pwc-archive/papers-with-abstracts huggingface dataset:
Uplift Modeling from Separate Labels
Authors: Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama
Date: 2018-03-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Marketing

Uplift modeling is aimed at estimating the incremental impact of an action on
an individual's behavior, which is useful in various application domains such
as targeted marketing (advertisement campaigns) and personalized medicine
(medical treatments). Conventional methods of uplift modeling require every
instance to be jointly equipped with two types of labels: the taken action and
its outcome. However, obtaining two labels for each instance at the same time
is difficult or expensive in many real-world problems. In this paper, we
propose a novel method of uplift modeling that is applicable to a more
practical setting where only one type of labels is available for each instance.
We show a mean squared error bound for the proposed estimator and demonstrate
its effectiveness through experiments.
Generating protected fingerprint template utilizing coprime mapping transformation
Authors: Rudresh Dwivedi, Somnath Dey
Date: 2018-05-25 00:00:00

The identity of a user is permanently lost if biometric data gets compromised
since the biometric information is irreplaceable and irrevocable. To revoke and
reissue a new template in place of the compromised biometric template, the idea
of cancelable biometrics has been introduced. The concept behind cancelable
biometric is to irreversibly transform the original biometric template and
perform the comparison in the protected domain. In this paper, a coprime
transformation scheme has been proposed to derive a protected fingerprint
template. The method divides the fingerprint region into a number of sectors
with respect to each minutiae point and identifies the nearest-neighbor
minutiae in each sector. Then, ridge features for all neighboring minutiae
points are computed and mapped onto co-prime positions of a random matrix to
generate the cancelable template. The proposed approach achieves an EER of
1.82, 1.39, 4.02 and 5.77 on DB1, DB2, DB3 and DB4 datasets of the FVC2002 and
an EER of 8.70, 7.95, 5.23 and 4.87 on DB1, DB2, DB3 and DB4 datasets of
FVC2004 databases, respectively. Experimental evaluations indicate that the
method outperforms in comparison to the current state-of-the-art. Moreover, it
has been confirmed from the security analysis that the proposed method fulfills
the desired characteristics of diversity, revocability, and non-invertibility
with a minor performance degradation caused by the transformation.
Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning
Authors: Dhruv Rathi, Sushant Jain, Dr. S. Indu
Date: 2018-05-25 00:00:00
Tasks: Classification, General Classification

The target of this paper is to recommend a way for Automated classification
of Fish species. A high accuracy fish classification is required for greater
understanding of fish behavior in Ichthyology and by marine biologists.
Maintaining a ledger of the number of fishes per species and marking the
endangered species in large and small water bodies is required by concerned
institutions. Majority of available methods focus on classification of fishes
outside of water because underwater classification poses challenges such as
background noises, distortion of images, the presence of other water bodies in
images, image quality and occlusion. This method uses a novel technique based
on Convolutional Neural Networks, Deep Learning and Image Processing to achieve
an accuracy of 96.29%. This method ensures considerably discrimination accuracy
improvements than the previously proposed methods.
The (1+$λ$) Evolutionary Algorithm with Self-Adjusting Mutation Rate
Authors: Benjamin Doerr, Christian Gießen, Carsten Witt, Jing Yang
Date: 2017-04-07 00:00:00
Tasks: Evolutionary Algorithms

