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This work targets what we consider to be the foundational step for urban
airborne robots, a safe landing. Our attention is directed toward what we deem
the most crucial aspect of the safe landing perception stack: segmentation. We
present a streamlined reactive UAV system that employs visual servoing by
harnessing the capabilities of open vocabulary image segmentation. This
approach can adapt to various scenarios with minimal adjustments, bypassing the
necessity for extensive data accumulation for refining internal models, thanks
to its open vocabulary methodology. Given the limitations imposed by local
authorities, our primary focus centers on operations originating from altitudes
of 100 meters. This choice is deliberate, as numerous preceding works have
dealt with altitudes up to 30 meters, aligning with the capabilities of small
stereo cameras. Consequently, we leave the remaining 20m to be navigated using
conventional 3D path planning methods. Utilizing monocular cameras and image
segmentation, our findings demonstrate the system's capability to successfully
execute landing maneuvers at altitudes as low as 20 meters. However, this
approach is vulnerable to intermittent and occasionally abrupt fluctuations in
the segmentation between frames in a video stream. To address this challenge,
we enhance the image segmentation output by introducing what we call a dynamic
focus: a masking mechanism that self adjusts according to the current landing
stage. This dynamic focus guides the control system to avoid regions beyond the
drone's safety radius projected onto the ground, thus mitigating the problems
with fluctuations. Through the implementation of this supplementary layer, our
experiments have reached improvements in the landing success rate of almost
tenfold when compared to global segmentation. All the source code is open
source and available online (github.com/MISTLab/DOVESEI). | [
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] |
Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is
a widely used modality for diagnosing myocardial blood flow (perfusion)
abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300
time-resolved images of myocardial perfusion are acquired at various contrast
"wash in/out" phases. Manual segmentation of myocardial contours in each
time-frame of a DCE image series can be tedious and time-consuming,
particularly when non-rigid motion correction has failed or is unavailable.
While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI
datasets, a "dynamic quality control" (dQC) technique for reliably detecting
failed segmentations is lacking. Here we propose a new space-time uncertainty
metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI
datasets by validating the proposed metric on an external dataset and
establishing a human-in-the-loop framework to improve the segmentation results.
In the proposed approach, we referred the top 10% most uncertain segmentations
as detected by our dQC tool to the human expert for refinement. This approach
resulted in a significant increase in the Dice score (p<0.001) and a notable
decrease in the number of images with failed segmentation (16.2% to 11.3%)
whereas the alternative approach of randomly selecting the same number of
segmentations for human referral did not achieve any significant improvement.
Our results suggest that the proposed dQC framework has the potential to
accurately identify poor-quality segmentations and may enable efficient
DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical
interpretation and reporting of dynamic CMRI datasets. | [
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] |
Transfer learning represents a recent paradigm shift in the way we build
artificial intelligence (AI) systems. In contrast to training task-specific
models, transfer learning involves pre-training deep learning models on a large
corpus of data and minimally fine-tuning them for adaptation to specific tasks.
Even so, for 3D medical imaging tasks, we do not know if it is best to
pre-train models on natural images, medical images, or even synthetically
generated MRI scans or video data. To evaluate these alternatives, here we
benchmarked vision transformers (ViTs) and convolutional neural networks
(CNNs), initialized with varied upstream pre-training approaches. These methods
were then adapted to three unique downstream neuroimaging tasks with a range of
difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD)
classification, "brain age" prediction. Experimental tests led to the following
key observations: 1. Pre-training improved performance across all tasks
including a boost of 7.4% for AD classification and 4.6% for PD classification
for the ViT and 19.1% for PD classification and reduction in brain age
prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video
or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in
limited-data settings, and in-domain pretraining enhanced their performances,
4. Pre-training improved generalization to out-of-distribution datasets and
sites. Overall, we benchmarked different vision architectures, revealing the
value of pre-training them with emerging datasets for model initialization. The
resulting pre-trained models can be adapted to a range of downstream
neuroimaging tasks, even when training data for the target task is limited. | [
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Wire harnesses are essential hardware for electronic systems in modern
automotive vehicles. With a shift in the automotive industry towards
electrification and autonomous driving, more and more automotive electronics
are responsible for energy transmission and safety-critical functions such as
maneuvering, driver assistance, and safety system. This paradigm shift places
more demand on automotive wiring harnesses from the safety perspective and
stresses the greater importance of high-quality wire harness assembly in
vehicles. However, most of the current operations of wire harness assembly are
still performed manually by skilled workers, and some of the manual processes
are problematic from different perspectives, such as quality control and
ergonomics. There is also a persistent demand in the industry to increase
competitiveness and gain market share. Hence, assuring assembly quality while
improving ergonomics and optimizing labor costs is desired. Robotized assembly,
accomplished by robots or in human-robot collaboration, is a key enabler for
fulfilling the increasingly demanding quality and safety as it enables more
replicable, transparent, and comprehensible processes than completely manual
operations. However, robotized assembly of wire harnesses is challenging in
real environments due to the flexibility of the deformable objects, though many
preliminary automation solutions have been proposed under simplified industrial
configurations. Previous research efforts have proposed the use of computer
vision technology to facilitate robotized automation of wire harness assembly,
enabling the robots to better perceive and manipulate the flexible wire
harness. This article presents an overview on computer vision technology
proposed for robotized wire harness assembly and derives research gaps that
require further study to facilitate a more practical robotized assembly of wire
harness. | [
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] |
Pediatric brain and spinal cancers remain the leading cause of cancer-related
death in children. Advancements in clinical decision-support in pediatric
neuro-oncology utilizing the wealth of radiology imaging data collected through
standard care, however, has significantly lagged other domains. Such data is
ripe for use with predictive analytics such as artificial intelligence (AI)
methods, which require large datasets. To address this unmet need, we provide a
multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric
MRI exams acquired through routine care for 1,526 brain tumor patients, as part
of the Children's Brain Tumor Network. This includes longitudinal MRIs across
various cancer diagnoses, with associated patient-level clinical information,
digital pathology slides, as well as tissue genotype and omics data. To
facilitate downstream analysis, treatment-na\"ive images for 370 subjects were
processed and released through the NCI Childhood Cancer Data Initiative via the
Cancer Data Service. Through ongoing efforts to continuously build these
imaging repositories, our aim is to accelerate discovery and translational AI
models with real-world data, to ultimately empower precision medicine for
children. | [
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] |
Reading and interpreting chest X-ray images is one of the most radiologist's
routines. However, it still can be challenging, even for the most experienced
ones. Therefore, we proposed a multi-model deep learning-based automated chest
X-ray report generator system designed to assist radiologists in their work.
