pwc-archive/papers-with-abstracts huggingface dataset:
Expert-Driven Genetic Algorithms for Simulating Evaluation Functions
Authors: Eli David, Moshe Koppel, Nathan S. Netanyahu
Date: 2017-11-18 00:00:00

In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate expert (or mentor), we can evolve a program that
is on par with top tournament-playing chess programs, outperforming a two-time
World Computer Chess Champion. This performance gain is achieved by evolving a
program that mimics the behavior of a superior expert. The resulting evaluation
function of the evolved program consists of a much smaller number of parameters
than the expert's. The extended experimental results provided in this paper
include a report of our successful participation in the 2008 World Computer
Chess Championship. In principle, our expert-driven approach could be used in a
wide range of problems for which appropriate experts are available.
Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions
Authors: Eli David, H. Jaap van den Herik, Moshe Koppel, Nathan S. Netanyahu
Date: 2017-11-18 00:00:00

This paper demonstrates the use of genetic algorithms for evolving a
grandmaster-level evaluation function for a chess program. This is achieved by
combining supervised and unsupervised learning. In the supervised learning
phase the organisms are evolved to mimic the behavior of human grandmasters,
and in the unsupervised learning phase these evolved organisms are further
improved upon by means of coevolution.
  While past attempts succeeded in creating a grandmaster-level program by
mimicking the behavior of existing computer chess programs, this paper presents
the first successful attempt at evolving a state-of-the-art evaluation function
by learning only from databases of games played by humans. Our results
demonstrate that the evolved program outperforms a two-time World Computer
Chess Champion.
Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
Authors: Eli David, Moshe Koppel, Nathan S. Netanyahu
Date: 2017-11-18 00:00:00

In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate mentor, we can evolve a program that is on par
with top tournament-playing chess programs, outperforming a two-time World
Computer Chess Champion. This performance gain is achieved by evolving a
program with a smaller number of parameters in its evaluation function to mimic
the behavior of a superior mentor which uses a more extensive evaluation
function. In principle, our mentor-assisted approach could be used in a wide
range of problems for which appropriate mentors are available.
Iteratively reweighted $\ell_1$ algorithms with extrapolation
Authors: Peiran Yu, Ting Kei Pong
Date: 2017-10-22 00:00:00
Tasks: CPU

Iteratively reweighted $\ell_1$ algorithm is a popular algorithm for solving
a large class of optimization problems whose objective is the sum of a
Lipschitz differentiable loss function and a possibly nonconvex sparsity
inducing regularizer. In this paper, motivated by the success of extrapolation
techniques in accelerating first-order methods, we study how widely used
extrapolation techniques such as those in [4,5,22,28] can be incorporated to
possibly accelerate the iteratively reweighted $\ell_1$ algorithm. We consider
three versions of such algorithms. For each version, we exhibit an explicitly
checkable condition on the extrapolation parameters so that the sequence
generated provably clusters at a stationary point of the optimization problem.
We also investigate global convergence under additional Kurdyka-$\L$ojasiewicz
assumptions on certain potential functions. Our numerical experiments show that
our algorithms usually outperform the general iterative shrinkage and
thresholding algorithm in [21] and an adaptation of the iteratively reweighted
$\ell_1$ algorithm in [23, Algorithm 7] with nonmonotone line-search for
solving random instances of log penalty regularized least squares problems in
terms of both CPU time and solution quality.
Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference
Authors: Moontae Lee, David Mimno
Date: 2017-11-18 00:00:00
Proceeding: EMNLP 2014 10

The anchor words algorithm performs provably efficient topic model inference
by finding an approximate convex hull in a high-dimensional word co-occurrence
space. However, the existing greedy algorithm often selects poor anchor words,
reducing topic quality and interpretability. Rather than finding an approximate
convex hull in a high-dimensional space, we propose to find an exact convex
hull in a visualizable 2- or 3-dimensional space. Such low-dimensional
embeddings both improve topics and clearly show users why the algorithm selects
certain words.
A framework for Multi-A(rmed)/B(andit) testing with online FDR control
Authors: Fanny Yang, Aaditya Ramdas, Kevin Jamieson, Martin J. Wainwright
Date: 2017-06-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: valid

