pwc-archive/papers-with-abstracts huggingface dataset:
A Block Regression Model for Short-Term Mobile Traffic Forecasting
Authors: Huimin Pan, Jingchu Liu, Sheng Zhou, Zhisheng Niu
Date: 2015-11-17 00:00:00
Tasks: regression

Accurate mobile traffic forecast is important for efficient network planning
and operations. However, existing traffic forecasting models have high
complexity, making the forecasting process slow and costly. In this paper, we
analyze some characteristics of mobile traffic such as periodicity, spatial
similarity and short term relativity. Based on these characteristics, we
propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting.
This model employs seasonal differentiation so as to take into account of the
temporally repetitive nature of mobile traffic. One of the key features of our
{BR} model lies in its low complexity since it constructs a single model for
all base stations. We evaluate the accuracy of {BR} model based on real traffic
data and compare it with the existing models. Results show that our {BR} model
offers equal accuracy to the existing models but has much less complexity.
Complete Dictionary Recovery over the Sphere
Authors: Ju Sun, Qing Qu, John Wright
Date: 2015-04-26 00:00:00
Tasks: Dictionary Learning

We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb R^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to the theoretical
understanding of dictionary learning, which seeks a sparse representation for a
collection of input signals, and finds numerous applications in modern signal
processing and machine learning. We give the first efficient algorithm that
provably recovers $\mathbf A_0$ when $\mathbf X_0$ has $O(n)$ nonzeros per
column, under suitable probability model for $\mathbf X_0$. In contrast, prior
results based on efficient algorithms provide recovery guarantees when $\mathbf
X_0$ has only $O(n^{1-\delta})$ nonzeros per column for any constant $\delta
\in (0, 1)$.
  Our algorithmic pipeline centers around solving a certain nonconvex
optimization problem with a spherical constraint, and hence is naturally
phrased in the language of manifold optimization. To show this apparently hard
problem is tractable, we first provide a geometric characterization of the
high-dimensional objective landscape, which shows that with high probability
there are no "spurious" local minima. This particular geometric structure
allows us to design a Riemannian trust region algorithm over the sphere that
provably converges to one local minimizer with an arbitrary initialization,
despite the presence of saddle points. The geometric approach we develop here
may also shed light on other problems arising from nonconvex recovery of
structured signals.
Ethical Artificial Intelligence
Authors: Bill Hibbard
Date: 2014-11-05 00:00:00

This book-length article combines several peer reviewed papers and new
material to analyze the issues of ethical artificial intelligence (AI). The
behavior of future AI systems can be described by mathematical equations, which
are adapted to analyze possible unintended AI behaviors and ways that AI
designs can avoid them. This article makes the case for utility-maximizing
agents and for avoiding infinite sets in agent definitions. It shows how to
avoid agent self-delusion using model-based utility functions and how to avoid
agents that corrupt their reward generators (sometimes called "perverse
instantiation") using utility functions that evaluate outcomes at one point in
time from the perspective of humans at a different point in time. It argues
that agents can avoid unintended instrumental actions (sometimes called "basic
AI drives" or "instrumental goals") by accurately learning human values. This
article defines a self-modeling agent framework and shows how it can avoid
problems of resource limits, being predicted by other agents, and inconsistency
between the agent's utility function and its definition (one version of this
problem is sometimes called "motivated value selection"). This article also
discusses how future AI will differ from current AI, the politics of AI, and
the ultimate use of AI to help understand the nature of the universe and our
place in it.
Combinatorial Cascading Bandits
Authors: Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
Date: 2015-07-15 00:00:00
Proceeding: NeurIPS 2015 12

We propose combinatorial cascading bandits, a class of partial monitoring
problems where at each step a learning agent chooses a tuple of ground items
subject to constraints and receives a reward if and only if the weights of all
chosen items are one. The weights of the items are binary, stochastic, and
drawn independently of each other. The agent observes the index of the first
chosen item whose weight is zero. This observation model arises in network
routing, for instance, where the learning agent may only observe the first link
in the routing path which is down, and blocks the path. We propose a UCB-like
algorithm for solving our problems, CombCascade; and prove gap-dependent and
gap-free upper bounds on its $n$-step regret. Our proofs build on recent work
in stochastic combinatorial semi-bandits but also address two novel challenges
of our setting, a non-linear reward function and partial observability. We
evaluate CombCascade on two real-world problems and show that it performs well
even when our modeling assumptions are violated. We also demonstrate that our
setting requires a new learning algorithm.
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
Authors: Peter Li, Jiyuan Qian, Tian Wang
Date: 2015-11-17 00:00:00
Tasks: Feature Engineering, Information Retrieval, Instrument Recognition, Music Information Retrieval, Retrieval

