---
res:
bibo_abstract:
- 'We study the problem of maximum marginal prediction (MMP) in probabilistic graphical
models, a task that occurs, for example, as the Bayes optimal decision rule under
a Hamming loss. MMP is typically performed as a two-stage procedure: one estimates
each variable''s marginal probability and then forms a prediction from the states
of maximal probability. In this work we propose a simple yet effective technique
for accelerating MMP when inference is sampling-based: instead of the above two-stage
procedure we directly estimate the posterior probability of each decision variable.
This allows us to identify the point of time when we are sufficiently certain
about any individual decision. Whenever this is the case, we dynamically prune
the variables we are confident about from the underlying factor graph. Consequently,
at any time only samples of variables whose decision is still uncertain need to
be created. Experiments in two prototypical scenarios, multi-label classification
and image inpainting, show that adaptive sampling can drastically accelerate MMP
without sacrificing prediction accuracy.@eng'
bibo_authorlist:
- foaf_Person:
foaf_givenName: Christoph
foaf_name: Lampert, Christoph
foaf_surname: Lampert
foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0001-8622-7887
bibo_volume: 1
dct_date: 2012^xs_gYear
dct_language: eng
dct_publisher: Neural Information Processing Systems@
dct_title: Dynamic pruning of factor graphs for maximum marginal prediction@
...