pwc-archive/papers-with-abstracts huggingface dataset:
Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
Authors: Subhadeep Paul, Yuguo Chen
Date: 2015-06-08 00:00:00
Tasks: Community Detection, Stochastic Block Model

In recent years there has been an increased interest in statistical analysis
of data with multiple types of relations among a set of entities. Such
multi-relational data can be represented as multi-layer graphs where the set of
vertices represents the entities and multiple types of edges represent the
different relations among them. For community detection in multi-layer graphs,
we consider two random graph models, the multi-layer stochastic blockmodel
(MLSBM) and a model with a restricted parameter space, the restricted
multi-layer stochastic blockmodel (RMLSBM). We derive consistency results for
community assignments of the maximum likelihood estimators (MLEs) in both
models where MLSBM is assumed to be the true model, and either the number of
nodes or the number of types of edges or both grow. We compare MLEs in the two
models with other baseline approaches, such as separate modeling of layers,
aggregating the layers and majority voting. RMLSBM is shown to have advantage
over MLSBM when either the growth rate of the number of communities is high or
the growth rate of the average degree of the component graphs in the
multi-graph is low. We also derive minimax rates of error and sharp thresholds
for achieving consistency of community detection in both models, which are then
used to compare the multi-layer models with a baseline model, the aggregate
stochastic block model. The simulation studies and real data applications
confirm the superior performance of the multi-layer approaches in comparison to
the baseline procedures.
Cluster-Aided Mobility Predictions
Authors: Jaeseong Jeong, Mathieu Leconte, Alexandre Proutiere
Date: 2015-07-12 00:00:00
Tasks: Clustering

Predicting the future location of users in wireless net- works has numerous
applications, and can help service providers to improve the quality of service
perceived by their clients. The location predictors proposed so far estimate
the next location of a specific user by inspecting the past individual
trajectories of this user. As a consequence, when the training data collected
for a given user is limited, the resulting prediction is inaccurate. In this
paper, we develop cluster-aided predictors that exploit past trajectories
collected from all users to predict the next location of a given user. These
predictors rely on clustering techniques and extract from the training data
similarities among the mobility patterns of the various users to improve the
prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility
Predictor), a cluster-aided predictor whose design is based on recent
non-parametric bayesian statistical tools. CAMP is robust and adaptive in the
sense that it exploits similarities in users' mobility only if such
similarities are really present in the training data. We analytically prove the
consistency of the predictions provided by CAMP, and investigate its
performance using two large-scale datasets. CAMP significantly outperforms
existing predictors, and in particular those that only exploit individual past
trajectories.
SYNTAGMA. A Linguistic Approach to Parsing
Authors: Daniel Christen
Date: 2013-03-24 00:00:00

SYNTAGMA is a rule-based parsing system, structured on two levels: a general
parsing engine and a language specific grammar. The parsing engine is a
language independent program, while grammar and language specific rules and
resources are given as text files, consisting in a list of constituent
structuresand a lexical database with word sense related features and
constraints. Since its theoretical background is principally Tesniere's
Elements de syntaxe, SYNTAGMA's grammar emphasizes the role of argument
structure (valency) in constraint satisfaction, and allows also horizontal
bounds, for instance treating coordination. Notions such as Pro, traces, empty
categories are derived from Generative Grammar and some solutions are close to
Government&Binding Theory, although they are the result of an autonomous
research. These properties allow SYNTAGMA to manage complex syntactic
configurations and well known weak points in parsing engineering. An important
resource is the semantic network, which is used in disambiguation tasks.
Parsing process follows a bottom-up, rule driven strategy. Its behavior can be
controlled and fine-tuned.
Syntax-Semantics Interaction Parsing Strategies. Inside SYNTAGMA
Authors: Daniel Christen
Date: 2016-01-21 00:00:00
Tasks: Management, Word Sense Disambiguation