We propose a new way to self-adjust the mutation rate in population-based
evolutionary algorithms in discrete search spaces. Roughly speaking, it
consists of creating half the offspring with a mutation rate that is twice the
current mutation rate and the other half with half the current rate. The
mutation rate is then updated to the rate used in that subpopulation which
contains the best offspring.
  We analyze how the $(1+\lambda)$ evolutionary algorithm with this
self-adjusting mutation rate optimizes the OneMax test function. We prove that
this dynamic version of the $(1+\lambda)$ EA finds the optimum in an expected
optimization time (number of fitness evaluations) of
$O(n\lambda/\log\lambda+n\log n)$. This time is asymptotically smaller than the
optimization time of the classic $(1+\lambda)$ EA. Previous work shows that
this performance is best-possible among all $\lambda$-parallel mutation-based
unbiased black-box algorithms.
  This result shows that the new way of adjusting the mutation rate can find
optimal dynamic parameter values on the fly. Since our adjustment mechanism is
simpler than the ones previously used for adjusting the mutation rate and does
not have parameters itself, we are optimistic that it will find other
applications.
Recursive nonlinear-system identification using latent variables
Authors: Per Mattsson, Dave Zachariah, Petre Stoica
Date: 2016-06-14 00:00:00

In this paper we develop a method for learning nonlinear systems with
multiple outputs and inputs. We begin by modelling the errors of a nominal
predictor of the system using a latent variable framework. Then using the
maximum likelihood principle we derive a criterion for learning the model. The
resulting optimization problem is tackled using a majorization-minimization
approach. Finally, we develop a convex majorization technique and show that it
enables a recursive identification method. The method learns parsimonious
predictive models and is tested on both synthetic and real nonlinear systems.
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
Authors: Alireza Sepas-Moghaddam, Mohammad A. Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira
Date: 2018-05-25 00:00:00
Tasks: Face Recognition

Face recognition has attracted increasing attention due to its wide range of
applications, but it is still challenging when facing large variations in the
biometric data characteristics. Lenslet light field cameras have recently come
into prominence to capture rich spatio-angular information, thus offering new
possibilities for advanced biometric recognition systems. This paper proposes a
double-deep spatio-angular learning framework for light field based face
recognition, which is able to learn both texture and angular dynamics in
sequence using convolutional representations; this is a novel recognition
framework that has never been proposed before for either face recognition or
any other visual recognition task. The proposed double-deep learning framework
includes a long short-term memory (LSTM) recurrent network whose inputs are
VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional
neural network (CNN). The VGG-16 network uses different face viewpoints
rendered from a full light field image, which are organised as a pseudo-video
sequence. A comprehensive set of experiments has been conducted with the
IST-EURECOM light field face database, for varied and challenging recognition
tasks. Results show that the proposed framework achieves superior face
recognition performance when compared to the state-of-the-art.
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Authors: Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12

We consider stochastic gradient descent (SGD) for least-squares regression
with potentially several passes over the data. While several passes have been
widely reported to perform practically better in terms of predictive
performance on unseen data, the existing theoretical analysis of SGD suggests
that a single pass is statistically optimal. While this is true for
low-dimensional easy problems, we show that for hard problems, multiple passes
lead to statistically optimal predictions while single pass does not; we also
show that in these hard models, the optimal number of passes over the data
increases with sample size. In order to define the notion of hardness and show
that our predictive performances are optimal, we consider potentially
infinite-dimensional models and notions typically associated to kernel methods,
namely, the decay of eigenvalues of the covariance matrix of the features and
the complexity of the optimal predictor as measured through the covariance
matrix. We illustrate our results on synthetic experiments with non-linear
kernel methods and on a classical benchmark with a linear model.
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
Authors: Pratik Gajane, Ronald Ortner, Peter Auer
Date: 2018-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider reinforcement learning in changing Markov Decision Processes
where both the state-transition probabilities and the reward functions may vary
over time. For this problem setting, we propose an algorithm using a sliding
window approach and provide performance guarantees for the regret evaluated
against the optimal non-stationary policy. We also characterize the optimal
window size suitable for our algorithm. These results are complemented by a
sample complexity bound on the number of sub-optimal steps taken by the
algorithm. Finally, we present some experimental results to support our
theoretical analysis.
Incorporating Literals into Knowledge Graph Embeddings
Authors: Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
Date: 2018-02-03 00:00:00
Tasks: Entity Embeddings, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing link prediction methods. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models based on LiteralE and evaluate their performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based methods, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Authors: Michael Gadermayr, Laxmi Gupta, Barbara M. Klinkhammer, Peter Boor, Dorit Merhof
Date: 2018-05-25 00:00:00
Tasks: Image-to-Image Translation, Segmentation, Translation