We propose an alternative framework to existing setups for controlling false
alarms when multiple A/B tests are run over time. This setup arises in many
practical applications, e.g. when pharmaceutical companies test new treatment
options against control pills for different diseases, or when internet
companies test their default webpages versus various alternatives over time.
Our framework proposes to replace a sequence of A/B tests by a sequence of
best-arm MAB instances, which can be continuously monitored by the data
scientist. When interleaving the MAB tests with an an online false discovery
rate (FDR) algorithm, we can obtain the best of both worlds: low sample
complexity and any time online FDR control. Our main contributions are: (i) to
propose reasonable definitions of a null hypothesis for MAB instances; (ii) to
demonstrate how one can derive an always-valid sequential p-value that allows
continuous monitoring of each MAB test; and (iii) to show that using rejection
thresholds of online-FDR algorithms as the confidence levels for the MAB
algorithms results in both sample-optimality, high power and low FDR at any
point in time. We run extensive simulations to verify our claims, and also
report results on real data collected from the New Yorker Cartoon Caption
contest.
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
Authors: Lijun Zhao, Jie Liang, Huihui Bai, Lili Meng, Anhong Wang, Yao Zhao
Date: 2017-07-09 00:00:00
Tasks: Denoising, Image Denoising, image smoothing

In this paper, two local activity-tuned filtering frameworks are proposed for
noise removal and image smoothing, where the local activity measurement is
given by the clipped and normalized local variance or standard deviation. The
first framework is a modified anisotropic diffusion for noise removal of
piece-wise smooth image. The second framework is a local activity-tuned
Relative Total Variation (LAT-RTV) method for image smoothing. Both frameworks
employ the division of gradient and the local activity measurement to achieve
noise removal. In addition, to better capture local information, the proposed
LAT-RTV uses the product of gradient and local activity measurement to boost
the performance of image smoothing. Experimental results are presented to
demonstrate the efficiency of the proposed methods on various applications,
including depth image filtering, clip-art compression artifact removal, image
smoothing, and image denoising.
Household poverty classification in data-scarce environments: a machine learning approach
Authors: Varun Kshirsagar, Jerzy Wieczorek, Sharada Ramanathan, Rachel Wells
Date: 2017-11-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Survey

We describe a method to identify poor households in data-scarce countries by
leveraging information contained in nationally representative household
surveys. It employs standard statistical learning techniques---cross-validation
and parameter regularization---which together reduce the extent to which the
model is over-fitted to match the idiosyncracies of observed survey data. The
automated framework satisfies three important constraints of this development
setting: i) The prediction model uses at most ten questions, which limits the
costs of data collection; ii) No computation beyond simple arithmetic is needed
to calculate the probability that a given household is poor, immediately after
data on the ten indicators is collected; and iii) One specification of the
model (i.e. one scorecard) is used to predict poverty throughout a country that
may be characterized by significant sub-national differences. Using survey data
from Zambia, the model's out-of-sample predictions distinguish poor households
from non-poor households using information contained in ten questions.
Convergence analysis of belief propagation for pairwise linear Gaussian models
Authors: Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura
Date: 2017-06-12 00:00:00

Gaussian belief propagation (BP) has been widely used for distributed
inference in large-scale networks such as the smart grid, sensor networks, and
social networks, where local measurements/observations are scattered over a
wide geographical area. One particular case is when two neighboring agents
share a common observation. For example, to estimate voltage in the direct
current (DC) power flow model, the current measurement over a power line is
proportional to the voltage difference between two neighboring buses. When
applying the Gaussian BP algorithm to this type of problem, the convergence
condition remains an open issue. In this paper, we analyze the convergence
properties of Gaussian BP for this pairwise linear Gaussian model. We show
analytically that the updating information matrix converges at a geometric rate
to a unique positive definite matrix with arbitrary positive semidefinite
initial value and further provide the necessary and sufficient convergence
condition for the belief mean vector to the optimal estimate.
Unsupervised learning of object frames by dense equivariant image labelling
Authors: James Thewlis, Hakan Bilen, Andrea Vedaldi
Date: 2017-06-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Object, Optical Flow Estimation, Unsupervised Facial Landmark Detection

One of the key challenges of visual perception is to extract abstract models
of 3D objects and object categories from visual measurements, which are
affected by complex nuisance factors such as viewpoint, occlusion, motion, and
deformations. Starting from the recent idea of viewpoint factorization, we
propose a new approach that, given a large number of images of an object and no
other supervision, can extract a dense object-centric coordinate frame. This
coordinate frame is invariant to deformations of the images and comes with a
dense equivariant labelling neural network that can map image pixels to their
corresponding object coordinates. We demonstrate the applicability of this
method to simple articulated objects and deformable objects such as human
faces, learning embeddings from random synthetic transformations or optical
flow correspondences, all without any manual supervision.
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
Authors: Mahmoud Afifi, Abdelrahman Abdelhamed
Date: 2017-06-13 00:00:00
Tasks: Gender Classification, General Classification