The basic idea of the proposed system is by utilizing multi
binary-classification models for detecting multi abnormalities, with each model
responsible for detecting one abnormality, in a single image. In this study, we
limited the radiology abnormalities detection to only cardiomegaly, lung
effusion, and consolidation. The system generates a radiology report by
performing the following three steps: image pre-processing, utilizing deep
learning models to detect abnormalities, and producing a report. The aim of the
image pre-processing step is to standardize the input by scaling it to 128x128
pixels and slicing it into three segments, which covers the upper, lower, and
middle parts of the lung. After pre-processing, each corresponding model
classifies the image, resulting in a 0 (zero) for no abnormality detected and a
1 (one) for the presence of an abnormality. The prediction outputs of each
model are then concatenated to form a 'result code'. The 'result code' is used
to construct a report by selecting the appropriate pre-determined sentence for
each detected abnormality in the report generation step. The proposed system is
expected to reduce the workload of radiologists and increase the accuracy of
chest X-ray diagnosis. | [
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Object detection is the central issue of intelligent traffic systems, and
recent advancements in single-vehicle lidar-based 3D detection indicate that it
can provide accurate position information for intelligent agents to make
decisions and plan. Compared with single-vehicle perception, multi-view
vehicle-road cooperation perception has fundamental advantages, such as the
elimination of blind spots and a broader range of perception, and has become a
research hotspot. However, the current perception of cooperation focuses on
improving the complexity of fusion while ignoring the fundamental problems
caused by the absence of single-view outlines. We propose a multi-view
vehicle-road cooperation perception system, vehicle-to-everything cooperative
perception (V2X-AHD), in order to enhance the identification capability,
particularly for predicting the vehicle's shape. At first, we propose an
asymmetric heterogeneous distillation network fed with different training data
to improve the accuracy of contour recognition, with multi-view teacher
features transferring to single-view student features. While the point cloud
data are sparse, we propose Spara Pillar, a spare convolutional-based plug-in
feature extraction backbone, to reduce the number of parameters and improve and
enhance feature extraction capabilities. Moreover, we leverage the multi-head
self-attention (MSA) to fuse the single-view feature, and the lightweight
design makes the fusion feature a smooth expression. The results of applying
our algorithm to the massive open dataset V2Xset demonstrate that our method
achieves the state-of-the-art result. The V2X-AHD can effectively improve the
accuracy of 3D object detection and reduce the number of network parameters,
according to this study, which serves as a benchmark for cooperative
perception. The code for this article is available at
https://github.com/feeling0414-lab/V2X-AHD. | [
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] |
Transformer is a popularly used neural network architecture, especially for
language understanding. We introduce an extended and unified architecture that
can be used for tasks involving a variety of modalities like image, text,
videos, etc. We propose a spatio-temporal cache mechanism that enables learning
spatial dimension of the input in addition to the hidden states corresponding
to the temporal input sequence. The proposed architecture further enables a
single model to support tasks with multiple input modalities as well as
asynchronous multi-task learning, thus we refer to it as OmniNet. For example,
a single instance of OmniNet can concurrently learn to perform the tasks of
part-of-speech tagging, image captioning, visual question answering and video
activity recognition. We demonstrate that training these four tasks together
results in about three times compressed model while retaining the performance
in comparison to training them individually. We also show that using this
neural network pre-trained on some modalities assists in learning unseen tasks
such as video captioning and video question answering. This illustrates the
generalization capacity of the self-attention mechanism on the spatio-temporal
cache present in OmniNet. | [
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] |
Recurrent Neural Networks with Long Short-Term Memory (LSTM) make use of
gating mechanisms to mitigate exploding and vanishing gradients when learning
long-term dependencies. For this reason, LSTMs and other gated RNNs are widely
adopted, being the standard de facto for many sequence modeling tasks. Although
the memory cell inside the LSTM contains essential information, it is not
allowed to influence the gating mechanism directly. In this work, we improve
the gate potential by including information coming from the internal cell
state. The proposed modification, named Working Memory Connection, consists in
adding a learnable nonlinear projection of the cell content into the network
gates. This modification can fit into the classical LSTM gates without any
assumption on the underlying task, being particularly effective when dealing
with longer sequences. Previous research effort in this direction, which goes
back to the early 2000s, could not bring a consistent improvement over vanilla
LSTM. As part of this paper, we identify a key issue tied to previous
connections that heavily limits their effectiveness, hence preventing a
successful integration of the knowledge coming from the internal cell state. We
show through extensive experimental evaluation that Working Memory Connections
constantly improve the performance of LSTMs on a variety of tasks. Numerical
results suggest that the cell state contains useful information that is worth
including in the gate structure. | [
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] |
The Bayesian framework is ideally suited for induction problems. The
probability of observing $x_t$ at time $t$, given past observations
$x_1...x_{t-1}$ can be computed with Bayes' rule if the true distribution $\mu$
of the sequences $x_1x_2x_3...$ is known. The problem, however, is that in many
cases one does not even have a reasonable estimate of the true distribution. In
order to overcome this problem a universal distribution $\xi$ is defined as a
weighted sum of distributions $\mu_i\inM$, where $M$ is any countable set of
distributions including $\mu$. This is a generalization of Solomonoff
induction, in which $M$ is the set of all enumerable semi-measures. Systems
which predict $y_t$, given $x_1...x_{t-1}$ and which receive loss $l_{x_t y_t}$
if $x_t$ is the true next symbol of the sequence are considered. It is proven
that using the universal $\xi$ as a prior is nearly as good as using the
unknown true distribution $\mu$. Furthermore, games of chance, defined as a
sequence of bets, observations, and rewards are studied. The time needed to
reach the winning zone is bounded in terms of the relative entropy of $\mu$ and
$\xi$. Extensions to arbitrary alphabets, partial and delayed prediction, and
more active systems are discussed. | [
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] |
The problem of making sequential decisions in unknown probabilistic
environments is studied. In cycle $t$ action $y_t$ results in perception $x_t$
and reward $r_t$, where all quantities in general may depend on the complete
history. The perception $x_t$ and reward $r_t$ are sampled from the (reactive)
environmental probability distribution $\mu$. This very general setting
includes, but is not limited to, (partial observable, k-th order) Markov
decision processes. Sequential decision theory tells us how to act in order to
maximize the total expected reward, called value, if $\mu$ is known.
