((n/ log n)^(3/2)) size comparator circuits
One-way permutations, computational asymmetry and distortion
Computational asymmetry, i.e., the discrepancy between the complexity of
transformations and the complexity of their inverses, is at the core of one-way
transformations. We introduce a computational asymmetry function that measures
the amount of one-wayness of permutations. We also introduce the word-length
asymmetry function for groups, which is an algebraic analogue of computational
asymmetry. We relate boolean circuits to words in a Thompson monoid, over a
fixed generating set, in such a way that circuit size is equal to word-length.
Moreover, boolean circuits have a representation in terms of elements of a
Thompson group, in such a way that circuit size is polynomially equivalent to
word-length. We show that circuits built with gates that are not constrained to
have fixed-length inputs and outputs, are at most quadratically more compact
than circuits built from traditional gates (with fixed-length inputs and
outputs). Finally, we show that the computational asymmetry function is closely
related to certain distortion functions: The computational asymmetry function
is polynomially equivalent to the distortion of the path length in Schreier
graphs of certain Thompson groups, compared to the path length in Cayley graphs
of certain Thompson monoids. We also show that the results of Razborov and
others on monotone circuit complexity lead to exponential lower bounds on
certain distortions.Comment: 33 page
A Nearly Optimal Lower Bound on the Approximate Degree of AC
The approximate degree of a Boolean function is the least degree of a real polynomial that
approximates pointwise to error at most . We introduce a generic
method for increasing the approximate degree of a given function, while
preserving its computability by constant-depth circuits.
Specifically, we show how to transform any Boolean function with
approximate degree into a function on variables with approximate degree at least . In particular, if , then
is polynomially larger than . Moreover, if is computed by a
polynomial-size Boolean circuit of constant depth, then so is .
By recursively applying our transformation, for any constant we
exhibit an AC function of approximate degree . This
improves over the best previous lower bound of due to
Aaronson and Shi (J. ACM 2004), and nearly matches the trivial upper bound of
that holds for any function. Our lower bounds also apply to
(quasipolynomial-size) DNFs of polylogarithmic width.
We describe several applications of these results. We give:
* For any constant , an lower bound on the
quantum communication complexity of a function in AC.
* A Boolean function with approximate degree at least ,
where is the certificate complexity of . This separation is optimal
up to the term in the exponent.
* Improved secret sharing schemes with reconstruction procedures in AC.Comment: 40 pages, 1 figur
The conjunctive complexity of quadratic Boolean functions
AbstractThe minimal number, of conjuctions in monotone circuits for quadratic Boolean functions, i.e. disjunctions of quadratic monomials xixj, is investigated. Single level circuits which have only one level of conjuctions are compared with arbitrary monotone circuits. The computation of the single level complexity is shown to be NP complete. For almost all quadratic functions, almost optimal circuits can be computed in polynomial time. The single level conjecture is disproved, i.e. a quadratic function is defined whose single level complexity is larger than its conjuctive complexit
Efficient Parallel Path Checking for Linear-Time Temporal Logic With Past and Bounds
Path checking, the special case of the model checking problem where the model
under consideration is a single path, plays an important role in monitoring,
testing, and verification. We prove that for linear-time temporal logic (LTL),
path checking can be efficiently parallelized. In addition to the core logic,
we consider the extensions of LTL with bounded-future (BLTL) and past-time
(LTL+Past) operators. Even though both extensions improve the succinctness of
the logic exponentially, path checking remains efficiently parallelizable: Our
algorithm for LTL, LTL+Past, and BLTL+Past is in AC^1(logDCFL) \subseteq NC
A Type-Directed Negation Elimination
In the modal mu-calculus, a formula is well-formed if each recursive variable
occurs underneath an even number of negations. By means of De Morgan's laws, it
is easy to transform any well-formed formula into an equivalent formula without
negations -- its negation normal form. Moreover, if the formula is of size n,
its negation normal form of is of the same size O(n). The full modal
mu-calculus and the negation normal form fragment are thus equally expressive
and concise.
In this paper we extend this result to the higher-order modal fixed point
logic (HFL), an extension of the modal mu-calculus with higher-order recursive
predicate transformers. We present a procedure that converts a formula into an
equivalent formula without negations of quadratic size in the worst case and of
linear size when the number of variables of the formula is fixed.Comment: In Proceedings FICS 2015, arXiv:1509.0282
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