Gender classification aims at recognizing a person's gender. Despite the high
accuracy achieved by state-of-the-art methods for this task, there is still
room for improvement in generalized and unrestricted datasets. In this paper,
we advocate a new strategy inspired by the behavior of humans in gender
recognition. Instead of dealing with the face image as a sole feature, we rely
on the combination of isolated facial features and a holistic feature which we
call the foggy face. Then, we use these features to train deep convolutional
neural networks followed by an AdaBoost-based score fusion to infer the final
gender class. We evaluate our method on four challenging datasets to
demonstrate its efficacy in achieving better or on-par accuracy with
state-of-the-art methods. In addition, we present a new face dataset that
intensifies the challenges of occluded faces and illumination changes, which we
believe to be a much-needed resource for gender classification research.
Prediction Scores as a Window into Classifier Behavior
Authors: Medha Katehara, Emma Beauxis-Aussalet, Bilal Alsallakh
Date: 2017-11-18 00:00:00
Tasks: General Classification, Prediction

Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class analysis of these scores. Our system, called Classilist,
enables relating these scores to the classification correctness and to the
underlying samples and their features. We illustrate how such analysis reveals
varying behavior of different classifiers. Classilist is available for use
online, along with source code, video tutorials, and plugins for R, RapidMiner,
and KNIME at https://katehara.github.io/classilist-site/.
Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees
Authors: José Marcio Luna, Eric Eaton, Lyle H. Ungar, Eric Diffenderfer, Shane T. Jensen, Efstathios D. Gennatas, Mateo Wirth, Charles B. Simone II, Timothy D. Solberg, Gilmer Valdes
Date: 2017-11-18 00:00:00
Tasks: Additive models, General Classification

Additive models, such as produced by gradient boosting, and full interaction
models, such as classification and regression trees (CART), are widely used
algorithms that have been investigated largely in isolation. We show that these
models exist along a spectrum, revealing never-before-known connections between
these two approaches. This paper introduces a novel technique called
tree-structured boosting for creating a single decision tree, and shows that
this method can produce models equivalent to CART or gradient boosted stumps at
the extremes by varying a single parameter. Although tree-structured boosting
is designed primarily to provide both the model interpretability and predictive
performance needed for high-stake applications like medicine, it also can
produce decision trees represented by hybrid models between CART and boosted
stumps that can outperform either of these approaches.
MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks
Authors: Minmin Chen
Date: 2017-11-18 00:00:00

We introduce MinimalRNN, a new recurrent neural network architecture that
achieves comparable performance as the popular gated RNNs with a simplified
structure. It employs minimal updates within RNN, which not only leads to
efficient learning and testing but more importantly better interpretability and
trainability. We demonstrate that by endorsing the more restrictive update
rule, MinimalRNN learns disentangled RNN states. We further examine the
learning dynamics of different RNN structures using input-output Jacobians, and
show that MinimalRNN is able to capture longer range dependencies than existing
RNN architectures.
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
Authors: Risheng Liu, Xin Fan, Minjun Hou, Zhiying Jiang, Zhongxuan Luo, Lei Zhang
Date: 2017-11-18 00:00:00
Tasks: Image Dehazing, Image Enhancement, Rain Removal, Single Image Dehazing, valid

Single image dehazing is an important low-level vision task with many
applications. Early researches have investigated different kinds of visual
priors to address this problem. However, they may fail when their assumptions
are not valid on specific images. Recent deep networks also achieve relatively
good performance in this task. But unfortunately, due to the disappreciation of
rich physical rules in hazes, large amounts of data are required for their
training. More importantly, they may still fail when there exist completely
different haze distributions in testing images. By considering the
collaborations of these two perspectives, this paper designs a novel residual
architecture to aggregate both prior (i.e., domain knowledge) and data (i.e.,
haze distribution) information to propagate transmissions for scene radiance
estimation. We further present a variational energy based perspective to
investigate the intrinsic propagation behavior of our aggregated deep model. In
this way, we actually bridge the gap between prior driven models and data
driven networks and leverage advantages but avoid limitations of previous
dehazing approaches. A lightweight learning framework is proposed to train our
propagation network. Finally, by introducing a taskaware image separation
formulation with a flexible optimization scheme, we extend the proposed model
for more challenging vision tasks, such as underwater image enhancement and
single image rain removal. Experiments on both synthetic and realworld images
demonstrate the effectiveness and efficiency of the proposed framework.
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Authors: Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
Date: 2017-11-18 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms can learn complex behavioral skills,
but real-world application of these methods requires a large amount of
experience to be collected by the agent. In practical settings, such as
robotics, this involves repeatedly attempting a task, resetting the environment
between each attempt. However, not all tasks are easily or automatically
reversible. In practice, this learning process requires extensive human
intervention. In this work, we propose an autonomous method for safe and
efficient reinforcement learning that simultaneously learns a forward and reset
policy, with the reset policy resetting the environment for a subsequent
attempt. By learning a value function for the reset policy, we can
automatically determine when the forward policy is about to enter a
non-reversible state, providing for uncertainty-aware safety aborts. Our
experiments illustrate that proper use of the reset policy can greatly reduce
the number of manual resets required to learn a task, can reduce the number of
unsafe actions that lead to non-reversible states, and can automatically induce
a curriculum.
Pricing Football Players using Neural Networks
Authors: Sourya Dey
Date: 2017-11-16 00:00:00
Tasks: Game of Football, L2 Regularization

