348 research outputs found
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELC
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey
Machine learning methods are increasingly widely used in high-risk settings
such as healthcare, transportation, and finance. In these settings, it is
important that a model produces calibrated uncertainty to reflect its own
confidence and avoid failures. In this paper we survey recent works on
uncertainty quantification (UQ) for deep learning, in particular
distribution-free Conformal Prediction method for its mathematical properties
and wide applicability. We will cover the theoretical guarantees of conformal
methods, introduce techniques that improve calibration and efficiency for UQ in
the context of spatiotemporal data, and discuss the role of UQ in the context
of safe decision making
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time series and dependence analyses
A hybrid sampler for Poisson-Kingman mixture models
This paper concerns the introduction of a new Markov Chain Monte Carlo scheme
for posterior sampling in Bayesian nonparametric mixture models with priors
that belong to the general Poisson-Kingman class. We present a novel compact
way of representing the infinite dimensional component of the model such that
while explicitly representing this infinite component it has less memory and
storage requirements than previous MCMC schemes. We describe comparative
simulation results demonstrating the efficacy of the proposed MCMC algorithm
against existing marginal and conditional MCMC samplers
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