Recently, generative adversarial networks exhibited excellent performances in
semi-supervised image analysis scenarios. In this paper, we go even further by
proposing a fully unsupervised approach for segmentation applications with
prior knowledge of the objects' shapes. We propose and investigate different
strategies to generate simulated label data and perform image-to-image
translation between the image and the label domain using an adversarial model.
Specifically, we assess the impact of the annotation model's accuracy as well
as the effect of simulating additional low-level image features. For
experimental evaluation, we consider the segmentation of the glomeruli, an
application scenario from renal pathology. Experiments provide proof of concept
and also confirm that the strategy for creating the simulated label data is of
particular relevance considering the stability of GAN trainings.
Stochastic algorithms with descent guarantees for ICA
Authors: Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach
Date: 2018-05-25 00:00:00

Independent component analysis (ICA) is a widespread data exploration technique, where observed signals are modeled as linear mixtures of independent components. From a machine learning point of view, it amounts to a matrix factorization problem with a statistical independence criterion. Infomax is one of the most used ICA algorithms. It is based on a loss function which is a non-convex log-likelihood. We develop a new majorization-minimization framework adapted to this loss function. We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits. First, unlike most algorithms found in the literature, the proposed methods do not rely on any critical hyper-parameter like a step size, nor do they require a line-search technique. Second, the algorithm for the finite sum setting, although stochastic, guarantees a decrease of the loss function at each iteration. Experiments demonstrate progress on the state-of-the-art for large scale datasets, without the necessity for any manual parameter tuning.
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity
Authors: Andrea Insabato, John P. Cunningham, Matthieu Gilson
Date: 2018-05-25 00:00:00

Estimation of reliable whole-brain connectivity is a crucial step towards the
use of connectivity information in quantitative approaches to the study of
neuropsychiatric disorders. When estimating brain connectivity a challenge is
imposed by the paucity of time samples and the large dimensionality of the
measurements. Bayesian estimation methods for network models offer a number of
advantages in this context but are not commonly employed. Here we compare three
different estimation methods for the multivariate Ornstein-Uhlenbeck model,
that has recently gained some popularity for characterizing whole-brain
connectivity. We first show that a Bayesian estimation of model parameters
assuming uniform priors is equivalent to an application of the method of
moments. Then, using synthetic data, we show that the Bayesian estimate scales
poorly with number of nodes in the network as compared to an iterative Lyapunov
optimization. In particular when the network size is in the order of that used
for whole-brain studies (about 100 nodes) the Bayesian method needs about eight
times more time samples than Lyapunov method in order to achieve similar
estimation accuracy. We also show that the higher estimation accuracy of
Lyapunov method is reflected in a much better classification of individuals
based on the estimated connectivity from a real dataset of BOLD fMRI. Finally
we show that the poor accuracy of Bayesian method is due to numerical errors,
when the imaginary part of the connectivity estimate gets large compared to its
real part.
Japanese Predicate Conjugation for Neural Machine Translation
Authors: Michiki Kurosawa, Yukio Matsumura, Hayahide Yamagishi, Mamoru Komachi
Date: 2018-05-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) has a drawback in that can generate only
high-frequency words owing to the computational costs of the softmax function
in the output layer.
  In Japanese-English NMT, Japanese predicate conjugation causes an increase in
vocabulary size. For example, one verb can have as many as 19 surface
varieties. In this research, we focus on predicate conjugation for compressing
the vocabulary size in Japanese. The vocabulary list is filled with the various
forms of verbs. We propose methods using predicate conjugation information
without discarding linguistic information. The proposed methods can generate
low-frequency words and deal with unknown words. Two methods were considered to
introduce conjugation information: the first considers it as a token
(conjugation token) and the second considers it as an embedded vector
(conjugation feature).
  The results using these methods demonstrate that the vocabulary size can be
compressed by approximately 86.1% (Tanaka corpus) and the NMT models can output
the words not in the training data set. Furthermore, BLEU scores improved by
0.91 points in Japanese-to-English translation, and 0.32 points in
English-to-Japanese translation with ASPEC.
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
Authors: Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy
Date: 2018-05-25 00:00:00
Tasks: Clustering, Network Embedding