Traditional methods to tackle many music information retrieval tasks
typically follow a two-step architecture: feature engineering followed by a
simple learning algorithm. In these "shallow" architectures, feature
engineering and learning are typically disjoint and unrelated. Additionally,
feature engineering is difficult, and typically depends on extensive domain
expertise.
  In this paper, we present an application of convolutional neural networks for
the task of automatic musical instrument identification. In this model, feature
extraction and learning algorithms are trained together in an end-to-end
fashion. We show that a convolutional neural network trained on raw audio can
achieve performance surpassing traditional methods that rely on hand-crafted
features.
Neurocontrol methods review
Authors: Artem Chernodub, Dmitry Dziuba
Date: 2015-11-17 00:00:00

Methods of applying neural networks to control plants are considered. Methods
and schemes are described, their advantages and disadvantages are discussed.
Active exploration of sensor networks from a robotics perspective
Authors: Christian Blum, Verena V. Hafner
Date: 2015-11-17 00:00:00
Tasks: Position

Traditional algorithms for robots who need to integrate into a wireless
network often focus on one specific task. In this work we want to develop
simple, adaptive and reusable algorithms for real world applications for this
scenario. Starting with the most basic task for mobile wireless network nodes,
finding the position of another node, we introduce an algorithm able to solve
this task. We then show how this algorithm can readily be employed to solve a
large number of other related tasks like finding the optimal position to bridge
two static network nodes. For this we first introduce a meta-algorithm inspired
by autonomous robot learning strategies and the concept of internal models
which yields a class of source seeking algorithms for mobile nodes. The
effectiveness of this algorithm is demonstrated in real world experiments using
a physical mobile robot and standard 802.11 wireless LAN in an office
environment. We also discuss the differences to conventional algorithms and
give the robotics perspective on this class of algorithms. Then we proceed to
show how more complex tasks, which might be encountered by mobile nodes, can be
encoded in the same framework and how the introduced algorithm can solve them.
These tasks can be direct (cross layer) optimization tasks or can also encode
more complex tasks like bridging two network nodes. We choose the bridging
scenario as an example, implemented on a real physical robot, and show how the
robot can solve it in a real world experiment.
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Authors: Johan Dahlin, Fredrik Lindsten, Joel Kronander, Thomas B. Schön
Date: 2015-11-17 00:00:00
Tasks: Bayesian Inference

Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian
inference in models where the posterior distribution is analytical intractable
or computationally costly to evaluate directly. It operates by introducing
additional auxiliary variables into the model and form an extended target
distribution, which then can be evaluated point-wise. In many cases, the
standard Metropolis-Hastings is then applied to sample from the extended target
and the sought posterior can be obtained by marginalisation. However, in some
implementations this approach suffers from poor mixing as the auxiliary
variables are sampled from an independent proposal. We propose a modification
to the pmMH algorithm in which a Crank-Nicolson (CN) proposal is used instead.
This results in that we introduce a positive correlation in the auxiliary
variables. We investigate how to tune the CN proposal and its impact on the
mixing of the resulting pmMH sampler. The conclusion is that the proposed
modification can have a beneficial effect on both the mixing of the Markov
chain and the computational cost for each iteration of the pmMH algorithm.
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Authors: Alexander Lavin, Subutai Ahmad
Date: 2015-10-12 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Much of the world's data is streaming, time-series data, where anomalies give
significant information in critical situations; examples abound in domains such
as finance, IT, security, medical, and energy. Yet detecting anomalies in
streaming data is a difficult task, requiring detectors to process data in
real-time, not batches, and learn while simultaneously making predictions.
There are no benchmarks to adequately test and score the efficacy of real-time
anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which
attempts to provide a controlled and repeatable environment of open-source
tools to test and measure anomaly detection algorithms on streaming data. The
perfect detector would detect all anomalies as soon as possible, trigger no
false alarms, work with real-world time-series data across a variety of
domains, and automatically adapt to changing statistics. Rewarding these
characteristics is formalized in NAB, using a scoring algorithm designed for
streaming data. NAB evaluates detectors on a benchmark dataset with labeled,
real-world time-series data. We present these components, and give results and
analyses for several open source, commercially-used algorithms. The goal for
NAB is to provide a standard, open source framework with which the research
community can compare and evaluate different algorithms for detecting anomalies
in streaming data.
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
Authors: Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
Date: 2015-11-17 00:00:00
Proceeding: NeurIPS 2015 12

