195 research outputs found
Unwrapping black box models: a case study in credit risk
The past two decades have witnessed the rapid development of machine learning
techniques, which have proven to be powerful tools for the construction of predictive
models, such as those used in credit risk management. A considerable volume of
published work has looked at the utility of machine learning for this purpose, the
increased predictive capacities delivered and how new types of data can be
exploited. However, these benefits come at the cost of increased complexity, which
may render the models uninterpretable. To overcome this issue a new field has
emerged under the name of explainable artificial intelligence, with numerous tools
being proposed to gain an insight into the inner workings of these models. This type
of understanding is fundamental in credit risk in order to ensure compliance with the
existing regulatory requirements and to comprehend the factors driving the
predictions and their macro-economic implications. This paper studies the
effectiveness of some of the most widely-used interpretability techniques on a neural
network trained on real data. These techniques are found to be useful for
understanding the model, even though some limitations have been encountered.En las dos últimas décadas se ha observado un rápido desarrollo de las técnicas
de aprendizaje automático, que han demostrado ser herramientas muy potentes
para elaborar modelos de predicción, como los utilizados en la gestión del riesgo de
crédito. En un volumen considerable de trabajos publicados se analizan la utilidad del
aprendizaje automático para este fin, las mayores capacidades predictivas que
ofrece y la forma en la que se pueden explotar nuevos tipos de datos. Sin embargo,
estas ventajas llevan aparejada una mayor complejidad, que puede imposibilitar la
interpretación de los modelos. Para solventar este punto ha surgido un nuevo campo
de investigación, denominado «inteligencia artificial explicable» (del inglés explicable
artificial intelligence), en el que se proponen numerosas herramientas para obtener
información relativa al funcionamiento interno de estos modelos. Este tipo de
conocimiento es fundamental en materia de riesgo de crédito para garantizar que se
cumplen los requerimientos regulatorios existentes y para comprender los factores
determinantes de las predicciones y sus implicaciones macroeconómicas. En este
artÃculo se estudia la eficacia de algunas de las técnicas de interpretabilidad más
utilizadas en una red neuronal entrenada con datos reales. Estas técnicas se
consideran útiles para la comprensión del modelo, pese a que se han detectado
algunas limitaciones
The offence of stalking : a domcatic and jurisprudential analysis
Máster Universitario en Acceso a la Profesión de Abogado (M155
On the method of Bukhgeim for two-dimensional inverse problems
Tesis Doctoral inédita leÃda en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Matemáticas. Fecha de lectura: 22-02-201
Percentiles of sums of heavy-tailed random variables: Beyond the single-loss approximation
The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9376-6A perturbative approach is used to derive
approximations of arbitrary order to estimate high percentiles
of sums of positive independent random variables
that exhibit heavy tails. Closed-form expressions
for the successive approximations are obtained both
when the number of terms in the sum is deterministic
and when it is random. The zeroth order approximation
is the percentile of the maximum term in the
sum. Higher orders in the perturbative series involve
the right-truncated moments of the individual random
variables that appear in the sum. These censored moments
are always finite. As a result, and in contrast
to previous approximations proposed in the literature,
the perturbative series has the same form regardless of
whether these random variables have a finite mean or
not. For high percentiles, and specially for heavier tails,
the quality of the estimate improves as more terms are
included in the series, up to a certain order. Beyond
that order the convergence of the series deteriorates.
Nevertheless, the approximations obtained by truncating
the perturbative series at intermediate orders are
remarkably accurate for a variety of distributions in a
wide range of parameters.The authors thank the anonymous reviewers
for their valuable comments and suggestions. A.S.
acknowledges financial support from the Spanish Dirección
General de Investigación, project TIN2010-21575-C02-02
A bankable method for the field testingor a CPV plant
The bankability of CPV projects is an important issue to pave the way toward a swift and sustained growth in this technology. The bankability of a PV plant is generally addressed through the modeling of its energy yield under a b
aseline loss scenario, followed by an on-site measurement
campaign aimed at verifying its energetic behavior. The main difference between PV and CPV resides in the proper CPV modules, in particular in the inclusion of optical lements and III-V multijunction cells that are much more sensitive to spectral variations than xSi cells, while the rest of the system behaves in a way that possesses many common points with xSi technology. The modeling of the DC power output of a CPV system thus requires several impo
rtant second order parameters to be considered, mainly related to optics, spectral direct solar radiation, wind speed, tracker accuracy and heat dissipation of cells
- …