pwc-archive/papers-with-abstracts huggingface dataset:
Ring Migration Topology Helps Bypassing Local Optima
Authors: Clemens Frahnow, Timo Kötzing
Date: 2018-06-04 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Running several evolutionary algorithms in parallel and occasionally
exchanging good solutions is referred to as island models. The idea is that the
independence of the different islands leads to diversity, thus possibly
exploring the search space better. Many theoretical analyses so far have found
a complete (or sufficiently quickly expanding) topology as underlying migration
graph most efficient for optimization, even though a quick dissemination of
individuals leads to a loss of diversity. We suggest a simple fitness function
FORK with two local optima parametrized by $r \geq 2$ and a scheme for
composite fitness functions. We show that, while the (1+1) EA gets stuck in a
bad local optimum and incurs a run time of $\Theta(n^{2r})$ fitness evaluations
on FORK, island models with a complete topology can achieve a run time of
$\Theta(n^{1.5r})$ by making use of rare migrations in order to explore the
search space more effectively. Finally, the ring topology, making use of rare
migrations and a large diameter, can achieve a run time of
$\tilde{\Theta}(n^r)$, the black box complexity of FORK. This shows that the
ring topology can be preferable over the complete topology in order to maintain
diversity.
Modeling Realistic Degradations in Non-blind Deconvolution
Authors: Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo
Date: 2018-06-04 00:00:00
Tasks: Deblurring, Image Deblurring, Quantization, Stochastic Optimization

Most image deblurring methods assume an over-simplistic image formation model
and as a result are sensitive to more realistic image degradations. We propose
a novel variational framework, that explicitly handles pixel saturation, noise,
quantization, as well as non-linear camera response function due to e.g., gamma
correction. We show that accurately modeling a more realistic image acquisition
pipeline leads to significant improvements, both in terms of image quality and
PSNR. Furthermore, we show that incorporating the non-linear response in both
the data and the regularization terms of the proposed energy leads to a more
detailed restoration than a naive inversion of the non-linear curve. The
minimization of the proposed energy is performed using stochastic optimization.
A dataset consisting of realistically degraded images is created in order to
evaluate the method.
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
Authors: Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf
Date: 2018-06-04 00:00:00
Tasks: Causal Inference, EEG, Electroencephalogram (EEG)

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding. It extends the ordinary ICA model in a theoretically sound and explicit way to incorporate group-wise (or environment-wise) confounding. We show that our proposed general noise model allows to perform ICA in settings where other noisy ICA procedures fail. Additionally, it can be used for applications with grouped data by adjusting for different stationary noise within each group. Our proposed noise model has a natural relation to causality and we explain how it can be applied in the context of causal inference. In addition to our theoretical framework, we provide an efficient estimation procedure and prove identifiability of the unmixing matrix under mild assumptions. Finally, we illustrate the performance and robustness of our method on simulated data, provide audible and visual examples, and demonstrate the applicability to real-world scenarios by experiments on publicly available Antarctic ice core data as well as two EEG data sets. We provide a scikit-learn compatible pip-installable Python package coroICA as well as R and Matlab implementations accompanied by a documentation at https://sweichwald.de/coroICA/
The Expanding Approvals Rule: Improving Proportional Representation and Monotonicity
Authors: Haris Aziz, Barton Lee
Date: 2017-08-25 00:00:00

Proportional representation (PR) is often discussed in voting settings as a
major desideratum. For the past century or so, it is common both in practice
and in the academic literature to jump to single transferable vote (STV) as the
solution for achieving PR. Some of the most prominent electoral reform
movements around the globe are pushing for the adoption of STV. It has been
termed a major open problem to design a voting rule that satisfies the same PR
properties as STV and better monotonicity properties. In this paper, we first
present a taxonomy of proportional representation axioms for general weak order
preferences, some of which generalise and strengthen previously introduced
concepts. We then present a rule called Expanding Approvals Rule (EAR) that
satisfies properties stronger than the central PR axiom satisfied by STV, can
handle indifferences in a convenient and computationally efficient manner, and
also satisfies better candidate monotonicity properties. In view of this, our
proposed rule seems to be a compelling solution for achieving proportional
representation in voting settings.
$\ell_1$-regression with Heavy-tailed Distributions
Authors: Lijun Zhang, Zhi-Hua Zhou
Date: 2018-05-02 00:00:00
Proceeding: NeurIPS 2018
Tasks: regression

