pwc-archive/papers-with-abstracts huggingface dataset:
FairGAN: Fairness-aware Generative Adversarial Networks
Authors: Depeng Xu, Shuhan Yuan, Lu Zhang, Xintao Wu
Date: 2018-05-28 00:00:00
Tasks: Fairness, General Classification

Fairness-aware learning is increasingly important in data mining.
Discrimination prevention aims to prevent discrimination in the training data
before it is used to conduct predictive analysis. In this paper, we focus on
fair data generation that ensures the generated data is discrimination free.
Inspired by generative adversarial networks (GAN), we present fairness-aware
generative adversarial networks, called FairGAN, which are able to learn a
generator producing fair data and also preserving good data utility. Compared
with the naive fair data generation models, FairGAN further ensures the
classifiers which are trained on generated data can achieve fair classification
on real data. Experiments on a real dataset show the effectiveness of FairGAN.
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Authors: Trevor Campbell, Tamara Broderick
Date: 2018-02-05 00:00:00
Proceeding: ICML 2018 7
Tasks: Uncertainty Quantification

Coherent uncertainty quantification is a key strength of Bayesian methods.
But modern algorithms for approximate Bayesian posterior inference often
sacrifice accurate posterior uncertainty estimation in the pursuit of
scalability. This work shows that previous Bayesian coreset construction
algorithms---which build a small, weighted subset of the data that approximates
the full dataset---are no exception. We demonstrate that these algorithms scale
the coreset log-likelihood suboptimally, resulting in underestimated posterior
uncertainty. To address this shortcoming, we develop greedy iterative geodesic
ascent (GIGA), a novel algorithm for Bayesian coreset construction that scales
the coreset log-likelihood optimally. GIGA provides geometric decay in
posterior approximation error as a function of coreset size, and maintains the
fast running time of its predecessors. The paper concludes with validation of
GIGA on both synthetic and real datasets, demonstrating that it reduces
posterior approximation error by orders of magnitude compared with previous
coreset constructions.
A parallel implementation of the covariance matrix adaptation evolution strategy
Authors: Najeeb Khan
Date: 2018-05-28 00:00:00

In many practical optimization problems, the derivatives of the functions to
be optimized are unavailable or unreliable. Such optimization problems are
solved using derivative-free optimization techniques. One of the
state-of-the-art techniques for derivative-free optimization is the covariance
matrix adaptation evolution strategy (CMA-ES) algorithm. However, the
complexity of CMA-ES algorithm makes it undesirable for tasks where fast
optimization is needed. To reduce the execution time of CMA-ES, a parallel
implementation is proposed, and its performance is analyzed using the benchmark
problems in PythOPT optimization environment.
Value Propagation Networks
Authors: Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier
Date: 2018-05-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

We present Value Propagation (VProp), a set of parameter-efficient
differentiable planning modules built on Value Iteration which can successfully
be trained using reinforcement learning to solve unseen tasks, has the
capability to generalize to larger map sizes, and can learn to navigate in
dynamic environments. We show that the modules enable learning to plan when the
environment also includes stochastic elements, providing a cost-efficient
learning system to build low-level size-invariant planners for a variety of
interactive navigation problems. We evaluate on static and dynamic
configurations of MazeBase grid-worlds, with randomly generated environments of
several different sizes, and on a StarCraft navigation scenario, with more
complex dynamics, and pixels as input.
Lifelong Generative Modeling
Authors: Jason Ramapuram, Magda Gregorova, Alexandros Kalousis
Date: 2017-05-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Lifelong learning, Transfer Learning

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and preserve all the distributions seen so far, without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, inspired by a Bayesian update rule, the student model leverages the information learned by the teacher, which acts as a probabilistic knowledge store. The regularizer reduces the effect of catastrophic interference that appears when we learn over sequences of distributions. We validate our model's performance on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A and demonstrate that our model mitigates the effects of catastrophic interference faced by neural networks in sequential learning scenarios.
CapsNet comparative performance evaluation for image classification
Authors: Rinat Mukhometzianov, Juan Carrillo
Date: 2018-05-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Image classification has become one of the main tasks in the field of
computer vision technologies. In this context, a recent algorithm called
CapsNet that implements an approach based on activity vectors and dynamic
routing between capsules may overcome some of the limitations of the current
state of the art artificial neural networks (ANN) classifiers, such as
convolutional neural networks (CNN). In this paper, we evaluated the
performance of the CapsNet algorithm in comparison with three well-known
classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification
accuracy on four datasets with a different number of instances and classes,
including images of faces, traffic signs, and everyday objects. The evaluation
results show that even for simple architectures, training the CapsNet algorithm
requires significant computational resources and its classification performance
falls below the average accuracy values of the other three classifiers.
However, we argue that CapsNet seems to be a promising new technique for image
classification, and further experiments using more robust computation resources
and re-fined CapsNet architectures may produce better outcomes.
TADAM: Task dependent adaptive metric for improved few-shot learning
Authors: Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste
Date: 2018-05-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Few-Shot Image Classification, Few-Shot Learning

