76,614 research outputs found
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
The optimization of algorithm (hyper-)parameters is crucial for achieving
peak performance across a wide range of domains, ranging from deep neural
networks to solvers for hard combinatorial problems. The resulting algorithm
configuration (AC) problem has attracted much attention from the machine
learning community. However, the proper evaluation of new AC procedures is
hindered by two key hurdles. First, AC benchmarks are hard to set up. Second
and even more significantly, they are computationally expensive: a single run
of an AC procedure involves many costly runs of the target algorithm whose
performance is to be optimized in a given AC benchmark scenario. One common
workaround is to optimize cheap-to-evaluate artificial benchmark functions
(e.g., Branin) instead of actual algorithms; however, these have different
properties than realistic AC problems. Here, we propose an alternative
benchmarking approach that is similarly cheap to evaluate but much closer to
the original AC problem: replacing expensive benchmarks by surrogate benchmarks
constructed from AC benchmarks. These surrogate benchmarks approximate the
response surface corresponding to true target algorithm performance using a
regression model, and the original and surrogate benchmark share the same
(hyper-)parameter space. In our experiments, we construct and evaluate
surrogate benchmarks for hyperparameter optimization as well as for AC problems
that involve performance optimization of solvers for hard combinatorial
problems, drawing training data from the runs of existing AC procedures. We
show that our surrogate benchmarks capture overall important characteristics of
the AC scenarios, such as high- and low-performing regions, from which they
were derived, while being much easier to use and orders of magnitude cheaper to
evaluate
Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks
The autoregressive neural networks are emerging as a powerful computational
tool to solve relevant problems in classical and quantum mechanics. One of
their appealing functionalities is that, after they have learned a probability
distribution from a dataset, they allow exact and efficient sampling of typical
system configurations. Here we employ a neural autoregressive distribution
estimator (NADE) to boost Markov chain Monte Carlo (MCMC) simulations of a
paradigmatic classical model of spin-glass theory, namely the two-dimensional
Edwards-Anderson Hamiltonian. We show that a NADE can be trained to accurately
mimic the Boltzmann distribution using unsupervised learning from system
configurations generated using standard MCMC algorithms. The trained NADE is
then employed as smart proposal distribution for the Metropolis-Hastings
algorithm. This allows us to perform efficient MCMC simulations, which provide
unbiased results even if the expectation value corresponding to the probability
distribution learned by the NADE is not exact. Notably, we implement a
sequential tempering procedure, whereby a NADE trained at a higher temperature
is iteratively employed as proposal distribution in a MCMC simulation run at a
slightly lower temperature. This allows one to efficiently simulate the
spin-glass model even in the low-temperature regime, avoiding the divergent
correlation times that plague MCMC simulations driven by local-update
algorithms. Furthermore, we show that the NADE-driven simulations quickly
sample ground-state configurations, paving the way to their future utilization
to tackle binary optimization problems.Comment: 13 pages, 14 figure
Business Process Configuration According to Data Dependency Specification
Configuration techniques have been used in several fields, such as the design of business
process models. Sometimes these models depend on the data dependencies, being easier to describe
what has to be done instead of how. Configuration models enable to use a declarative representation
of business processes, deciding the most appropriate work-flow in each case. Unfortunately,
data dependencies among the activities and how they can affect the correct execution of the process,
has been overlooked in the declarative specifications and configurable systems found in the literature.
In order to find the best process configuration for optimizing the execution time of processes according
to data dependencies, we propose the use of Constraint Programming paradigm with the aim of
obtaining an adaptable imperative model in function of the data dependencies of the activities
described declarative.Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-RFondo Europeo de Desarrollo Regiona
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