Reinforcement learning is usually used if $\mu$ is unknown. In the Bayesian
approach one defines a mixture distribution $\xi$ as a weighted sum of
distributions $\nu\in\M$, where $\M$ is any class of distributions including
the true environment $\mu$. We show that the Bayes-optimal policy $p^\xi$ based
on the mixture $\xi$ is self-optimizing in the sense that the average value
converges asymptotically for all $\mu\in\M$ to the optimal value achieved by
the (infeasible) Bayes-optimal policy $p^\mu$ which knows $\mu$ in advance. We
show that the necessary condition that $\M$ admits self-optimizing policies at
all, is also sufficient. No other structural assumptions are made on $\M$. As
an example application, we discuss ergodic Markov decision processes, which
allow for self-optimizing policies. Furthermore, we show that $p^\xi$ is
Pareto-optimal in the sense that there is no other policy yielding higher or
equal value in {\em all} environments $\nu\in\M$ and a strictly higher value in
at least one. | [
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] |
Recursive loops in a logic program present a challenging problem to the PLP
framework. On the one hand, they loop forever so that the PLP backward-chaining
inferences would never stop. On the other hand, they generate cyclic
influences, which are disallowed in Bayesian networks. Therefore, in existing
PLP approaches logic programs with recursive loops are considered to be
problematic and thus are excluded. In this paper, we propose an approach that
makes use of recursive loops to build a stationary dynamic Bayesian network.
Our work stems from an observation that recursive loops in a logic program
imply a time sequence and thus can be used to model a stationary dynamic
Bayesian network without using explicit time parameters. We introduce a
Bayesian knowledge base with logic clauses of the form $A \leftarrow
A_1,...,A_l, true, Context, Types$, which naturally represents the knowledge
that the $A_i$s have direct influences on $A$ in the context $Context$ under
the type constraints $Types$. We then use the well-founded model of a logic
program to define the direct influence relation and apply SLG-resolution to
compute the space of random variables together with their parental connections.
We introduce a novel notion of influence clauses, based on which a declarative
semantics for a Bayesian knowledge base is established and algorithms for
building a two-slice dynamic Bayesian network from a logic program are
developed. | [
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] |
Several technologies are emerging that provide new ways to capture, store,
present and use knowledge. This book is the first to provide a comprehensive
introduction to five of the most important of these technologies: Knowledge
Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and
Semantic Webs. For each of these, answers are given to a number of key
questions (What is it? How does it operate? How is a system developed? What can
it be used for? What tools are available? What are the main issues?). The book
is aimed at students, researchers and practitioners interested in Knowledge
Management, Artificial Intelligence, Design Engineering and Web Technologies.
During the 1990s, Nick worked at the University of Nottingham on the
application of AI techniques to knowledge management and on various knowledge
acquisition projects to develop expert systems for military applications. In
1999, he joined Epistemics where he worked on numerous knowledge projects and
helped establish knowledge management programmes at large organisations in the
engineering, technology and legal sectors. He is author of the book "Knowledge
Acquisition in Practice", which describes a step-by-step procedure for
acquiring and implementing expertise. He maintains strong links with leading
research organisations working on knowledge technologies, such as
knowledge-based engineering, ontologies and semantic technologies. | [
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] |
The key approaches for machine learning, especially learning in unknown
probabilistic environments are new representations and computation mechanisms.
In this paper, a novel quantum reinforcement learning (QRL) method is proposed
by combining quantum theory and reinforcement learning (RL). Inspired by the
state superposition principle and quantum parallelism, a framework of value
updating algorithm is introduced. The state (action) in traditional RL is
identified as the eigen state (eigen action) in QRL. The state (action) set can
be represented with a quantum superposition state and the eigen state (eigen
action) can be obtained by randomly observing the simulated quantum state
according to the collapse postulate of quantum measurement. The probability of
the eigen action is determined by the probability amplitude, which is
parallelly updated according to rewards. Some related characteristics of QRL
such as convergence, optimality and balancing between exploration and
exploitation are also analyzed, which shows that this approach makes a good
tradeoff between exploration and exploitation using the probability amplitude
and can speed up learning through the quantum parallelism. To evaluate the
performance and practicability of QRL, several simulated experiments are given
and the results demonstrate the effectiveness and superiority of QRL algorithm
for some complex problems. The present work is also an effective exploration on
the application of quantum computation to artificial intelligence. | [
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] |
In this paper we analyze judgement aggregation problems in which a group of
agents independently votes on a set of complex propositions that has some
interdependency constraint between them(e.g., transitivity when describing
preferences). We consider the issue of judgement aggregation from the
perspective of approximation. That is, we generalize the previous results by
studying approximate judgement aggregation. We relax the main two constraints
assumed in the current literature, Consistency and Independence and consider
mechanisms that only approximately satisfy these constraints, that is, satisfy
them up to a small portion of the inputs. The main question we raise is whether
the relaxation of these notions significantly alters the class of satisfying
aggregation mechanisms. The recent works for preference aggregation of Kalai,
Mossel, and Keller fit into this framework. The main result of this paper is
that, as in the case of preference aggregation, in the case of a subclass of a
natural class of aggregation problems termed `truth-functional agendas', the
set of satisfying aggregation mechanisms does not extend non-trivially when
relaxing the constraints. Our proof techniques involve Boolean Fourier
transform and analysis of voter influences for voting protocols. The question
we raise for Approximate Aggregation can be stated in terms of Property
Testing. For instance, as a corollary from our result we get a generalization
of the classic result for property testing of linearity of Boolean functions.
An updated version (RePEc:huj:dispap:dp574R) is available at
http://www.ratio.huji.ac.il/dp_files/dp574R.pdf | [
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] |
In a Role-Playing Game, finding optimal trajectories is one of the most
important tasks. In fact, the strategy decision system becomes a key component
of a game engine. Determining the way in which decisions are taken (online,
batch or simulated) and the consumed resources in decision making (e.g.
execution time, memory) will influence, in mayor degree, the game performance.
