5,793 research outputs found
Linear Estimating Equations for Exponential Families with Application to Gaussian Linear Concentration Models
In many families of distributions, maximum likelihood estimation is
intractable because the normalization constant for the density which enters
into the likelihood function is not easily available. The score matching
estimator of Hyv\"arinen (2005) provides an alternative where this
normalization constant is not required. The corresponding estimating equations
become linear for an exponential family. The score matching estimator is shown
to be consistent and asymptotically normally distributed for such models,
although not necessarily efficient. Gaussian linear concentration models are
examples of such families. For linear concentration models that are also linear
in the covariance we show that the score matching estimator is identical to the
maximum likelihood estimator, hence in such cases it is also efficient.
Gaussian graphical models and graphical models with symmetries form
particularly interesting subclasses of linear concentration models and we
investigate the potential use of the score matching estimator for this case
Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in
the broad field of computational intelligence. Accordingly, a wide repertoire
of pattern recognition tools, such as classifiers and knowledge discovery
procedures, are nowadays available and tested for various datasets of labeled
graphs. However, the design of effective learning procedures operating in the
space of labeled graphs is still a challenging problem, especially from the
computational complexity viewpoint. In this paper, we present a major
improvement of a general-purpose classifier for graphs, which is conceived on
an interplay between dissimilarity representation, clustering,
information-theoretic techniques, and evolutionary optimization algorithms. The
improvement focuses on a specific key subroutine devised to compress the input
data. We prove different theorems which are fundamental to the setting of the
parameters controlling such a compression operation. We demonstrate the
effectiveness of the resulting classifier by benchmarking the developed
variants on well-known datasets of labeled graphs, considering as distinct
performance indicators the classification accuracy, computing time, and
parsimony in terms of structural complexity of the synthesized classification
models. The results show state-of-the-art standards in terms of test set
accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infinite set of all computable descriptions of tasks with possibly
computable solutions. The novel algorithmic framework POWERPLAY (2011)
continually searches the space of possible pairs of new tasks and modifications
of the current problem solver, until it finds a more powerful problem solver
that provably solves all previously learned tasks plus the new one, while the
unmodified predecessor does not. Wow-effects are achieved by continually making
previously learned skills more efficient such that they require less time and
space. New skills may (partially) re-use previously learned skills. POWERPLAY's
search orders candidate pairs of tasks and solver modifications by their
conditional computational (time & space) complexity, given the stored
experience so far. The new task and its corresponding task-solving skill are
those first found and validated. The computational costs of validating new
tasks need not grow with task repertoire size. POWERPLAY's ongoing search for
novelty keeps breaking the generalization abilities of its present solver. This
is related to Goedel's sequence of increasingly powerful formal theories based
on adding formerly unprovable statements to the axioms without affecting
previously provable theorems. The continually increasing repertoire of problem
solving procedures can be exploited by a parallel search for solutions to
additional externally posed tasks. POWERPLAY may be viewed as a greedy but
practical implementation of basic principles of creativity. A first
experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to
first experiments with POWERPLA
Dynamic Peer-to-Peer Competition
The dynamic behavior of a multiagent system in which the agent size
is variable it is studied along a Lotka-Volterra approach. The agent size has
hereby for meaning the fraction of a given market that an agent is able to
capture (market share). A Lotka-Volterra system of equations for prey-predator
problems is considered, the competition factor being related to the difference
in size between the agents in a one-on-one competition. This mechanism
introduces a natural self-organized dynamic competition among agents. In the
competition factor, a parameter is introduced for scaling the
intensity of agent size similarity, which varies in each iteration cycle. The
fixed points of this system are analytically found and their stability analyzed
for small systems (with agents). We have found that different scenarios
are possible, from chaotic to non-chaotic motion with cluster formation as
function of the parameter and depending on the initial conditions
imposed to the system. The present contribution aim is to show how a realistic
though minimalist nonlinear dynamics model can be used to describe market
competition (companies, brokers, decision makers) among other opinion maker
communities.Comment: 17 pages, 50 references, 6 figures, 1 tabl
Equilibrium Points of an AND-OR Tree: under Constraints on Probability
We study a probability distribution d on the truth assignments to a uniform
binary AND-OR tree. Liu and Tanaka [2007, Inform. Process. Lett.] showed the
following: If d achieves the equilibrium among independent distributions (ID)
then d is an independent identical distribution (IID). We show a stronger form
of the above result. Given a real number r such that 0 < r < 1, we consider a
constraint that the probability of the root node having the value 0 is r. Our
main result is the following: When we restrict ourselves to IDs satisfying this
constraint, the above result of Liu and Tanaka still holds. The proof employs
clever tricks of induction. In particular, we show two fundamental
relationships between expected cost and probability in an IID on an OR-AND
tree: (1) The ratio of the cost to the probability (of the root having the
value 0) is a decreasing function of the probability x of the leaf. (2) The
ratio of derivative of the cost to the derivative of the probability is a
decreasing function of x, too.Comment: 13 pages, 3 figure
Towards Machine Wald
The past century has seen a steady increase in the need of estimating and
predicting complex systems and making (possibly critical) decisions with
limited information. Although computers have made possible the numerical
evaluation of sophisticated statistical models, these models are still designed
\emph{by humans} because there is currently no known recipe or algorithm for
dividing the design of a statistical model into a sequence of arithmetic
operations. Indeed enabling computers to \emph{think} as \emph{humans} have the
ability to do when faced with uncertainty is challenging in several major ways:
(1) Finding optimal statistical models remains to be formulated as a well posed
problem when information on the system of interest is incomplete and comes in
the form of a complex combination of sample data, partial knowledge of
constitutive relations and a limited description of the distribution of input
random variables. (2) The space of admissible scenarios along with the space of
relevant information, assumptions, and/or beliefs, tend to be infinite
dimensional, whereas calculus on a computer is necessarily discrete and finite.
With this purpose, this paper explores the foundations of a rigorous framework
for the scientific computation of optimal statistical estimators/models and
reviews their connections with Decision Theory, Machine Learning, Bayesian
Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty
Quantification and Information Based Complexity.Comment: 37 page
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