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.
A dual framework for low-rank tensor completion
Authors: Madhav Nimishakavi, Pratik Jawanpuria, Bamdev Mishra
Date: 2017-12-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Riemannian optimization

One of the popular approaches for low-rank tensor completion is to use the
latent trace norm regularization. However, most existing works in this
direction learn a sparse combination of tensors. In this work, we fill this gap
by proposing a variant of the latent trace norm that helps in learning a
non-sparse combination of tensors. We develop a dual framework for solving the
low-rank tensor completion problem. We first show a novel characterization of
the dual solution space with an interesting factorization of the optimal
solution. Overall, the optimal solution is shown to lie on a Cartesian product
of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
optimization framework for proposing computationally efficient trust region
algorithm. The experiments illustrate the efficacy of the proposed algorithm on
several real-world datasets across applications.
Graph Bayesian Optimization: Algorithms, Evaluations and Applications
Authors: Jiaxu Cui, Bo Yang
Date: 2018-05-03 00:00:00
Tasks: Bayesian Optimization

Network structure optimization is a fundamental task in complex network
analysis. However, almost all the research on Bayesian optimization is aimed at
optimizing the objective functions with vectorial inputs. In this work, we
first present a flexible framework, denoted graph Bayesian optimization, to
handle arbitrary graphs in the Bayesian optimization community. By combining
the proposed framework with graph kernels, it can take full advantage of
implicit graph structural features to supplement explicit features guessed
according to the experience, such as tags of nodes and any attributes of
graphs. The proposed framework can identify which features are more important
during the optimization process. We apply the framework to solve four problems
including two evaluations and two applications to demonstrate its efficacy and
potential applications.
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Authors: Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
Date: 2018-05-25 00:00:00
Tasks: BIG-bench Machine Learning

We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generalizes previous results that assumed a majority of non-faulty nodes; we need assume only one non-faulty worker. Our key idea is to suspect workers that are potentially defective. Since this is likely to lead to false positives, we use a ranking-based preference mechanism. We prove the convergence of SGD for non-convex problems under these scenarios. Experimental results show that Zeno outperforms existing approaches.
DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification
Authors: Vineet Mehta, Devendra Pratap Yadav, Sai Srinadhu Katta, Abhinav Dhall
Date: 2018-05-25 00:00:00
Tasks: Person Identification

Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.
Key Person Aided Re-identification in Partially Ordered Pedestrian Set
Authors: Chen Chen, Min Cao, Xiyuan Hu, Silong Peng
Date: 2018-05-25 00:00:00
Tasks: Person Re-Identification

Ideally person re-identification seeks for perfect feature representation and
metric model that re-identify all various pedestrians well in non-overlapping
views at different locations with different camera configurations, which is
very challenging. However, in most pedestrian sets, there always are some
outstanding persons who are relatively easy to re-identify. Inspired by the
existence of such data division, we propose a novel key person aided person
re-identification framework based on the re-defined partially ordered
pedestrian sets. The outstanding persons, namely "key persons", are selected by
the K-nearest neighbor based saliency measurement. The partial order defined by
pedestrian entering time in surveillance associates the key persons with the
query person temporally and helps to locate the possible candidates.
Experiments conducted on two video datasets show that the proposed key person
aided framework outperforms the state-of-the-art methods and improves the
matching accuracy greatly at all ranks.