When classical search algorithms such as A* can be used, they are the very
first option. Nevertheless, such methods rely on precise and complete models of
the search space, and there are many interesting scenarios where their
application is not possible. Then, model free methods for sequential decision
making under uncertainty are the best choice. In this paper, we propose a
heuristic planning strategy to incorporate the ability of heuristic-search in
path-finding into a Dyna agent. The proposed Dyna-H algorithm, as A* does,
selects branches more likely to produce outcomes than other branches. Besides,
it has the advantages of being a model-free online reinforcement learning
algorithm. The proposal was evaluated against the one-step Q-Learning and
Dyna-Q algorithms obtaining excellent experimental results: Dyna-H
significantly overcomes both methods in all experiments. We suggest also, a
functional analogy between the proposed sampling from worst trajectories
heuristic and the role of dreams (e.g. nightmares) in human behavior. | [
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] |
In this paper, we study CPU utilization time patterns of several Map-Reduce
applications. After extracting running patterns of several applications, the
patterns with their statistical information are saved in a reference database
to be later used to tweak system parameters to efficiently execute unknown
applications in future. To achieve this goal, CPU utilization patterns of new
applications along with its statistical information are compared with the
already known ones in the reference database to find/predict their most
probable execution patterns. Because of different patterns lengths, the Dynamic
Time Warping (DTW) is utilized for such comparison; a statistical analysis is
then applied to DTWs' outcomes to select the most suitable candidates.
Moreover, under a hypothesis, another algorithm is proposed to classify
applications under similar CPU utilization patterns. Three widely used text
processing applications (WordCount, Distributed Grep, and Terasort) and another
application (Exim Mainlog parsing) are used to evaluate our hypothesis in
tweaking system parameters in executing similar applications. Results were very
promising and showed effectiveness of our approach on 5-node Map-Reduce
platform | [
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] |
We consider unsupervised estimation of mixtures of discrete graphical models,
where the class variable corresponding to the mixture components is hidden and
each mixture component over the observed variables can have a potentially
different Markov graph structure and parameters. We propose a novel approach
for estimating the mixture components, and our output is a tree-mixture model
which serves as a good approximation to the underlying graphical model mixture.
Our method is efficient when the union graph, which is the union of the Markov
graphs of the mixture components, has sparse vertex separators between any pair
of observed variables. This includes tree mixtures and mixtures of bounded
degree graphs. For such models, we prove that our method correctly recovers the
union graph structure and the tree structures corresponding to
maximum-likelihood tree approximations of the mixture components. The sample
and computational complexities of our method scale as $\poly(p, r)$, for an
$r$-component mixture of $p$-variate graphical models. We further extend our
results to the case when the union graph has sparse local separators between
any pair of observed variables, such as mixtures of locally tree-like graphs,
and the mixture components are in the regime of correlation decay. | [
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] |
To understand the structural dynamics of a large-scale social, biological or
technological network, it may be useful to discover behavioral roles
representing the main connectivity patterns present over time. In this paper,
we propose a scalable non-parametric approach to automatically learn the
structural dynamics of the network and individual nodes. Roles may represent
structural or behavioral patterns such as the center of a star, peripheral
nodes, or bridge nodes that connect different communities. Our novel approach
learns the appropriate structural role dynamics for any arbitrary network and
tracks the changes over time. In particular, we uncover the specific global
network dynamics and the local node dynamics of a technological, communication,
and social network. We identify interesting node and network patterns such as
stationary and non-stationary roles, spikes/steps in role-memberships (perhaps
indicating anomalies), increasing/decreasing role trends, among many others.
Our results indicate that the nodes in each of these networks have distinct
connectivity patterns that are non-stationary and evolve considerably over
time. Overall, the experiments demonstrate the effectiveness of our approach
for fast mining and tracking of the dynamics in large networks. Furthermore,
the dynamic structural representation provides a basis for building more
sophisticated models and tools that are fast for exploring large dynamic
networks. | [
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] |
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed. | [
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] |
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials. | [
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] |
The exploration/exploitation (E/E) dilemma arises naturally in many subfields
of Science. Multi-armed bandit problems formalize this dilemma in its canonical
form. Most current research in this field focuses on generic solutions that can
be applied to a wide range of problems. However, in practice, it is often the
case that a form of prior information is available about the specific class of
target problems. Prior knowledge is rarely used in current solutions due to the
lack of a systematic approach to incorporate it into the E/E strategy.
To address a specific class of E/E problems, we propose to proceed in three
steps: (i) model prior knowledge in the form of a probability distribution over
the target class of E/E problems; (ii) choose a large hypothesis space of
candidate E/E strategies; and (iii), solve an optimization problem to find a
candidate E/E strategy of maximal average performance over a sample of problems
drawn from the prior distribution.
We illustrate this meta-learning approach with two different hypothesis
spaces: one where E/E strategies are numerically parameterized and another
where E/E strategies are represented as small symbolic formulas. We propose
appropriate optimization algorithms for both cases. Our experiments, with
two-armed Bernoulli bandit problems and various playing budgets, show that the
meta-learnt E/E strategies outperform generic strategies of the literature
(UCB1, UCB1-Tuned, UCB-v, KL-UCB and epsilon greedy); they also evaluate the
robustness of the learnt E/E strategies, by tests carried out on arms whose
rewards follow a truncated Gaussian distribution. | [
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] |
Direct policy search (DPS) and look-ahead tree (LT) policies are two widely
used classes of techniques to produce high performance policies for sequential
decision-making problems. To make DPS approaches work well, one crucial issue
is to select an appropriate space of parameterized policies with respect to the
targeted problem. A fundamental issue in LT approaches is that, to take good
decisions, such policies must develop very large look-ahead trees which may
require excessive online computational resources. In this paper, we propose a
new hybrid policy learning scheme that lies at the intersection of DPS and LT,
in which the policy is an algorithm that develops a small look-ahead tree in a
directed way, guided by a node scoring function that is learned through DPS.
The LT-based representation is shown to be a versatile way of representing
policies in a DPS scheme, while at the same time, DPS enables to significantly
reduce the size of the look-ahead trees that are required to take high-quality
decisions.
