35,281 research outputs found
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
On surrogate loss functions and -divergences
The goal of binary classification is to estimate a discriminant function
from observations of covariate vectors and corresponding binary
labels. We consider an elaboration of this problem in which the covariates are
not available directly but are transformed by a dimensionality-reducing
quantizer . We present conditions on loss functions such that empirical risk
minimization yields Bayes consistency when both the discriminant function and
the quantizer are estimated. These conditions are stated in terms of a general
correspondence between loss functions and a class of functionals known as
Ali-Silvey or -divergence functionals. Whereas this correspondence was
established by Blackwell [Proc. 2nd Berkeley Symp. Probab. Statist. 1 (1951)
93--102. Univ. California Press, Berkeley] for the 0--1 loss, we extend the
correspondence to the broader class of surrogate loss functions that play a key
role in the general theory of Bayes consistency for binary classification. Our
result makes it possible to pick out the (strict) subset of surrogate loss
functions that yield Bayes consistency for joint estimation of the discriminant
function and the quantizer.Comment: Published in at http://dx.doi.org/10.1214/08-AOS595 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval,
is nowadays routinely used as a performance metric for problems such as binary
classification, multi-label classification, and structured output prediction.
Optimizing this measure is a statistically and computationally challenging
problem, since no closed-form solution exists. Adopting a decision-theoretic
perspective, this article provides a formal and experimental analysis of
different approaches for maximizing the F-measure. We start with a Bayes-risk
analysis of related loss functions, such as Hamming loss and subset zero-one
loss, showing that optimizing such losses as a surrogate of the F-measure leads
to a high worst-case regret. Subsequently, we perform a similar type of
analysis for F-measure maximizing algorithms, showing that such algorithms are
approximate, while relying on additional assumptions regarding the statistical
distribution of the binary response variables. Furthermore, we present a new
algorithm which is not only computationally efficient but also Bayes-optimal,
regardless of the underlying distribution. To this end, the algorithm requires
only a quadratic (with respect to the number of binary responses) number of
parameters of the joint distribution. We illustrate the practical performance
of all analyzed methods by means of experiments with multi-label classification
problems
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