Few-shot learning has become essential for producing models that generalize
from few examples. In this work, we identify that metric scaling and metric
task conditioning are important to improve the performance of few-shot
algorithms. Our analysis reveals that simple metric scaling completely changes
the nature of few-shot algorithm parameter updates. Metric scaling provides
improvements up to 14% in accuracy for certain metrics on the mini-Imagenet
5-way 5-shot classification task. We further propose a simple and effective way
of conditioning a learner on the task sample set, resulting in learning a
task-dependent metric space. Moreover, we propose and empirically test a
practical end-to-end optimization procedure based on auxiliary task co-training
to learn a task-dependent metric space. The resulting few-shot learning model
based on the task-dependent scaled metric achieves state of the art on
mini-Imagenet. We confirm these results on another few-shot dataset that we
introduce in this paper based on CIFAR100. Our code is publicly available at
https://github.com/ElementAI/TADAM.
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks
Authors: Vishal Kaushal, Anurag Sahoo, Khoshrav Doctor, Narasimha Raju, Suyash Shetty, Pankaj Singh, Rishabh Iyer, Ganesh Ramakrishnan
Date: 2018-05-28 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Diversity, General Classification, image-classification, Image Classification, Object Recognition, Scene Recognition

Supervised machine learning based state-of-the-art computer vision techniques
are in general data hungry and pose the challenges of not having adequate
computing resources and of high costs involved in human labeling efforts.
Training data subset selection and active learning techniques have been
proposed as possible solutions to these challenges respectively. A special
class of subset selection functions naturally model notions of diversity,
coverage and representation and they can be used to eliminate redundancy and
thus lend themselves well for training data subset selection. They can also
help improve the efficiency of active learning in further reducing human
labeling efforts by selecting a subset of the examples obtained using the
conventional uncertainty sampling based techniques. In this work we empirically
demonstrate the effectiveness of two diversity models, namely the
Facility-Location and Disparity-Min models for training-data subset selection
and reducing labeling effort. We do this for a variety of computer vision tasks
including Gender Recognition, Scene Recognition and Object Recognition. Our
results show that subset selection done in the right way can add 2-3% in
accuracy on existing baselines, particularly in the case of less training data.
This allows the training of complex machine learning models (like Convolutional
Neural Networks) with much less training data while incurring minimal
performance loss.
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
Authors: Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi, Mamoru Komachi
Date: 2018-05-28 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Grammatical Error Correction, Machine Translation, Translation

Encoder-decoder models typically only employ words that are frequently used
in the training corpus to reduce the computational costs and exclude noise.
However, this vocabulary set may still include words that interfere with
learning in encoder-decoder models. This paper proposes a method for selecting
more suitable words for learning encoders by utilizing not only frequency, but
also co-occurrence information, which we capture using the HITS algorithm. We
apply our proposed method to two tasks: machine translation and grammatical
error correction. For Japanese-to-English translation, this method achieves a
BLEU score that is 0.56 points more than that of a baseline. It also
outperforms the baseline method for English grammatical error correction, with
an F0.5-measure that is 1.48 points higher.
Semi-Implicit Variational Inference
Authors: Mingzhang Yin, Mingyuan Zhou
Date: 2018-05-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Inference, Variational Inference

Semi-implicit variational inference (SIVI) is introduced to expand the
commonly used analytic variational distribution family, by mixing the
variational parameter with a flexible distribution. This mixing distribution
can assume any density function, explicit or not, as long as independent random
samples can be generated via reparameterization. Not only does SIVI expand the
variational family to incorporate highly flexible variational distributions,
including implicit ones that have no analytic density functions, but also
sandwiches the evidence lower bound (ELBO) between a lower bound and an upper
bound, and further derives an asymptotically exact surrogate ELBO that is
amenable to optimization via stochastic gradient ascent. With a substantially
expanded variational family and a novel optimization algorithm, SIVI is shown
to closely match the accuracy of MCMC in inferring the posterior in a variety
of Bayesian inference tasks.
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Authors: Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Albrecht Stenzinger, Laura Parlow, Jan Budczies, Benjamin Goeppert, Denise Treue, Manato Kotani, Masaru Ishii, Manfred Dietel, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen
Date: 2018-05-28 00:00:00
Tasks: BIG-bench Machine Learning