This paper discusses SYNTAGMA, a rule based NLP system addressing the tricky
issues of syntactic ambiguity reduction and word sense disambiguation as well
as providing innovative and original solutions for constituent generation and
constraints management. To provide an insight into how it operates, the
system's general architecture and components, as well as its lexical, syntactic
and semantic resources are described. After that, the paper addresses the
mechanism that performs selective parsing through an interaction between
syntactic and semantic information, leading the parser to a coherent and
accurate interpretation of the input text.
Spatial Scaling of Satellite Soil Moisture using Temporal Correlations and Ensemble Learning
Authors: Subit Chakrabarti, Jasmeet Judge, Tara Bongiovanni, Anand Rangarajan, Sanjay Ranka
Date: 2016-01-21 00:00:00
Tasks: Ensemble Learning, regression

A novel algorithm is developed to downscale soil moisture (SM), obtained at
satellite scales of 10-40 km by utilizing its temporal correlations to
historical auxiliary data at finer scales. Including such correlations
drastically reduces the size of the training set needed, accounts for
time-lagged relationships, and enables downscaling even in the presence of
short gaps in the auxiliary data. The algorithm is based upon bagged regression
trees (BRT) and uses correlations between high-resolution remote sensing
products and SM observations. The algorithm trains multiple regression trees
and automatically chooses the trees that generate the best downscaled
estimates. The algorithm was evaluated using a multi-scale synthetic dataset in
north central Florida for two years, including two growing seasons of corn and
one growing season of cotton per year. The time-averaged error across the
region was found to be 0.01 $\mathrm{m}^3/\mathrm{m}^3$, with a standard
deviation of 0.012 $\mathrm{m}^3/\mathrm{m}^3$ when 0.02% of the data were used
for training in addition to temporal correlations from the past seven days, and
all available data from the past year. The maximum spatially averaged errors
obtained using this algorithm in downscaled SM were 0.005
$\mathrm{m}^3/\mathrm{m}^3$, for pixels with cotton land-cover. When land
surface temperature~(LST) on the day of downscaling was not included in the
algorithm to simulate "data gaps", the spatially averaged error increased
minimally by 0.015 $\mathrm{m}^3/\mathrm{m}^3$ when LST is unavailable on the
day of downscaling. The results indicate that the BRT-based algorithm provides
high accuracy for downscaling SM using complex non-linear spatio-temporal
correlations, under heterogeneous micro meteorological conditions.
A Confidence-Based Approach for Balancing Fairness and Accuracy
Authors: Benjamin Fish, Jeremy Kun, Ádám D. Lelkes
Date: 2016-01-21 00:00:00
Tasks: Fairness

We study three classical machine learning algorithms in the context of
algorithmic fairness: adaptive boosting, support vector machines, and logistic
regression. Our goal is to maintain the high accuracy of these learning
algorithms while reducing the degree to which they discriminate against
individuals because of their membership in a protected group.
  Our first contribution is a method for achieving fairness by shifting the
decision boundary for the protected group. The method is based on the theory of
margins for boosting. Our method performs comparably to or outperforms previous
algorithms in the fairness literature in terms of accuracy and low
discrimination, while simultaneously allowing for a fast and transparent
quantification of the trade-off between bias and error.
  Our second contribution addresses the shortcomings of the bias-error
trade-off studied in most of the algorithmic fairness literature. We
demonstrate that even hopelessly naive modifications of a biased algorithm,
which cannot be reasonably said to be fair, can still achieve low bias and high
accuracy. To help to distinguish between these naive algorithms and more
sensible algorithms we propose a new measure of fairness, called resilience to
random bias (RRB). We demonstrate that RRB distinguishes well between our naive
and sensible fairness algorithms. RRB together with bias and accuracy provides
a more complete picture of the fairness of an algorithm.
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
Authors: Peter Schulam, Suchi Saria
Date: 2016-01-18 00:00:00
Proceeding: NeurIPS 2015 12

