161 research outputs found
Asymptotically idempotent aggregation operators for trust management in multi-agent systems
The study of trust management in
multi-agent system, especially distributed,
has grown over the last
years. Trust is a complex subject
that has no general consensus in literature,
but has emerged the importance
of reasoning about it computationally.
Reputation systems takes
into consideration the history of an
entity’s actions/behavior in order to
compute trust, collecting and aggregating
ratings from members in a
community. In this scenario the aggregation
problem becomes fundamental,
in particular depending on
the environment. In this paper we
describe a technique based on a class
of asymptotically idempotent aggregation
operators, suitable particulary
for distributed anonymous environments
Sharp Oracle Inequalities for Aggregation of Affine Estimators
We consider the problem of combining a (possibly uncountably infinite) set of
affine estimators in non-parametric regression model with heteroscedastic
Gaussian noise. Focusing on the exponentially weighted aggregate, we prove a
PAC-Bayesian type inequality that leads to sharp oracle inequalities in
discrete but also in continuous settings. The framework is general enough to
cover the combinations of various procedures such as least square regression,
kernel ridge regression, shrinking estimators and many other estimators used in
the literature on statistical inverse problems. As a consequence, we show that
the proposed aggregate provides an adaptive estimator in the exact minimax
sense without neither discretizing the range of tuning parameters nor splitting
the set of observations. We also illustrate numerically the good performance
achieved by the exponentially weighted aggregate
Forecasting Aggregated Time Series Variables: A Survey
Aggregated times series variables can be forecasted in different ways. For example, they may be forecasted on the basis of the aggregate series or forecasts of disaggregated variables may be obtained first and then these forecasts may be aggregated. A number of forecasts are presented and compared. Classical theoretical results on the relative efficiencies of different forecasts are reviewed and some complications are discussed which invalidate the theoretical results. Contemporaneous as well as temporal aggregation are considered.Autoregressive moving-average process, temporal aggregation, contemporaneous aggregation, vector autoregressive moving-average process
SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers
We investigate a problem in which each member of a group of learners is
trained separately to solve the same classification task. Each learner has
access to a training dataset (possibly with overlap across learners) but each
trained classifier can be evaluated on a validation dataset. We propose a new
approach to aggregate the learner predictions in the possibility theory
framework. For each classifier prediction, we build a possibility distribution
assessing how likely the classifier prediction is correct using frequentist
probabilities estimated on the validation set. The possibility distributions
are aggregated using an adaptive t-norm that can accommodate dependency and
poor accuracy of the classifier predictions. We prove that the proposed
approach possesses a number of desirable classifier combination robustness
properties
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Copulae: On the Crossroads of Mathematics and Economics
The central focus of the workshop was on copula theory as well as applications to multivariate stochastic modelling. The programme was intrinsically interdisciplinary and represented areas with much recent progress. The workshop included talks and dynamic discussions on construction, estimation and various applications of copulas to finance, insurance, hydrology, medicine, risk management and related fields
Small gain theorems for large scale systems and construction of ISS Lyapunov functions
We consider interconnections of n nonlinear subsystems in the input-to-state
stability (ISS) framework. For each subsystem an ISS Lyapunov function is given
that treats the other subsystems as independent inputs. A gain matrix is used
to encode the mutual dependencies of the systems in the network. Under a small
gain assumption on the monotone operator induced by the gain matrix, a locally
Lipschitz continuous ISS Lyapunov function is obtained constructively for the
entire network by appropriately scaling the individual Lyapunov functions for
the subsystems. The results are obtained in a general formulation of ISS, the
cases of summation, maximization and separation with respect to external gains
are obtained as corollaries.Comment: provisionally accepted by SIAM Journal on Control and Optimizatio
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