Efficient and robust algorithms for decentralized estimation in networks are
essential to many distributed systems. Whereas distributed estimation of sample
mean statistics has been the subject of a good deal of attention, computation
of $U$-statistics, relying on more expensive averaging over pairs of
observations, is a less investigated area. Yet, such data functionals are
essential to describe global properties of a statistical population, with
important examples including Area Under the Curve, empirical variance, Gini
mean difference and within-cluster point scatter. This paper proposes new
synchronous and asynchronous randomized gossip algorithms which simultaneously
propagate data across the network and maintain local estimates of the
$U$-statistic of interest. We establish convergence rate bounds of $O(1/t)$ and
$O(\log t / t)$ for the synchronous and asynchronous cases respectively, where
$t$ is the number of iterations, with explicit data and network dependent
terms. Beyond favorable comparisons in terms of rate analysis, numerical
experiments provide empirical evidence the proposed algorithms surpasses the
previously introduced approach.
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
Authors: Doniyor Ulmasov, Caroline Baroukh, Benoit Chachuat, Marc Peter Deisenroth, Ruth Misener
Date: 2015-11-17 00:00:00
Tasks: Bayesian Optimization, Scheduling

Bayesian Optimization (BO) is a data-efficient method for global black-box
optimization of an expensive-to-evaluate fitness function. BO typically assumes
that computation cost of BO is cheap, but experiments are time consuming or
costly. In practice, this allows us to optimize ten or fewer critical
parameters in up to 1,000 experiments. But experiments may be less expensive
than BO methods assume: In some simulation models, we may be able to conduct
multiple thousands of experiments in a few hours, and the computational burden
of BO is no longer negligible compared to experimentation time. To address this
challenge we introduce a new Dimension Scheduling Algorithm (DSA), which
reduces the computational burden of BO for many experiments. The key idea is
that DSA optimizes the fitness function only along a small set of dimensions at
each iteration. This DSA strategy (1) reduces the necessary computation time,
(2) finds good solutions faster than the traditional BO method, and (3) can be
parallelized straightforwardly. We evaluate the DSA in the context of
optimizing parameters of dynamic models of microalgae metabolism and show
faster convergence than traditional BO.
Constant Time EXPected Similarity Estimation using Stochastic Optimization
Authors: Markus Schneider, Wolfgang Ertel, Günther Palm
Date: 2015-11-17 00:00:00
Tasks: Anomaly Detection, Stochastic Optimization

A new algorithm named EXPected Similarity Estimation (EXPoSE) was recently
proposed to solve the problem of large-scale anomaly detection. It is a
non-parametric and distribution free kernel method based on the Hilbert space
embedding of probability measures. Given a dataset of $n$ samples, EXPoSE needs
only $\mathcal{O}(n)$ (linear time) to build a model and $\mathcal{O}(1)$
(constant time) to make a prediction. In this work we improve the linear
computational complexity and show that an $\epsilon$-accurate model can be
estimated in constant time, which has significant implications for large-scale
learning problems. To achieve this goal, we cast the original EXPoSE
formulation into a stochastic optimization problem. It is crucial that this
approach allows us to determine the number of iteration based on a desired
accuracy $\epsilon$, independent of the dataset size $n$. We will show that the
proposed stochastic gradient descent algorithm works in general (possible
infinite-dimensional) Hilbert spaces, is easy to implement and requires no
additional step-size parameters.
Vertex nomination schemes for membership prediction
Authors: D. E. Fishkind, V. Lyzinski, H. Pao, L. Chen, C. E. Priebe
Date: 2013-12-10 00:00:00
Tasks: Prediction, Stochastic Block Model

Suppose that a graph is realized from a stochastic block model where one of
the blocks is of interest, but many or all of the vertices' block labels are
unobserved. The task is to order the vertices with unobserved block labels into
a ``nomination list'' such that, with high probability, vertices from the
interesting block are concentrated near the list's beginning. We propose
several vertex nomination schemes. Our basic - but principled - setting and
development yields a best nomination scheme (which is a Bayes-Optimal
analogue), and also a likelihood maximization nomination scheme that is
practical to implement when there are a thousand vertices, and which is
empirically near-optimal when the number of vertices is small enough to allow
comparison to the best nomination scheme. We then illustrate the robustness of
the likelihood maximization nomination scheme to the modeling challenges
inherent in real data, using examples which include a social network involving
human trafficking, the Enron Graph, a worm brain connectome and a political
blog network.
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Authors: Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo
Date: 2015-11-12 00:00:00
Tasks: Domain Adaptation