We experimentally compare our method with two other state-of-the-art DPS
techniques and four common LT policies on four benchmark domains and show that
it combines the advantages of the two techniques from which it originates. In
particular, we show that our method: (1) produces overall better performing
policies than both pure DPS and pure LT policies, (2) requires a substantially
smaller number of policy evaluations than other DPS techniques, (3) is easy to
tune and (4) results in policies that are quite robust with respect to
perturbations of the initial conditions. | [
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] |
Perhaps surprisingly, it is possible to predict how long an algorithm will
take to run on a previously unseen input, using machine learning techniques to
build a model of the algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to algorithm
analysis, portfolio-based algorithm selection, and the automatic configuration
of parameterized algorithms. Over the past decade, a wide variety of techniques
have been studied for building such models. Here, we describe extensions and
improvements of existing models, new families of models, and -- perhaps most
importantly -- a much more thorough treatment of algorithm parameters as model
inputs. We also comprehensively describe new and existing features for
predicting algorithm runtime for propositional satisfiability (SAT), travelling
salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate
these innovations through the largest empirical analysis of its kind, comparing
to a wide range of runtime modelling techniques from the literature. Our
experiments consider 11 algorithms and 35 instance distributions; they also
span a very wide range of SAT, MIP, and TSP instances, with the least
structured having been generated uniformly at random and the most structured
having emerged from real industrial applications. Overall, we demonstrate that
our new models yield substantially better runtime predictions than previous
approaches in terms of their generalization to new problem instances, to new
algorithms from a parameterized space, and to both simultaneously. | [
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] |
The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided. | [
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] |
Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. This paper investigates how classical
inference and learning tasks known from the graphical model community can be
tackled for probabilistic logic programs. Several such tasks such as computing
the marginals given evidence and learning from (partial) interpretations have
not really been addressed for probabilistic logic programs before.
The first contribution of this paper is a suite of efficient algorithms for
various inference tasks. It is based on a conversion of the program and the
queries and evidence to a weighted Boolean formula. This allows us to reduce
the inference tasks to well-studied tasks such as weighted model counting,
which can be solved using state-of-the-art methods known from the graphical
model and knowledge compilation literature. The second contribution is an
algorithm for parameter estimation in the learning from interpretations
setting. The algorithm employs Expectation Maximization, and is built on top of
the developed inference algorithms.
The proposed approach is experimentally evaluated. The results show that the
inference algorithms improve upon the state-of-the-art in probabilistic logic
programming and that it is indeed possible to learn the parameters of a
probabilistic logic program from interpretations. | [
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] |
In this paper, we consider active information acquisition when the prediction
model is meant to be applied on a targeted subset of the population. The goal
is to label a pre-specified fraction of customers in the target or test set by
iteratively querying for information from the non-target or training set. The
number of queries is limited by an overall budget. Arising in the context of
two rather disparate applications- banking and medical diagnosis, we pose the
active information acquisition problem as a constrained optimization problem.
We propose two greedy iterative algorithms for solving the above problem. We
conduct experiments with synthetic data and compare results of our proposed
algorithms with few other baseline approaches. The experimental results show
that our proposed approaches perform better than the baseline schemes. | [
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] |
We investigate the Student-t process as an alternative to the Gaussian
process as a nonparametric prior over functions. We derive closed form
expressions for the marginal likelihood and predictive distribution of a
Student-t process, by integrating away an inverse Wishart process prior over
the covariance kernel of a Gaussian process model. We show surprising
equivalences between different hierarchical Gaussian process models leading to
Student-t processes, and derive a new sampling scheme for the inverse Wishart
process, which helps elucidate these equivalences. Overall, we show that a
Student-t process can retain the attractive properties of a Gaussian process --
a nonparametric representation, analytic marginal and predictive distributions,
and easy model selection through covariance kernels -- but has enhanced
flexibility, and predictive covariances that, unlike a Gaussian process,
explicitly depend on the values of training observations. We verify empirically
that a Student-t process is especially useful in situations where there are
changes in covariance structure, or in applications like Bayesian optimization,
where accurate predictive covariances are critical for good performance. These
advantages come at no additional computational cost over Gaussian processes. | [
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] |
Identification of the influential clinical symptoms and laboratory features
that help in the diagnosis of dengue fever in early phase of the illness would
aid in designing effective public health management and virological
surveillance strategies. Keeping this as our main objective we develop in this
paper, a new computational intelligence based methodology that predicts the
diagnosis in real time, minimizing the number of false positives and false
negatives. Our methodology consists of three major components (i) a novel
missing value imputation procedure that can be applied on any data set
consisting of categorical (nominal) and/or numeric (real or integer) (ii) a
wrapper based features selection method with genetic search for extracting a
subset of most influential symptoms that can diagnose the illness and (iii) an
alternating decision tree method that employs boosting for generating highly
accurate decision rules. The predictive models developed using our methodology
are found to be more accurate than the state-of-the-art methodologies used in
the diagnosis of the dengue fever. | [
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Monaural source separation is important for many real world applications. It
is challenging because, with only a single channel of information available,
without any constraints, an infinite number of solutions are possible. In this
paper, we explore joint optimization of masking functions and deep recurrent
neural networks for monaural source separation tasks, including monaural speech
separation, monaural singing voice separation, and speech denoising. The joint
optimization of the deep recurrent neural networks with an extra masking layer
enforces a reconstruction constraint. Moreover, we explore a discriminative
criterion for training neural networks to further enhance the separation
performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT
datasets for speech separation, singing voice separation, and speech denoising
tasks, respectively. Our approaches achieve 2.30--4.98 dB SDR gain compared to
NMF models in the speech separation task, 2.30--2.48 dB GNSDR gain and
4.32--5.42 dB GSIR gain compared to existing models in the singing voice
separation task, and outperform NMF and DNN baselines in the speech denoising
task. | [
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] |
We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showing how to convert a large class of no-regret algorithms into
online posted-price and learning mechanisms. Our results in a sense parallel
classic sample complexity guarantees, but with the key resource being money
rather than quantity of data: With a budget constraint $B$, we give robust risk
(predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an
active approach, we can often guarantee to do significantly better by
leveraging correlations between costs and data.
