43,909 research outputs found
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
We propose Factual News Graph (FANG), a novel graphical social context
representation and learning framework for fake news detection. Unlike previous
contextual models that have targeted performance, our focus is on
representation learning. Compared to transductive models, FANG is scalable in
training as it does not have to maintain all nodes, and it is efficient at
inference time, without the need to re-process the entire graph. Our
experimental results show that FANG is better at capturing the social context
into a high fidelity representation, compared to recent graphical and
non-graphical models. In particular, FANG yields significant improvements for
the task of fake news detection, and it is robust in the case of limited
training data. We further demonstrate that the representations learned by FANG
generalize to related tasks, such as predicting the factuality of reporting of
a news medium.Comment: To appear in CIKM 202
Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models
Describing the relationship between the variables in a study domain and
modelling the data generating mechanism is a fundamental problem in many
empirical sciences. Probabilistic graphical models are one common approach to
tackle the problem. Learning the graphical structure is computationally
challenging and a fervent area of current research with a plethora of
algorithms being developed. To facilitate the benchmarking of different
methods, we present a novel automated workflow, called benchpress for producing
scalable, reproducible, and platform-independent benchmarks of structure
learning algorithms for probabilistic graphical models. Benchpress is
interfaced via a simple JSON-file, which makes it accessible for all users,
while the code is designed in a fully modular fashion to enable researchers to
contribute additional methodologies. Benchpress currently provides an interface
to a large number of state-of-the-art algorithms from libraries such as BiDAG,
bnlearn, GOBNILP, pcalg, r.blip, scikit-learn, TETRAD, and trilearn as well as
a variety of methods for data generating models and performance evaluation.
Alongside user-defined models and randomly generated datasets, the software
tool also includes a number of standard datasets and graphical models from the
literature, which may be included in a benchmarking workflow. We demonstrate
the applicability of this workflow for learning Bayesian networks in four
typical data scenarios. The source code and documentation is publicly available
from http://github.com/felixleopoldo/benchpress.Comment: 30 pages, 1 figur
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NP-hard problem of MAP-inference for undirected discrete
graphical models. We propose a polynomial time and practically efficient
algorithm for finding a part of its optimal solution. Specifically, our
algorithm marks some labels of the considered graphical model either as (i)
optimal, meaning that they belong to all optimal solutions of the inference
problem; (ii) non-optimal if they provably do not belong to any solution. With
access to an exact solver of a linear programming relaxation to the
MAP-inference problem, our algorithm marks the maximal possible (in a specified
sense) number of labels. We also present a version of the algorithm, which has
access to a suboptimal dual solver only and still can ensure the
(non-)optimality for the marked labels, although the overall number of the
marked labels may decrease. We propose an efficient implementation, which runs
in time comparable to a single run of a suboptimal dual solver. Our method is
well-scalable and shows state-of-the-art results on computational benchmarks
from machine learning and computer vision.Comment: Reworked version, submitted to PAM
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