In this paper, we consider the problem of linear regression with heavy-tailed
distributions. Different from previous studies that use the squared loss to
measure the performance, we choose the absolute loss, which is capable of
estimating the conditional median. To address the challenge that both the input
and output could be heavy-tailed, we propose a truncated minimization problem,
and demonstrate that it enjoys an $\widetilde{O}(\sqrt{d/n})$ excess risk,
where $d$ is the dimensionality and $n$ is the number of samples. Compared with
traditional work on $\ell_1$-regression, the main advantage of our result is
that we achieve a high-probability risk bound without exponential moment
conditions on the input and output. Furthermore, if the input is bounded, we
show that the classical empirical risk minimization is competent for
$\ell_1$-regression even when the output is heavy-tailed.
Dynamic Regret of Strongly Adaptive Methods
Authors: Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
Date: 2017-01-26 00:00:00
Proceeding: ICML 2018 7

To cope with changing environments, recent developments in online learning
have introduced the concepts of adaptive regret and dynamic regret
independently. In this paper, we illustrate an intrinsic connection between
these two concepts by showing that the dynamic regret can be expressed in terms
of the adaptive regret and the functional variation. This observation implies
that strongly adaptive algorithms can be directly leveraged to minimize the
dynamic regret. As a result, we present a series of strongly adaptive
algorithms that have small dynamic regrets for convex functions, exponentially
concave functions, and strongly convex functions, respectively. To the best of
our knowledge, this is the first time that exponential concavity is utilized to
upper bound the dynamic regret. Moreover, all of those adaptive algorithms do
not need any prior knowledge of the functional variation, which is a
significant advantage over previous specialized methods for minimizing dynamic
regret.
Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models
Authors: Nathan Kallus, Michele Santacatterina
Date: 2018-06-04 00:00:00

Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to model misspecification. Various methods have been proposed to partially address this, including truncation and stabilized-IPTW to temper extreme weights and covariate balancing propensity score (CBPS) to address treatment model misspecification. In this paper, we present Kernel Optimal Weighting (KOW), a convex-optimization-based approach that finds weights for fitting the MSM that optimally balance time-dependent confounders while simultaneously controlling for precision, directly addressing the above limitations. KOW directly minimizes the error in estimation due to time-dependent confounding via a new decomposition as a functional. We further extend KOW to control for informative censoring. We evaluate the performance of KOW in a simulation study, comparing it with IPTW, stabilized-IPTW, and CBPS. We demonstrate the use of KOW in studying the effect of treatment initiation on time-to-death among people living with HIV and the effect of negative advertising on elections in the United States.
Wasserstein Variational Inference
Authors: Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, Variational Inference

This paper introduces Wasserstein variational inference, a new form of
approximate Bayesian inference based on optimal transport theory. Wasserstein
variational inference uses a new family of divergences that includes both
f-divergences and the Wasserstein distance as special cases. The gradients of
the Wasserstein variational loss are obtained by backpropagating through the
Sinkhorn iterations. This technique results in a very stable likelihood-free
training method that can be used with implicit distributions and probabilistic
programs. Using the Wasserstein variational inference framework, we introduce
several new forms of autoencoders and test their robustness and performance
against existing variational autoencoding techniques.
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
Authors: Benjamin Gutierrez-Becker, Christian Wachinger
Date: 2018-06-04 00:00:00
Tasks: Anatomy, regression

We propose a deep neural network for supervised learning on neuroanatomical
shapes. The network directly operates on raw point clouds without the need for
mesh processing or the identification of point correspondences, as spatial
transformer networks map the data to a canonical space. Instead of relying on
hand-crafted shape descriptors, an optimal representation is learned in the
end-to-end training stage of the network. The proposed network consists of
multiple branches, so that features for multiple structures are learned
simultaneously. We demonstrate the performance of our method on two
applications: (i) the prediction of Alzheimer's disease and mild cognitive
impairment and (ii) the regression of the brain age. Finally, we visualize the
important parts of the anatomy for the prediction by adapting the occlusion
method to point clouds.
Open Information Extraction on Scientific Text: An Evaluation
Authors: Paul Groth, Michael Lauruhn, Antony Scerri, Ron Daniel Jr
Date: 2018-02-15 00:00:00
Proceeding: COLING 2018 8
Tasks: Knowledge Base Construction, Open Information Extraction, Question Answering, Relation Extraction