For many complex diseases, there is a wide variety of ways in which an
individual can manifest the disease. The challenge of personalized medicine is
to develop tools that can accurately predict the trajectory of an individual's
disease, which can in turn enable clinicians to optimize treatments. We
represent an individual's disease trajectory as a continuous-valued
continuous-time function describing the severity of the disease over time. We
propose a hierarchical latent variable model that individualizes predictions of
disease trajectories. This model shares statistical strength across
observations at different resolutions--the population, subpopulation and the
individual level. We describe an algorithm for learning population and
subpopulation parameters offline, and an online procedure for dynamically
learning individual-specific parameters. Finally, we validate our model on the
task of predicting the course of interstitial lung disease, a leading cause of
death among patients with the autoimmune disease scleroderma. We compare our
approach against state-of-the-art and demonstrate significant improvements in
predictive accuracy.
Network Topology Identification using PCA and its Graph Theoretic Interpretations
Authors: Aravind Rajeswaran, Shankar Narasimhan
Date: 2015-06-01 00:00:00

We solve the problem of identifying (reconstructing) network topology from
steady state network measurements. Concretely, given only a data matrix
$\mathbf{X}$ where the $X_{ij}$ entry corresponds to flow in edge $i$ in
configuration (steady-state) $j$, we wish to find a network structure for which
flow conservation is obeyed at all the nodes. This models many network problems
involving conserved quantities like water, power, and metabolic networks. We
show that identification is equivalent to learning a model $\mathbf{A_n}$ which
captures the approximate linear relationships between the different variables
comprising $\mathbf{X}$ (i.e. of the form $\mathbf{A_n X \approx 0}$) such that
$\mathbf{A_n}$ is full rank (highest possible) and consistent with a network
node-edge incidence structure. The problem is solved through a sequence of
steps like estimating approximate linear relationships using Principal
Component Analysis, obtaining f-cut-sets from these approximate relationships,
and graph realization from f-cut-sets (or equivalently f-circuits). Each step
and the overall process is polynomial time. The method is illustrated by
identifying topology of a water distribution network. We also study the extent
of identifiability from steady-state data.
Sparse Recovery via Differential Inclusions
Authors: Stanley Osher, Feng Ruan, Jiechao Xiong, Yuan YAO, Wotao Yin
Date: 2014-06-30 00:00:00

In this paper, we recover sparse signals from their noisy linear measurements
by solving nonlinear differential inclusions, which is based on the notion of
inverse scale space (ISS) developed in applied mathematics. Our goal here is to
bring this idea to address a challenging problem in statistics, \emph{i.e.}
finding the oracle estimator which is unbiased and sign-consistent using
dynamics. We call our dynamics \emph{Bregman ISS} and \emph{Linearized Bregman
ISS}. A well-known shortcoming of LASSO and any convex regularization
approaches lies in the bias of estimators. However, we show that under proper
conditions, there exists a bias-free and sign-consistent point on the solution
paths of such dynamics, which corresponds to a signal that is the unbiased
estimate of the true signal and whose entries have the same signs as those of
the true signs, \emph{i.e.} the oracle estimator. Therefore, their solution
paths are regularization paths better than the LASSO regularization path, since
the points on the latter path are biased when sign-consistency is reached. We
also show how to efficiently compute their solution paths in both continuous
and discretized settings: the full solution paths can be exactly computed piece
by piece, and a discretization leads to \emph{Linearized Bregman iteration},
which is a simple iterative thresholding rule and easy to parallelize.
Theoretical guarantees such as sign-consistency and minimax optimal $l_2$-error
bounds are established in both continuous and discrete settings for specific
points on the paths. Early-stopping rules for identifying these points are
given. The key treatment relies on the development of differential inequalities
for differential inclusions and their discretizations, which extends the
previous results and leads to exponentially fast recovering of sparse signals
before selecting wrong ones.
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
Authors: Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis
Date: 2016-01-21 00:00:00
Tasks: Quantization