Our algorithms and analysis go through a model of no-regret learning with $T$
arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds
for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the
same order. | [
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] |
This paper presents a framework for exact discovery of the top-k sequential
patterns under Leverage. It combines (1) a novel definition of the expected
support for a sequential pattern - a concept on which most interestingness
measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for
the exact discovery of top-k sequential patterns under a given measure of
interest. Our interestingness measure employs the partition approach. A pattern
is interesting to the extent that it is more frequent than can be explained by
assuming independence between any of the pairs of patterns from which it can be
composed. The larger the support compared to the expectation under
independence, the more interesting is the pattern. We build on these two
elements to exactly extract the k sequential patterns with highest leverage,
consistent with our definition of expected support. We conduct experiments on
both synthetic data with known patterns and real-world datasets; both
experiments confirm the consistency and relevance of our approach with regard
to the state of the art. This article was published in Data Mining and
Knowledge Discovery and is accessible at
http://dx.doi.org/10.1007/s10618-016-0467-9. | [
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] |
Probabilistic graphical models offer a powerful framework to account for the
dependence structure between variables, which is represented as a graph.
However, the dependence between variables may render inference tasks
intractable. In this paper we review techniques exploiting the graph structure
for exact inference, borrowed from optimisation and computer science. They are
built on the principle of variable elimination whose complexity is dictated in
an intricate way by the order in which variables are eliminated. The so-called
treewidth of the graph characterises this algorithmic complexity: low-treewidth
graphs can be processed efficiently. The first message that we illustrate is
therefore the idea that for inference in graphical model, the number of
variables is not the limiting factor, and it is worth checking for the
treewidth before turning to approximate methods. We show how algorithms
providing an upper bound of the treewidth can be exploited to derive a 'good'
elimination order enabling to perform exact inference. The second message is
that when the treewidth is too large, algorithms for approximate inference
linked to the principle of variable elimination, such as loopy belief
propagation and variational approaches, can lead to accurate results while
being much less time consuming than Monte-Carlo approaches. We illustrate the
techniques reviewed in this article on benchmarks of inference problems in
genetic linkage analysis and computer vision, as well as on hidden variables
restoration in coupled Hidden Markov Models. | [
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] |
The true online TD({\lambda}) algorithm has recently been proposed (van
Seijen and Sutton, 2014) as a universal replacement for the popular
TD({\lambda}) algorithm, in temporal-difference learning and reinforcement
learning. True online TD({\lambda}) has better theoretical properties than
conventional TD({\lambda}), and the expectation is that it also results in
faster learning. In this paper, we put this hypothesis to the test.
Specifically, we compare the performance of true online TD({\lambda}) with that
of TD({\lambda}) on challenging examples, random Markov reward processes, and a
real-world myoelectric prosthetic arm. We use linear function approximation
with tabular, binary, and non-binary features. We assess the algorithms along
three dimensions: computational cost, learning speed, and ease of use. Our
results confirm the strength of true online TD({\lambda}): 1) for sparse
feature vectors, the computational overhead with respect to TD({\lambda}) is
minimal; for non-sparse features the computation time is at most twice that of
TD({\lambda}), 2) across all domains/representations the learning speed of true
online TD({\lambda}) is often better, but never worse than that of
TD({\lambda}), and 3) true online TD({\lambda}) is easier to use, because it
does not require choosing between trace types, and it is generally more stable
with respect to the step-size. Overall, our results suggest that true online
TD({\lambda}) should be the first choice when looking for an efficient,
general-purpose TD method. | [
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] |
Achieving efficient and scalable exploration in complex domains poses a major
challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to
the exploration problem offer strong formal guarantees, they are often
impractical in higher dimensions due to their reliance on enumerating the
state-action space. Hence, exploration in complex domains is often performed
with simple epsilon-greedy methods. In this paper, we consider the challenging
Atari games domain, which requires processing raw pixel inputs and delayed
rewards. We evaluate several more sophisticated exploration strategies,
including Thompson sampling and Boltzman exploration, and propose a new
exploration method based on assigning exploration bonuses from a concurrently
learned model of the system dynamics. By parameterizing our learned model with
a neural network, we are able to develop a scalable and efficient approach to
exploration bonuses that can be applied to tasks with complex, high-dimensional
state spaces. In the Atari domain, our method provides the most consistent
improvement across a range of games that pose a major challenge for prior
methods. In addition to raw game-scores, we also develop an AUC-100 metric for
the Atari Learning domain to evaluate the impact of exploration on this
benchmark. | [
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] |
Approximate Newton methods are a standard optimization tool which aim to
maintain the benefits of Newton's method, such as a fast rate of convergence,
whilst alleviating its drawbacks, such as computationally expensive calculation
or estimation of the inverse Hessian. In this work we investigate approximate
Newton methods for policy optimization in Markov Decision Processes (MDPs). We
first analyse the structure of the Hessian of the objective function for MDPs.
We show that, like the gradient, the Hessian exhibits useful structure in the
context of MDPs and we use this analysis to motivate two Gauss-Newton Methods
for MDPs. Like the Gauss-Newton method for non-linear least squares, these
methods involve approximating the Hessian by ignoring certain terms in the
Hessian which are difficult to estimate. The approximate Hessians possess
desirable properties, such as negative definiteness, and we demonstrate several
important performance guarantees including guaranteed ascent directions,
invariance to affine transformation of the parameter space, and convergence
guarantees. We finally provide a unifying perspective of key policy search
algorithms, demonstrating that our second Gauss-Newton algorithm is closely
related to both the EM-algorithm and natural gradient ascent applied to MDPs,
but performs significantly better in practice on a range of challenging
domains. | [
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] |
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions. | [
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] |
In this paper, we propose a model-based clustering method (TVClust) that
robustly incorporates noisy side information as soft-constraints and aims to
seek a consensus between side information and the observed data. Our method is
based on a nonparametric Bayesian hierarchical model that combines the
probabilistic model for the data instance and the one for the side-information.
An efficient Gibbs sampling algorithm is proposed for posterior inference.