Open Information Extraction (OIE) is the task of the unsupervised creation of
structured information from text. OIE is often used as a starting point for a
number of downstream tasks including knowledge base construction, relation
extraction, and question answering. While OIE methods are targeted at being
domain independent, they have been evaluated primarily on newspaper,
encyclopedic or general web text. In this article, we evaluate the performance
of OIE on scientific texts originating from 10 different disciplines. To do so,
we use two state-of-the-art OIE systems applying a crowd-sourcing approach. We
find that OIE systems perform significantly worse on scientific text than
encyclopedic text. We also provide an error analysis and suggest areas of work
to reduce errors. Our corpus of sentences and judgments are made available.
A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder
Authors: Scott H Lee, Matthew J Maenner, Charles M Heilig
Date: 2018-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Document Classification, General Classification

The Centers for Disease Control and Prevention (CDC) coordinates a
labor-intensive process to measure the prevalence of autism spectrum disorder
(ASD) among children in the United States. Random forests methods have shown
promise in speeding up this process, but they lag behind human classification
accuracy by about 5%. We explore whether more recently available document
classification algorithms can close this gap. We applied 8 supervised learning
algorithms to predict whether children meet the case definition for ASD based
solely on the words in their evaluations. We compared the algorithms'
performance across 10 random train-test splits of the data, using
classification accuracy, F1 score, and number of positive calls to evaluate
their potential use for surveillance. Across the 10 train-test cycles, the
random forest and support vector machine with Naive Bayes features (NB-SVM)
each achieved slightly more than 87% mean accuracy. The NB-SVM produced
significantly more false negatives than false positives (P = 0.027), but the
random forest did not, making its prevalence estimates very close to the true
prevalence in the data. The best-performing neural network performed similarly
to the random forest on both measures. The random forest performed as well as
more recently available models like the NB-SVM and the neural network, and it
also produced good prevalence estimates. NB-SVM may not be a good candidate for
use in a fully-automated surveillance workflow due to increased false
negatives. More sophisticated algorithms, like hierarchical convolutional
neural networks, may not be feasible to train due to characteristics of the
data. Current algorithms might perform better if the data are abstracted and
processed differently and if they take into account information about the
children in addition to their evaluations.
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
Authors: Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
Date: 2018-06-04 00:00:00
Tasks: Decision Making, Fairness, Learning-To-Rank

People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
Authors: Jindong Jiang, Lunan Zheng, Fei Luo, Zhijun Zhang
Date: 2018-06-04 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Indoor semantic segmentation has always been a difficult task in computer
vision. In this paper, we propose an RGB-D residual encoder-decoder
architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet,
the residual module is applied to both the encoder and decoder as the basic
building block, and the skip-connection is used to bypass the spatial feature
between the encoder and decoder. In order to incorporate the depth information
of the scene, a fusion structure is constructed, which makes inference on RGB
image and depth image separately, and fuses their features over several layers.
In order to efficiently optimize the network's parameters, we propose a
`pyramid supervision' training scheme, which applies supervised learning over
different layers in the decoder, to cope with the problem of gradients
vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves
a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.
Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
Authors: Farid Khosravikia, Yasaman Zeinali, Zoltan Nagy, Patricia Clayton, Ellen M. Rathje
Date: 2018-06-04 00:00:00
Tasks: motion prediction, Prediction

