10,488 research outputs found
High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
We consider the problem of high-dimensional Gaussian graphical model
selection. We identify a set of graphs for which an efficient estimation
algorithm exists, and this algorithm is based on thresholding of empirical
conditional covariances. Under a set of transparent conditions, we establish
structural consistency (or sparsistency) for the proposed algorithm, when the
number of samples n=omega(J_{min}^{-2} log p), where p is the number of
variables and J_{min} is the minimum (absolute) edge potential of the graphical
model. The sufficient conditions for sparsistency are based on the notion of
walk-summability of the model and the presence of sparse local vertex
separators in the underlying graph. We also derive novel non-asymptotic
necessary conditions on the number of samples required for sparsistency
Causal and homogeneous networks
Growing networks have a causal structure. We show that the causality strongly
influences the scaling and geometrical properties of the network. In particular
the average distance between nodes is smaller for causal networks than for
corresponding homogeneous networks. We explain the origin of this effect and
illustrate it using as an example a solvable model of random trees. We also
discuss the issue of stability of the scale-free node degree distribution. We
show that a surplus of links may lead to the emergence of a singular node with
the degree proportional to the total number of links. This effect is closely
related to the backgammon condensation known from the balls-in-boxes model.Comment: short review submitted to AIP proceedings, CNET2004 conference;
changes in the discussion of the distance distribution for growing trees,
Fig. 6-right change
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.Comment: GECCO '18: Genetic and Evolutionary Computation Conference, 2018,
Kyoto, Japa
POOL File Catalog, Collection and Metadata Components
The POOL project is the common persistency framework for the LHC experiments
to store petabytes of experiment data and metadata in a distributed and grid
enabled way. POOL is a hybrid event store consisting of a data streaming layer
and a relational layer. This paper describes the design of file catalog,
collection and metadata components which are not part of the data streaming
layer of POOL and outlines how POOL aims to provide transparent and efficient
data access for a wide range of environments and use cases - ranging from a
large production site down to a single disconnected laptops. The file catalog
is the central POOL component translating logical data references to physical
data files in a grid environment. POOL collections with their associated
metadata provide an abstract way of accessing experiment data via their logical
grouping into sets of related data objects.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, 1 eps figure, PSN MOKT00
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