Recent advances in cancer research largely rely on new developments in
microscopic or molecular profiling techniques offering high level of detail
with respect to either spatial or molecular features, but usually not both.
Here, we present a novel machine learning-based computational approach that
allows for the identification of morphological tissue features and the
prediction of molecular properties from breast cancer imaging data. This
integration of microanatomic information of tumors with complex molecular
profiling data, including protein or gene expression, copy number variation,
gene methylation and somatic mutations, provides a novel means to
computationally score molecular markers with respect to their relevance to
cancer and their spatial associations within the tumor microenvironment.
A CNN for homogneous Riemannian manifolds with applications to Neuroimaging
Authors: Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
Date: 2018-05-14 00:00:00

Convolutional neural networks are ubiquitous in Machine Learning applications
for solving a variety of problems. They however can not be used in their native
form when the domain of the data is commonly encountered manifolds such as the
sphere, the special orthogonal group, the Grassmanian, the manifold of
symmetric positive definite matrices and others. Most recently, generalization
of CNNs to data domains such as the 2-sphere has been reported by some research
groups, which is referred to as the spherical CNNs (SCNNs). The key property of
SCNNs distinct from CNNs is that they exhibit the rotational equivariance
property that allows for sharing learned weights within a layer. In this paper,
we theoretically generalize the CNNs to Riemannian homogeneous manifolds, that
include but are not limited to the aforementioned example manifolds. Our key
contributions in this work are: (i) A theorem stating that linear group
equivariance systems are fully characterized by correlation of functions on the
domain manifold and vice-versa. This is fundamental to the characterization of
all linear group equivariant systems and parallels the widely used result in
linear system theory for vector spaces. (ii) As a corrolary, we prove the
equivariance of the correlation operation to group actions admitted by the
input domains which are Riemannian homogeneous manifolds. (iii) We present the
first end-to-end deep network architecture for classification of diffusion
magnetic resonance image (dMRI) scans acquired from a cohort of 44 Parkinson
Disease patients and 50 control/normal subjects. (iv) A proof of concept
experiment involving synthetic data generated on the manifold of symmetric
positive definite matrices is presented to demonstrate the applicability of our
network to other types of domains.
Strongly polynomial efficient approximation scheme for segmentation
Authors: Nikolaj Tatti
Date: 2018-05-28 00:00:00
Tasks: 2k, Segmentation

Partitioning a sequence of length $n$ into $k$ coherent segments (Seg) is one
of the classic optimization problems. As long as the optimization criterion is
additive, Seg can be solved exactly in $O(n^2k)$ time using a classic dynamic
program. Due to the quadratic term, computing the exact segmentation may be too
expensive for long sequences, which has led to development of approximate
solutions. We consider an existing estimation scheme that computes $(1 +
\epsilon)$ approximation in polylogarithmic time. We augment this algorithm,
making it strongly polynomial. We do this by first solving a slightly different
segmentation problem (MaxSeg), where the quality of the segmentation is the
maximum penalty of an individual segment. By using this solution to initialize
the estimation scheme, we are able to obtain a strongly polynomial algorithm.
In addition, we consider a cumulative version of Seg, where we are asked to
discover the optimal segmentation for each prefix of the input sequence. We
propose a strongly polynomial algorithm that yields $(1 + \epsilon)$
approximation in $O(nk^2 / \epsilon)$ time. Finally, we consider a cumulative
version of MaxSeg, and show that we can solve the problem in $O(nk \log k)$
time.
Resilient Non-Submodular Maximization over Matroid Constraints
Authors: Vasileios Tzoumas, Ali Jadbabaie, George J. Pappas
Date: 2018-04-02 00:00:00
Tasks: Robot Navigation, Scheduling