While the harmonic function solution performs well in many semi-supervised
learning (SSL) tasks, it is known to scale poorly with the number of samples.
Recent successful and scalable methods, such as the eigenfunction method focus
on efficiently approximating the whole spectrum of the graph Laplacian
constructed from the data. This is in contrast to various subsampling and
quantization methods proposed in the past, which may fail in preserving the
graph spectra. However, the impact of the approximation of the spectrum on the
final generalization error is either unknown, or requires strong assumptions on
the data. In this paper, we introduce Sparse-HFS, an efficient
edge-sparsification algorithm for SSL. By constructing an edge-sparse and
spectrally similar graph, we are able to leverage the approximation guarantees
of spectral sparsification methods to bound the generalization error of
Sparse-HFS. As a result, we obtain a theoretically-grounded approximation
scheme for graph-based SSL that also empirically matches the performance of
known large-scale methods.
Finding structure in data using multivariate tree boosting
Authors: Patrick J. Miller, Gitta H. Lubke, Daniel B. McArtor, C. S. Bergeman
Date: 2015-11-06 00:00:00
Tasks: regression

Technology and collaboration enable dramatic increases in the size of
psychological and psychiatric data collections, but finding structure in these
large data sets with many collected variables is challenging. Decision tree
ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful
tool for finding structure, but are difficult to interpret with multiple
outcome variables which are often of interest in psychology. To find and
interpret structure in data sets with multiple outcomes and many predictors
(possibly exceeding the sample size), we introduce a multivariate extension to
a decision tree ensemble method called Gradient Boosted Regression Trees
(Friedman, 2001). Our method, multivariate tree boosting, can be used for
identifying important predictors, detecting predictors with non-linear effects
and interactions without specification of such effects, and for identifying
predictors that cause two or more outcome variables to covary without
parametric assumptions. We provide the R package 'mvtboost' to estimate, tune,
and interpret the resulting model, which extends the implementation of
univariate boosting in the R package 'gbm' (Ridgeway, 2013) to continuous,
multivariate outcomes. To illustrate the approach, we analyze predictors of
psychological well-being (Ryff and Keyes, 1995). Simulations verify that our
approach identifies predictors with non-linear effects and achieves high
prediction accuracy, exceeding or matching the performance of (penalized)
multivariate multiple regression and multivariate decision trees over a wide
range of conditions.
Model-Coupled Autoencoder for Time Series Visualisation
Authors: Nikolaos Gianniotis, Sven D. Kügler, Peter Tiňo, Kai L. Polsterer
Date: 2016-01-21 00:00:00
Tasks: Dimensionality Reduction, model, Time Series, Time Series Analysis

We present an approach for the visualisation of a set of time series that
combines an echo state network with an autoencoder. For each time series in the
dataset we train an echo state network, using a common and fixed reservoir of
hidden neurons, and use the optimised readout weights as the new
representation. Dimensionality reduction is then performed via an autoencoder
on the readout weight representations. The crux of the work is to equip the
autoencoder with a loss function that correctly interprets the reconstructed
readout weights by associating them with a reconstruction error measured in the
data space of sequences. This essentially amounts to measuring the predictive
performance that the reconstructed readout weights exhibit on their
corresponding sequences when plugged back into the echo state network with the
same fixed reservoir. We demonstrate that the proposed visualisation framework
can deal both with real valued sequences as well as binary sequences. We derive
magnification factors in order to analyse distance preservations and
distortions in the visualisation space. The versatility and advantages of the
proposed method are demonstrated on datasets of time series that originate from
diverse domains.
B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction
Authors: Weilong Peng, Zhiyong Feng, Chao Xu
Date: 2016-01-21 00:00:00
Tasks: Face Reconstruction, Form