We designed a multilayer perceptron neural network to predict the price of a
football (soccer) player using data on more than 15,000 players from the
football simulation video game FIFA 2017. The network was optimized by
experimenting with different activation functions, number of neurons and
layers, learning rate and its decay, Nesterov momentum based stochastic
gradient descent, L2 regularization, and early stopping. Simultaneous
exploration of various aspects of neural network training is performed and
their trade-offs are investigated. Our final model achieves a top-5 accuracy of
87.2% among 119 pricing categories, and places any footballer within 6.32% of
his actual price on average.
Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network
Authors: Abderrahim Khalifa, Younes Idsouguou, Loubna Benabbou, Mourad Zirari
Date: 2017-11-18 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we aim to illustrate different approaches we followed while
developing a forecasting tool for highway traffic in Morocco. Two main
approaches were adopted: Statistical Analysis as a step of data exploration and
data wrangling. Therefore, a beta model is carried out for a better
understanding of traffic behavior. Next, we moved to Machine Learning where we
worked with a bunch of algorithms such as Random Forest, Artificial Neural
Networks, Extra Trees, etc. yet, we were convinced that this field of study is
still considered under state of the art models, so, we were also covering an
application of Long Short-Term Memory Neural Networks.
Characterizing Driving Context from Driver Behavior
Authors: Sobhan Moosavi, Behrooz Omidvar-Tehrani, R. Bruce Craig, Arnab Nandi, Rajiv Ramnath
Date: 2017-10-13 00:00:00

Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the correlation between driving behavior and traffic conditions. This
contextual information enables analysts to validate observation-based
hypotheses about the driving of an individual. In this paper, we present
DriveContext, a novel framework to find the characteristics of a context, by
extracting significant driving patterns (e.g., a slow-down), and then
identifying the set of potential causes behind patterns (e.g., traffic
congestion). Our experimental results confirm the feasibility of the framework
in identifying meaningful driving patterns, with improvements in comparison
with the state-of-the-art. We also demonstrate how the framework derives
interesting characteristics for different contexts, through real-world
examples.
Large-Margin Softmax Loss for Convolutional Neural Networks
Authors: Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang
Date: 2016-12-07 00:00:00
Tasks: General Classification

Cross-entropy loss together with softmax is arguably one of the most common
used supervision components in convolutional neural networks (CNNs). Despite
its simplicity, popularity and excellent performance, the component does not
explicitly encourage discriminative learning of features. In this paper, we
propose a generalized large-margin softmax (L-Softmax) loss which explicitly
encourages intra-class compactness and inter-class separability between learned
features. Moreover, L-Softmax not only can adjust the desired margin but also
can avoid overfitting. We also show that the L-Softmax loss can be optimized by
typical stochastic gradient descent. Extensive experiments on four benchmark
datasets demonstrate that the deeply-learned features with L-softmax loss
become more discriminative, hence significantly boosting the performance on a
variety of visual classification and verification tasks.
Approximate Gradient Coding via Sparse Random Graphs
Authors: Zachary Charles, Dimitris Papailiopoulos, Jordan Ellenberg
Date: 2017-11-17 00:00:00

Distributed algorithms are often beset by the straggler effect, where the
slowest compute nodes in the system dictate the overall running time.
Coding-theoretic techniques have been recently proposed to mitigate stragglers
via algorithmic redundancy. Prior work in coded computation and gradient coding
has mainly focused on exact recovery of the desired output. However, slightly
inexact solutions can be acceptable in applications that are robust to noise,
such as model training via gradient-based algorithms. In this work, we present
computationally simple gradient codes based on sparse graphs that guarantee
fast and approximately accurate distributed computation. We demonstrate that
sacrificing a small amount of accuracy can significantly increase algorithmic
robustness to stragglers.