The control and sensing of large-scale systems results in combinatorial
problems not only for sensor and actuator placement but also for scheduling or
observability/controllability. Such combinatorial constraints in system design
and implementation can be captured using a structure known as matroids. In
particular, the algebraic structure of matroids can be exploited to develop
scalable algorithms for sensor and actuator selection, along with quantifiable
approximation bounds. However, in large-scale systems, sensors and actuators
may fail or may be (cyber-)attacked. The objective of this paper is to focus on
resilient matroid-constrained problems arising in control and sensing but in
the presence of sensor and actuator failures. In general, resilient
matroid-constrained problems are computationally hard. Contrary to the
non-resilient case (with no failures), even though they often involve objective
functions that are monotone or submodular, no scalable approximation algorithms
are known for their solution. In this paper, we provide the first algorithm,
that also has the following properties: First, it achieves system-wide
resiliency, i.e., the algorithm is valid for any number of denial-of-service
attacks or failures. Second, it is scalable, as our algorithm terminates with
the same running time as state-of-the-art algorithms for (non-resilient)
matroid-constrained optimization. Third, it provides provable approximation
bounds on the system performance, since for monotone objective functions our
algorithm guarantees a solution close to the optimal. We quantify our
algorithm's approximation performance using a notion of curvature for monotone
(not necessarily submodular) set functions. Finally, we support our theoretical
analyses with numerical experiments, by considering a control-aware sensor
selection scenario, namely, sensing-constrained robot navigation.
A visual approach for age and gender identification on Twitter
Authors: Miguel A. Alvarez-Carmona, Luis Pellegrin, Manuel Montes-y-Gómez, Fernando Sánchez-Vega, Hugo Jair Escalante, A. Pastor López-Monroy, Luis Villaseñor-Pineda, Esaú Villatoro-Tello
Date: 2018-05-28 00:00:00
Tasks: Author Profiling, Marketing

The goal of Author Profiling (AP) is to identify demographic aspects (e.g.,
age, gender) from a given set of authors by analyzing their written texts.
Recently, the AP task has gained interest in many problems related to computer
forensics, psychology, marketing, but specially in those related with social
media exploitation. As known, social media data is shared through a wide range
of modalities (e.g., text, images and audio), representing valuable information
to be exploited for extracting valuable insights from users. Nevertheless, most
of the current work in AP using social media data has been devoted to analyze
textual information only, and there are very few works that have started
exploring the gender identification using visual information. Contrastingly,
this paper focuses in exploiting the visual modality to perform both age and
gender identification in social media, specifically in Twitter. Our goal is to
evaluate the pertinence of using visual information in solving the AP task.
Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating
posted images from all the users, making a distinction between tweeted and
retweeted images. Performed experiments provide interesting evidence on the
usefulness of visual information in comparison with traditional textual
representations for the AP task.
Confidence Prediction for Lexicon-Free OCR
Authors: Noam Mor, Lior Wolf
Date: 2018-05-28 00:00:00
Tasks: General Classification, Multi-class Classification, Optical Character Recognition (OCR), Prediction

Having a reliable accuracy score is crucial for real world applications of
OCR, since such systems are judged by the number of false readings.
Lexicon-based OCR systems, which deal with what is essentially a multi-class
classification problem, often employ methods explicitly taking into account the
lexicon, in order to improve accuracy. However, in lexicon-free scenarios,
filtering errors requires an explicit confidence calculation. In this work we
show two explicit confidence measurement techniques, and show that they are
able to achieve a significant reduction in misreads on both standard benchmarks
and a proprietary dataset.
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
Authors: Daan Wynen, Cordelia Schmid, Julien Mairal
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12

In this paper, we introduce an unsupervised learning approach to
automatically discover, summarize, and manipulate artistic styles from large
collections of paintings. Our method is based on archetypal analysis, which is
an unsupervised learning technique akin to sparse coding with a geometric
interpretation. When applied to deep image representations from a collection of
artworks, it learns a dictionary of archetypal styles, which can be easily
visualized. After training the model, the style of a new image, which is
characterized by local statistics of deep visual features, is approximated by a
sparse convex combination of archetypes. This enables us to interpret which
archetypal styles are present in the input image, and in which proportion.
Finally, our approach allows us to manipulate the coefficients of the latent
archetypal decomposition, and achieve various special effects such as style
enhancement, transfer, and interpolation between multiple archetypes.
Model-Robust Counterfactual Prediction Method
Authors: Dave Zachariah, Petre Stoica
Date: 2017-05-19 00:00:00
Tasks: Conformal Prediction, counterfactual, model, Prediction, Prediction Intervals

We develop a novel method for counterfactual analysis based on observational
data using prediction intervals for units under different exposures. Unlike
methods that target heterogeneous or conditional average treatment effects of
an exposure, the proposed approach aims to take into account the irreducible
dispersions of counterfactual outcomes so as to quantify the relative impact of
different exposures. The prediction intervals are constructed in a
distribution-free and model-robust manner based on the conformal prediction
approach. The computational obstacles to this approach are circumvented by
leveraging properties of a tuning-free method that learns sparse additive
predictor models for counterfactual outcomes. The method is illustrated using
both real and synthetic data.