7,787 research outputs found
B-tests: Low Variance Kernel Two-Sample Tests
A family of maximum mean discrepancy (MMD) kernel two-sample tests is
introduced. Members of the test family are called Block-tests or B-tests, since
the test statistic is an average over MMDs computed on subsets of the samples.
The choice of block size allows control over the tradeoff between test power
and computation time. In this respect, the -test family combines favorable
properties of previously proposed MMD two-sample tests: B-tests are more
powerful than a linear time test where blocks are just pairs of samples, yet
they are more computationally efficient than a quadratic time test where a
single large block incorporating all the samples is used to compute a
U-statistic. A further important advantage of the B-tests is their
asymptotically Normal null distribution: this is by contrast with the
U-statistic, which is degenerate under the null hypothesis, and for which
estimates of the null distribution are computationally demanding. Recent
results on kernel selection for hypothesis testing transfer seamlessly to the
B-tests, yielding a means to optimize test power via kernel choice.Comment: Neural Information Processing Systems (2013
Reference image selection for difference imaging analysis
Difference image analysis (DIA) is an effective technique for obtaining
photometry in crowded fields, relative to a chosen reference image. As yet,
however, optimal reference image selection is an unsolved problem. We examine
how this selection depends on the combination of seeing, background and
detector pixel size. Our tests use a combination of simulated data and quality
indicators from DIA of well-sampled optical data and under-sampled
near-infrared data from the OGLE and VVV surveys, respectively. We search for a
figure-of-merit (FoM) which could be used to select reference images for each
survey. While we do not find a universally applicable FoM, survey-specific
measures indicate that the effect of spatial under-sampling may require a
change in strategy from the standard DIA approach, even though seeing remains
the primary criterion. We find that background is not an important criterion
for reference selection, at least for the dynamic range in the images we test.
For our analysis of VVV data in particular, we find that spatial under-sampling
is best handled by reversing the standard DIA procedure and convolving target
images to a better-sampled (poor seeing) reference image.Comment: 14 pages, 8 figures, 4 tables, accepted for publication in MNRA
Generalized Team Draft Interleaving
Interleaving is an online evaluation method that compares
two ranking functions by mixing their results and interpret-
ing the users' click feedback. An important property of
an interleaving method is its sensitivity, i.e. the ability to
obtain reliable comparison outcomes with few user interac-
tions. Several methods have been proposed so far to im-
prove interleaving sensitivity, which can be roughly divided
into two areas: (a) methods that optimize the credit assign-
ment function (how the click feedback is interpreted), and
(b) methods that achieve higher sensitivity by controlling
the interleaving policy (how often a particular interleaved
result page is shown).
In this paper, we propose an interleaving framework that
generalizes the previously studied interleaving methods in
two aspects. First, it achieves a higher sensitivity by per-
forming a joint data-driven optimization of the credit as-
signment function and the interleaving policy. Second, we
formulate the framework to be general w.r.t. the search do-
main where the interleaving experiment is deployed, so that
it can be applied in domains with grid-based presentation,
such as image search. In order to simplify the optimization,
we additionally introduce a stratifed estimate of the exper-
iment outcome. This stratifcation is also useful on its own,
as it reduces the variance of the outcome and thus increases
the interleaving sensitivity.
We perform an extensive experimental study using large-
scale document and image search datasets obtained from
a commercial search engine. The experiments show that
our proposed framework achieves marked improvements in
sensitivity over efective baselines on both datasets
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