1,075 research outputs found
A fast Monte-Carlo method with a Reduced Basis of Control Variates applied to Uncertainty Propagation and Bayesian Estimation
The Reduced-Basis Control-Variate Monte-Carlo method was introduced recently
in [S. Boyaval and T. Leli\`evre, CMS, 8 2010] as an improved Monte-Carlo
method, for the fast estimation of many parametrized expected values at many
parameter values. We provide here a more complete analysis of the method
including precise error estimates and convergence results. We also numerically
demonstrate that it can be useful to some parametrized frameworks in
Uncertainty Quantification, in particular (i) the case where the parametrized
expectation is a scalar output of the solution to a Partial Differential
Equation (PDE) with stochastic coefficients (an Uncertainty Propagation
problem), and (ii) the case where the parametrized expectation is the Bayesian
estimator of a scalar output in a similar PDE context. Moreover, in each case,
a PDE has to be solved many times for many values of its coefficients. This is
costly and we also use a reduced basis of PDE solutions like in [S. Boyaval, C.
Le Bris, Nguyen C., Y. Maday and T. Patera, CMAME, 198 2009]. This is the first
combination of various Reduced-Basis ideas to our knowledge, here with a view
to reducing as much as possible the computational cost of a simple approach to
Uncertainty Quantification
Roq: Robust Query Optimization Based on a Risk-aware Learned Cost Model
Query optimizers in relational database management systems (RDBMSs) search
for execution plans expected to be optimal for a given queries. They use
parameter estimates, often inaccurate, and make assumptions that may not hold
in practice. Consequently, they may select execution plans that are suboptimal
at runtime, when these estimates and assumptions are not valid, which may
result in poor query performance. Therefore, query optimizers do not
sufficiently support robust query optimization. Recent years have seen a surge
of interest in using machine learning (ML) to improve efficiency of data
systems and reduce their maintenance overheads, with promising results obtained
in the area of query optimization in particular. In this paper, inspired by
these advancements, and based on several years of experience of IBM Db2 in this
journey, we propose Robust Optimization of Queries, (Roq), a holistic framework
that enables robust query optimization based on a risk-aware learning approach.
Roq includes a novel formalization of the notion of robustness in the context
of query optimization and a principled approach for its quantification and
measurement based on approximate probabilistic ML. It also includes novel
strategies and algorithms for query plan evaluation and selection. Roq also
includes a novel learned cost model that is designed to predict query execution
cost and the associated risks and performs query optimization accordingly. We
demonstrate experimentally that Roq provides significant improvements to robust
query optimization compared to the state-of-the-art.Comment: 13 pages, 9 figures, submitted to SIGMOD 202
Performance Analysis of Multi-Task Deep Learning Models for Flux Regression in Discrete Fracture Networks
In this work, we investigate the sensitivity of a family of multi-task Deep Neural Networks (DNN) trained to predict fluxes through given Discrete Fracture Networks (DFNs), stochastically varying the fracture transmissivities. In particular, detailed performance and reliability analyses of more than two hundred Neural Networks (NN) are performed, training the models on sets of an increasing number of numerical simulations made on several DFNs with two fixed geometries (158 fractures and 385 fractures) and different transmissibility configurations. A quantitative evaluation of the trained NN predictions is proposed, and rules fitting the observed behavior are provided to predict the number of training simulations that are required for a given accuracy with respect to the variability in the stochastic distribution of the fracture transmissivities. A rule for estimating the cardinality of the training dataset for different configurations is proposed. From the analysis performed, an interesting regularity of the NN behaviors is observed, despite the stochasticity that imbues the whole training process. The proposed approach can be relevant for the use of deep learning models as model reduction methods in the framework of uncertainty quantification analysis for fracture networks and can be extended to similar geological problems (for example, to the more complex discrete fracture matrix models). The results of this study have the potential to grant concrete advantages to real underground flow characterization problems, making computational costs less expensive through the use of NNs
Stability of feature selection algorithms: a study on high-dimensional spaces
With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorith
10381 Summary and Abstracts Collection -- Robust Query Processing
Dagstuhl seminar 10381 on robust query processing (held 19.09.10 -
24.09.10) brought together a diverse set of researchers and practitioners
with a broad range of expertise for the purpose of fostering discussion
and collaboration regarding causes, opportunities, and solutions for
achieving robust query processing.
The seminar strove to build a unified view across
the loosely-coupled system components responsible for
the various stages of database query processing.
Participants were chosen for their experience with database
query processing and, where possible, their prior work in academic
research or in product development towards robustness in database query
processing.
In order to pave the way to motivate, measure, and protect future advances
in robust query processing, seminar 10381 focused on developing tests
for measuring the robustness of query processing.
In these proceedings, we first review the seminar topics, goals,
and results, then present abstracts or notes of some of the seminar break-out
sessions.
We also include, as an appendix,
the robust query processing reading list that
was collected and distributed to participants before the seminar began,
as well as summaries of a few of those papers that were
contributed by some participants
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