Recently, many methods have been proposed for face reconstruction from
multiple images, most of which involve fundamental principles of Shape from
Shading and Structure from motion. However, a majority of the methods just
generate discrete surface model of face. In this paper, B-spline Shape from
Motion and Shading (BsSfMS) is proposed to reconstruct continuous B-spline
surface for multi-view face images, according to an assumption that shading and
motion information in the images contain 1st- and 0th-order derivative of
B-spline face respectively. Face surface is expressed as a B-spline surface
that can be reconstructed by optimizing B-spline control points. Therefore,
normals and 3D feature points computed from shading and motion of images
respectively are used as the 1st- and 0th- order derivative information, to be
jointly applied in optimizing the B-spline face. Additionally, an IMLS
(iterative multi-least-square) algorithm is proposed to handle the difficult
control point optimization. Furthermore, synthetic samples and LFW dataset are
introduced and conducted to verify the proposed approach, and the experimental
results demonstrate the effectiveness with different poses, illuminations,
expressions etc., even with wild images.
Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
Authors: Hui Li, Chunhua Shen
Date: 2016-01-21 00:00:00
Tasks: License Plate Detection, Segmentation

In this work, we tackle the problem of car license plate detection and
recognition in natural scene images. Inspired by the success of deep neural
networks (DNNs) in various vision applications, here we leverage DNNs to learn
high-level features in a cascade framework, which lead to improved performance
on both detection and recognition.
  Firstly, we train a $37$-class convolutional neural network (CNN) to detect
all characters in an image, which results in a high recall, compared with
conventional approaches such as training a binary text/non-text classifier.
False positives are then eliminated by the second plate/non-plate CNN
classifier. Bounding box refinement is then carried out based on the edge
information of the license plates, in order to improve the
intersection-over-union (IoU) ratio. The proposed cascade framework extracts
license plates effectively with both high recall and precision. Last, we
propose to recognize the license characters as a {sequence labelling} problem.
A recurrent neural network (RNN) with long short-term memory (LSTM) is trained
to recognize the sequential features extracted from the whole license plate via
CNNs. The main advantage of this approach is that it is segmentation free. By
exploring context information and avoiding errors caused by segmentation, the
RNN method performs better than a baseline method of combining segmentation and
deep CNN classification; and achieves state-of-the-art recognition accuracy.
Automatic 3D modelling of craniofacial form
Authors: Nick Pears, Christian Duncan
Date: 2016-01-21 00:00:00
Tasks: Form

Three-dimensional models of craniofacial variation over the general
population are useful for assessing pre- and post-operative head shape when
treating various craniofacial conditions, such as craniosynostosis. We present
a new method of automatically building both sagittal profile models and full 3D
surface models of the human head using a range of techniques in 3D surface
image analysis; in particular, automatic facial landmarking using supervised
machine learning, global and local symmetry plane detection using a variant of
trimmed iterative closest points, locally-affine template warping (for full 3D
models) and a novel pose normalisation using robust iterative ellipse fitting.
The PCA-based models built using the new pose normalisation are more compact
than those using Generalised Procrustes Analysis and we demonstrate their
utility in a clinical case study.
MOCICE-BCubed F$_1$: A New Evaluation Measure for Biclustering Algorithms
Authors: Henry Rosales-Méndez, Yunior Ramírez-Cruz
Date: 2015-12-01 00:00:00

The validation of biclustering algorithms remains a challenging task, even
though a number of measures have been proposed for evaluating the quality of
these algorithms. Although no criterion is universally accepted as the overall
best, a number of meta-evaluation conditions to be satisfied by biclustering
algorithms have been enunciated. In this work, we present MOCICE-BCubed F$_1$,
a new external measure for evaluating biclusterings, in the scenario where gold
standard annotations are available for both the object clusters and the
associated feature subspaces. Our proposal relies on the so-called
micro-objects transformation and satisfies the most comprehensive set of
meta-evaluation conditions so far enunciated for biclusterings. Additionally,
the proposed measure adequately handles the occurrence of overlapping in both
the object and feature spaces. Moreover, when used for evaluating traditional
clusterings, which are viewed as a particular case of biclustering, the
proposed measure also satisfies the most comprehensive set of meta-evaluation
conditions so far enunciated for this task.