Using the small-variance asymptotics of our probabilistic model, we then derive
a new deterministic clustering algorithm (RDP-means). It can be viewed as an
extension of K-means that allows for the inclusion of side information and has
the additional property that the number of clusters does not need to be
specified a priori. Empirical studies have been carried out to compare our work
with many constrained clustering algorithms from the literature on both a
variety of data sets and under a variety of conditions such as using noisy side
information and erroneous k values. The results of our experiments show strong
results for our probabilistic and deterministic approaches under these
conditions when compared to other algorithms in the literature. | [
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Many scientific datasets are of high dimension, and the analysis usually
requires visual manipulation by retaining the most important structures of
data. Principal curve is a widely used approach for this purpose. However, many
existing methods work only for data with structures that are not
self-intersected, which is quite restrictive for real applications. A few
methods can overcome the above problem, but they either require complicated
human-made rules for a specific task with lack of convergence guarantee and
adaption flexibility to different tasks, or cannot obtain explicit structures
of data. To address these issues, we develop a new regularized principal graph
learning framework that captures the local information of the underlying graph
structure based on reversed graph embedding. As showcases, models that can
learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and
a new learning algorithm is developed that learns a set of principal points and
a graph structure from data, simultaneously. The new algorithm is simple with
guaranteed convergence. We then extend the proposed framework to deal with
large-scale data. Experimental results on various synthetic and six real world
datasets show that the proposed method compares favorably with baselines and
can uncover the underlying structure correctly. | [
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] |
We consider the problem of learning preferences over trajectories for mobile
manipulators such as personal robots and assembly line robots. The preferences
we learn are more intricate than simple geometric constraints on trajectories;
they are rather governed by the surrounding context of various objects and
human interactions in the environment. We propose a coactive online learning
framework for teaching preferences in contextually rich environments. The key
novelty of our approach lies in the type of feedback expected from the user:
the human user does not need to demonstrate optimal trajectories as training
data, but merely needs to iteratively provide trajectories that slightly
improve over the trajectory currently proposed by the system. We argue that
this coactive preference feedback can be more easily elicited than
demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds
of our algorithm match the asymptotic rates of optimal trajectory algorithms.
We implement our algorithm on two high degree-of-freedom robots, PR2 and
Baxter, and present three intuitive mechanisms for providing such incremental
feedback. In our experimental evaluation we consider two context rich settings
-- household chores and grocery store checkout -- and show that users are able
to train the robot with just a few feedbacks (taking only a few
minutes).\footnote{Parts of this work has been published at NIPS and ISRR
conferences~\citep{Jain13,Jain13b}. This journal submission presents a
consistent full paper, and also includes the proof of regret bounds, more
details of the robotic system, and a thorough related work.} | [
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] |
There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation planning based on the idea that many household objects share
similarly-operated object parts. We formulate the manipulation planning as a
structured prediction problem and learn to transfer manipulation strategy
across different objects by embedding point-cloud, natural language, and
manipulation trajectory data into a shared embedding space using a deep neural
network. In order to learn semantically meaningful spaces throughout our
network, we introduce a method for pre-training its lower layers for multimodal
feature embedding and a method for fine-tuning this embedding space using a
loss-based margin. In order to collect a large number of manipulation
demonstrations for different objects, we develop a new crowd-sourcing platform
called Robobarista. We test our model on our dataset consisting of 116 objects
and appliances with 249 parts along with 250 language instructions, for which
there are 1225 crowd-sourced manipulation demonstrations. We further show that
our robot with our model can even prepare a cup of a latte with appliances it
has never seen before. | [
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0.03453970327973366,
-0.05347610265016556
] |
Machine learning algorithms are increasingly influencing our decisions and
interacting with us in all parts of our daily lives. Therefore, just like for
power plants, highways, and myriad other engineered sociotechnical systems, we
must consider the safety of systems involving machine learning. In this paper,
we first discuss the definition of safety in terms of risk, epistemic
uncertainty, and the harm incurred by unwanted outcomes. Then we examine
dimensions, such as the choice of cost function and the appropriateness of
minimizing the empirical average training cost, along which certain real-world
applications may not be completely amenable to the foundational principle of
modern statistical machine learning: empirical risk minimization. In
particular, we note an emerging dichotomy of applications: ones in which safety
is important and risk minimization is not the complete story (we name these
Type A applications), and ones in which safety is not so critical and risk
minimization is sufficient (we name these Type B applications). Finally, we
discuss how four different strategies for achieving safety in engineering
(inherently safe design, safety reserves, safe fail, and procedural safeguards)
can be mapped to the machine learning context through interpretability and
causality of predictive models, objectives beyond expected prediction accuracy,
human involvement for labeling difficult or rare examples, and user experience
design of software. | [
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] |
We address the robot grasp optimization problem of unknown objects
considering uncertainty in the input space. Grasping unknown objects can be
achieved by using a trial and error exploration strategy. Bayesian optimization
is a sample efficient optimization algorithm that is especially suitable for
this setups as it actively reduces the number of trials for learning about the
function to optimize. In fact, this active object exploration is the same
strategy that infants do to learn optimal grasps. One problem that arises while
learning grasping policies is that some configurations of grasp parameters may
be very sensitive to error in the relative pose between the object and robot
end-effector. We call these configurations unsafe because small errors during
grasp execution may turn good grasps into bad grasps. Therefore, to reduce the
risk of grasp failure, grasps should be planned in safe areas. We propose a new
algorithm, Unscented Bayesian optimization that is able to perform sample
efficient optimization while taking into consideration input noise to find safe
optima. The contribution of Unscented Bayesian optimization is twofold as if
provides a new decision process that drives exploration to safe regions and a
new selection procedure that chooses the optimal in terms of its safety without
extra analysis or computational cost. Both contributions are rooted on the
strong theory behind the unscented transformation, a popular nonlinear
approximation method. We show its advantages with respect to the classical
Bayesian optimization both in synthetic problems and in realistic robot grasp
simulations. The results highlights that our method achieves optimal and robust
grasping policies after few trials while the selected grasps remain in safe
regions. | [
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] |
Additive utility function models are widely used in multiple criteria
decision analysis. In such models, a numerical value is associated to each
alternative involved in the decision problem. It is computed by aggregating the
scores of the alternative on the different criteria of the decision problem.
The score of an alternative is determined by a marginal value function that
evolves monotonically as a function of the performance of the alternative on
this criterion. Determining the shape of the marginals is not easy for a
decision maker. It is easier for him/her to make statements such as
"alternative $a$ is preferred to $b$". In order to help the decision maker, UTA
disaggregation procedures use linear programming to approximate the marginals
by piecewise linear functions based only on such statements. In this paper, we
propose to infer polynomials and splines instead of piecewise linear functions
for the marginals. In this aim, we use semidefinite programming instead of
linear programming. We illustrate this new elicitation method and present some
experimental results. | [
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] |
In this paper, we consider the problem of actively learning a linear
classifier through query synthesis where the learner can construct artificial
queries in order to estimate the true decision boundaries. This problem has
recently gained a lot of interest in automated science and adversarial reverse
engineering for which only heuristic algorithms are known. In such
applications, queries can be constructed de novo to elicit information (e.g.,
automated science) or to evade detection with minimal cost (e.g., adversarial
reverse engineering). We develop a general framework, called dimension coupling
(DC), that 1) reduces a d-dimensional learning problem to d-1 low dimensional
sub-problems, 2) solves each sub-problem efficiently, 3) appropriately
aggregates the results and outputs a linear classifier, and 4) provides a
theoretical guarantee for all possible schemes of aggregation. The proposed
method is proved resilient to noise. We show that the DC framework avoids the
curse of dimensionality: its computational complexity scales linearly with the
dimension. Moreover, we show that the query complexity of DC is near optimal
(within a constant factor of the optimum algorithm). To further support our
theoretical analysis, we compare the performance of DC with the existing work.