Since Convolutional Neural Networks (CNNs) have become the leading learning
paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based
classifiers have lost momentum in the community. This is because (1) such
algorithms cannot use CNN activations as input features; (2) they cannot be
used as final layer of CNN architectures for end-to-end training , and (3) they
are generally not scalable and hence cannot handle big data. This paper
proposes a framework that addresses all these issues, thus bringing back NBNNs
on the map. We solve the first by extracting CNN activations from local patches
at multiple scale levels, similarly to [1]. We address simultaneously the
second and third by proposing a scalable version of Naive Bayes Non-linear
Learning (NBNL, [2]). Results obtained using pre-trained CNNs on standard scene
and domain adaptation databases show the strength of our approach, opening a
new season for NBNNs.
Understanding learned CNN features through Filter Decoding with Substitution
Authors: Ivet Rafegas, Maria Vanrell
Date: 2015-11-16 00:00:00

In parallel with the success of CNNs to solve vision problems, there is a
growing interest in developing methodologies to understand and visualize the
internal representations of these networks. How the responses of a trained CNN
encode the visual information is a fundamental question both for computer and
human vision research. Image representations provided by the first
convolutional layer as well as the resolution change provided by the
max-polling operation are easy to understand, however, as soon as a second and
further convolutional layers are added in the representation, any intuition is
lost. A usual way to deal with this problem has been to define deconvolutional
networks that somehow allow to explore the internal representations of the most
important activations towards the image space, where deconvolution is assumed
as a convolution with the transposed filter. However, this assumption is not
the best approximation of an inverse convolution. In this paper we propose a
new assumption based on filter substitution to reverse the encoding of a
convolutional layer. This provides us with a new tool to directly visualize any
CNN single neuron as a filter in the first layer, this is in terms of the image
space.
Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes
Authors: Christophe Giraud, François Roueff, Andres Sanchez-Perez
Date: 2014-04-27 00:00:00

In this work, we study the problem of aggregating a finite number of
predictors for nonstationary sub-linear processes. We provide oracle
inequalities relying essentially on three ingredients: (1) a uniform bound of
the $\ell^1$ norm of the time varying sub-linear coefficients, (2) a Lipschitz
assumption on the predictors and (3) moment conditions on the noise appearing
in the linear representation. Two kinds of aggregations are considered giving
rise to different moment conditions on the noise and more or less sharp oracle
inequalities. We apply this approach for deriving an adaptive predictor for
locally stationary time varying autoregressive (TVAR) processes. It is obtained
by aggregating a finite number of well chosen predictors, each of them enjoying
an optimal minimax convergence rate under specific smoothness conditions on the
TVAR coefficients. We show that the obtained aggregated predictor achieves a
minimax rate while adapting to the unknown smoothness. To prove this result, a
lower bound is established for the minimax rate of the prediction risk for the
TVAR process. Numerical experiments complete this study. An important feature
of this approach is that the aggregated predictor can be computed recursively
and is thus applicable in an online prediction context.
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Authors: Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey
Date: 2015-11-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning, Segmentation

Convolutional neural networks (CNN) have achieved state of the art
performance on both classification and segmentation tasks. Applying CNNs to
microscopy images is challenging due to the lack of datasets labeled at the
single cell level. We extend the application of CNNs to microscopy image
classification and segmentation using multiple instance learning (MIL). We
present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is
robust to outliers. Combining CNNs with MIL enables training CNNs using full
resolution microscopy images with global labels. We base our approach on the
similarity between the aggregation function used in MIL and pooling layers used
in CNNs. We show that training MIL CNNs end-to-end outperforms several previous
methods on both mammalian and yeast microscopy images without requiring any
segmentation steps.
Semi-supervised Collaborative Ranking with Push at Top
Authors: Iman Barjasteh, Rana Forsati, Abdol-Hossein Esfahanian, Hayder Radha
Date: 2015-11-17 00:00:00
Tasks: Collaborative Ranking, Recommendation Systems

Existing collaborative ranking based recommender systems tend to perform best
when there is enough observed ratings for each user and the observation is made
completely at random. Under this setting recommender systems can properly
suggest a list of recommendations according to the user interests. However,
when the observed ratings are extremely sparse (e.g. in the case of cold-start
users where no rating data is available), and are not sampled uniformly at
random, existing ranking methods fail to effectively leverage side information
to transduct the knowledge from existing ratings to unobserved ones. We propose
a semi-supervised collaborative ranking model, dubbed \texttt{S$^2$COR}, to
improve the quality of cold-start recommendation. \texttt{S$^2$COR} mitigates
the sparsity issue by leveraging side information about both observed and
missing ratings by collaboratively learning the ranking model. This enables it
to deal with the case of missing data not at random, but to also effectively
incorporate the available side information in transduction. We experimentally
evaluated our proposed algorithm on a number of challenging real-world datasets
and compared against state-of-the-art models for cold-start recommendation. We
report significantly higher quality recommendations with our algorithm compared
to the state-of-the-art.