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of
seismicity in recent years, providing new datasets of earthquake recordings to
develop ground motion prediction models for this particular region of the
Central and Eastern North America (CENA). This paper outlines a framework for
using Artificial Neural Networks (ANNs) to develop attenuation models from the
ground motion recordings in this region. While attenuation models exist for the
CENA, concerns over the increased rate of seismicity in this region necessitate
investigation of ground motions prediction models particular to these states.
To do so, an ANN-based framework is proposed to predict peak ground
acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake
source-to-site distance, and shear wave velocity. In this framework,
approximately 4,500 ground motions with magnitude greater than 3.0 recorded in
these three states (Texas, Oklahoma, and Kansas) since 2005 are considered.
Results from this study suggest that existing ground motion prediction models
developed for CENA do not accurately predict the ground motion intensity
measures for earthquakes in this region, especially for those with low
source-to-site distances or on very soft soil conditions. The proposed ANN
models provide much more accurate prediction of the ground motion intensity
measures at all distances and magnitudes. The proposed ANN models are also
converted to relatively simple mathematical equations so that engineers can
easily use them to predict the ground motion intensity measures for future
events. Finally, through a sensitivity analysis, the contributions of the
predictive parameters to the prediction of the considered intensity measures
are investigated.
Deep Face Recognition: A Survey
Authors: Mei Wang, Weihong Deng
Date: 2018-04-18 00:00:00
Tasks: Face Recognition, Face Verification, Miscellaneous, Survey

Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and fostered successful real-world applications. In this survey, we provide a comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes. First, we summarize different network architectures and loss functions proposed in the rapid evolution of the deep FR methods. Second, the related face processing methods are categorized into two classes: "one-to-many augmentation" and "many-to-one normalization". Then, we summarize and compare the commonly used databases for both model training and evaluation. Third, we review miscellaneous scenes in deep FR, such as cross-factor, heterogenous, multiple-media and industrial scenes. Finally, the technical challenges and several promising directions are highlighted.
Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps
Authors: Tobias Hecking, Loet Leydesdorff
Date: 2018-06-04 00:00:00
Tasks: Decision Making, Topic Models

Using the 6,638 case descriptions of societal impact submitted for evaluation
in the Research Excellence Framework (REF 2014), we replicate the topic model
(Latent Dirichlet Allocation or LDA) made in this context and compare the
results with factor-analytic results using a traditional word-document matrix
(Principal Component Analysis or PCA). Removing a small fraction of documents
from the sample, for example, has on average a much larger impact on LDA than
on PCA-based models to the extent that the largest distortion in the case of
PCA has less effect than the smallest distortion of LDA-based models. In terms
of semantic coherence, however, LDA models outperform PCA-based models. The
topic models inform us about the statistical properties of the document sets
under study, but the results are statistical and should not be used for a
semantic interpretation - for example, in grant selections and micro-decision
making, or scholarly work-without follow-up using domain-specific semantic
maps.
A Desirability-Based Axiomatisation for Coherent Choice Functions
Authors: Jasper De Bock, Gert de Cooman
Date: 2018-06-04 00:00:00

Choice functions constitute a simple, direct and very general mathematical
framework for modelling choice under uncertainty. In particular, they are able
to represent the set-valued choices that typically arise from applying decision
rules to imprecise-probabilistic uncertainty models. We provide them with a
clear interpretation in terms of attitudes towards gambling, borrowing ideas
from the theory of sets of desirable gambles, and we use this interpretation to
derive a set of basic axioms. We show that these axioms lead to a full-fledged
theory of coherent choice functions, which includes a representation in terms
of sets of desirable gambles, and a conservative inference method.
A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images
Authors: Titinunt Kitrungrotsakul, Xian-Hau Han, Yutaro Iwamoto, Satoko Takemoto, Hideo Yokota, Sari Ipponjima, Tomomi Nemoto, Xiong Wei, Yen-Wei Chen
Date: 2018-06-04 00:00:00
Tasks: Cell Detection

In recent years, intravital skin imaging has been increasingly used in
mammalian skin research to investigate cell behaviors. A fundamental step of
the investigation is mitotic cell (cell division) detection. Because of the
complex backgrounds (normal cells), the majority of the existing methods cause
several false positives. In this paper, we proposed a 2.5D cascaded end-to-end
convolutional neural network (CasDetNet) with temporal information to
accurately detect automatic mitotic cell in 4D microscopic images with few
training data. The CasDetNet consists of two 2.5D networks. The first one is
used for detecting candidate cells with only volume information and the second
one, containing temporal information, for reducing false positive and adding
mitotic cells that were missed in the first step. The experimental results show
that our CasDetNet can achieve higher precision and recall compared to other
state-of-the-art methods.