33,965 research outputs found
Manifold Structured Prediction
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches
consider finite, albeit potentially huge, output spaces, in this paper we discuss how
structured prediction can be extended to a continuous scenario. Specifically, we
study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent
and study how geometric optimization can be used to compute the corresponding
estimator. Promising experimental results on both simulated and real data complete
our stud
Manifold structured prediction
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold-valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Event-centric structured prediction involves predicting structured outputs of
events. In most NLP cases, event structures are complex with manifold
dependency, and it is challenging to effectively represent these complicated
structured events. To address these issues, we propose Structured Prediction
with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex
dependency among event structured components with energy-based modeling, and
represents event classes with simple but effective hyperspheres. Experiments on
two unified-annotated event datasets indicate that SPEECH is predominant in
event detection and event-relation extraction tasks.Comment: Accepted by ACL 2023 Main Conference. Code is released at
\url{https://github.com/zjunlp/SPEECH
Structured Machine Learning for Robotics
Machine Learning has become the essential tool for automating tasks that consist in predicting the output associated to a certain input.
However many modern algorithms are mainly developed for the simple cases of classification and regression. Structured prediction is the field concerned with predicting outputs consisting of complex objects such as graphs, orientations or sequences. While these objects are often of practical interest, they do not have many of the mathematical properties that allow to design principled and computationally feasible algorithms with traditional techniques.
In this thesis we investigate and develop algorithms for learning manifold-valued functions in the context of structured prediction. Differentiable manifolds are a mathematical abstraction used in many domains to describe sets with continuous constraints and non-Euclidean geometric properties.
By taking a structured prediction approach we show how to define statistically consistent estimators for predicting elements of a manifold, in constrast to traditional structured predition algorithms that are restricted to output sets with finite cardinality.
We introduce a wide range of applications that leverage manifolds structures. Above all, we study the case of the hyperbolic manifold, a space suited for representing hierarchical data. By representing supervised datasets within hyperbolic space we show how it is possible to invent new concepts in a previously known hierarchy and show promising results in hierarchical classification.
We also study how modern structured approaches can help with practical robotics tasks, either improving performances in behavioural pipelines or showing more robust predictions for constrained tasks. Specifically, we show how structured prediction can be used to tackle inverse kinematics problems of redundant robots, accounting for the constraints of the robotic joints. We also consider the task of biological motion detection and show that by leveraging the sequence structure of video streams we significantly reduce the latency of the application. Our studies are complemented by empirical evaluations on both synthetic and real data
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
Feature selection guided by structural information
In generalized linear regression problems with an abundant number of
features, lasso-type regularization which imposes an -constraint on the
regression coefficients has become a widely established technique. Deficiencies
of the lasso in certain scenarios, notably strongly correlated design, were
unmasked when Zou and Hastie [J. Roy. Statist. Soc. Ser. B 67 (2005) 301--320]
introduced the elastic net. In this paper we propose to extend the elastic net
by admitting general nonnegative quadratic constraints as a second form of
regularization. The generalized ridge-type constraint will typically make use
of the known association structure of features, for example, by using temporal-
or spatial closeness. We study properties of the resulting "structured elastic
net" regression estimation procedure, including basic asymptotics and the issue
of model selection consistency. In this vein, we provide an analog to the
so-called "irrepresentable condition" which holds for the lasso. Moreover, we
outline algorithmic solutions for the structured elastic net within the
generalized linear model family. The rationale and the performance of our
approach is illustrated by means of simulated and real world data, with a focus
on signal regression.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS302 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Consistent Multitask Learning with Nonlinear Output Relations
Key to multitask learning is exploiting relationships between different tasks
to improve prediction performance. If the relations are linear, regularization
approaches can be used successfully. However, in practice assuming the tasks to
be linearly related might be restrictive, and allowing for nonlinear structures
is a challenge. In this paper, we tackle this issue by casting the problem
within the framework of structured prediction. Our main contribution is a novel
algorithm for learning multiple tasks which are related by a system of
nonlinear equations that their joint outputs need to satisfy. We show that the
algorithm is consistent and can be efficiently implemented. Experimental
results show the potential of the proposed method.Comment: 25 pages, 1 figure, 2 table
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