We observe that DC consistently outperforms the prior arts in terms of query
complexity while often running orders of magnitude faster. | [
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] |
Humans have an impressive ability to reason about new concepts and
experiences from just a single example. In particular, humans have an ability
for one-shot generalization: an ability to encounter a new concept, understand
its structure, and then be able to generate compelling alternative variations
of the concept. We develop machine learning systems with this important
capacity by developing new deep generative models, models that combine the
representational power of deep learning with the inferential power of Bayesian
reasoning. We develop a class of sequential generative models that are built on
the principles of feedback and attention. These two characteristics lead to
generative models that are among the state-of-the art in density estimation and
image generation. We demonstrate the one-shot generalization ability of our
models using three tasks: unconditional sampling, generating new exemplars of a
given concept, and generating new exemplars of a family of concepts. In all
cases our models are able to generate compelling and diverse samples---having
seen new examples just once---providing an important class of general-purpose
models for one-shot machine learning. | [
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] |
We present a method for automatically generating repair feedback for syntax
errors for introductory programming problems. Syntax errors constitute one of
the largest classes of errors (34%) in our dataset of student submissions
obtained from a MOOC course on edX. The previous techniques for generating
automated feed- back on programming assignments have focused on functional
correctness and style considerations of student programs. These techniques
analyze the program AST of the program and then perform some dynamic and
symbolic analyses to compute repair feedback. Unfortunately, it is not possible
to generate ASTs for student pro- grams with syntax errors and therefore the
previous feedback techniques are not applicable in repairing syntax errors.
We present a technique for providing feedback on syntax errors that uses
Recurrent neural networks (RNNs) to model syntactically valid token sequences.
Our approach is inspired from the recent work on learning language models from
Big Code (large code corpus). For a given programming assignment, we first
learn an RNN to model all valid token sequences using the set of syntactically
correct student submissions. Then, for a student submission with syntax errors,
we query the learnt RNN model with the prefix to- ken sequence to predict token
sequences that can fix the error by either replacing or inserting the predicted
token sequence at the error location. We evaluate our technique on over 14, 000
student submissions with syntax errors. Our technique can completely re- pair
31.69% (4501/14203) of submissions with syntax errors and in addition partially
correct 6.39% (908/14203) of the submissions. | [
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] |
In many scientific and engineering applications, we are tasked with the
maximisation of an expensive to evaluate black box function $f$. Traditional
settings for this problem assume just the availability of this single function.
However, in many cases, cheap approximations to $f$ may be obtainable. For
example, the expensive real world behaviour of a robot can be approximated by a
cheap computer simulation. We can use these approximations to eliminate low
function value regions cheaply and use the expensive evaluations of $f$ in a
small but promising region and speedily identify the optimum. We formalise this
task as a \emph{multi-fidelity} bandit problem where the target function and
its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a
novel method based on upper confidence bound techniques. In our theoretical
analysis we demonstrate that it exhibits precisely the above behaviour, and
achieves better regret than strategies which ignore multi-fidelity information.
Empirically, MF-GP-UCB outperforms such naive strategies and other
multi-fidelity methods on several synthetic and real experiments. | [
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] |
While probability theory is normally applied to external environments, there
has been some recent interest in probabilistic modeling of the outputs of
computations that are too expensive to run. Since mathematical logic is a
powerful tool for reasoning about computer programs, we consider this problem
from the perspective of integrating probability and logic. Recent work on
assigning probabilities to mathematical statements has used the concept of
coherent distributions, which satisfy logical constraints such as the
probability of a sentence and its negation summing to one. Although there are
algorithms which converge to a coherent probability distribution in the limit,
this yields only weak guarantees about finite approximations of these
distributions. In our setting, this is a significant limitation: Coherent
distributions assign probability one to all statements provable in a specific
logical theory, such as Peano Arithmetic, which can prove what the output of
any terminating computation is; thus, a coherent distribution must assign
probability one to the output of any terminating computation. To model
uncertainty about computations, we propose to work with approximations to
coherent distributions. We introduce inductive coherence, a strengthening of
coherence that provides appropriate constraints on finite approximations, and
propose an algorithm which satisfies this criterion. | [
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The estimation of class prevalence, i.e., the fraction of a population that
belongs to a certain class, is a very useful tool in data analytics and
learning, and finds applications in many domains such as sentiment analysis,
epidemiology, etc. For example, in sentiment analysis, the objective is often
not to estimate whether a specific text conveys a positive or a negative
sentiment, but rather estimate the overall distribution of positive and
negative sentiments during an event window. A popular way of performing the
above task, often dubbed quantification, is to use supervised learning to train
a prevalence estimator from labeled data.
Contemporary literature cites several performance measures used to measure
the success of such prevalence estimators. In this paper we propose the first
online stochastic algorithms for directly optimizing these
quantification-specific performance measures. We also provide algorithms that
optimize hybrid performance measures that seek to balance quantification and
classification performance. Our algorithms present a significant advancement in
the theory of multivariate optimization and we show, by a rigorous theoretical
analysis, that they exhibit optimal convergence. We also report extensive
experiments on benchmark and real data sets which demonstrate that our methods
significantly outperform existing optimization techniques used for these
performance measures. | [
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] |
Learning the true ordering between objects by aggregating a set of expert
opinion rank order lists is an important and ubiquitous problem in many
applications ranging from social choice theory to natural language processing
and search aggregation. We study the problem of unsupervised rank aggregation
where no ground truth ordering information in available, neither about the true
preference ordering between any set of objects nor about the quality of
individual rank lists. Aggregating the often inconsistent and poor quality rank
lists in such an unsupervised manner is a highly challenging problem, and
standard consensus-based methods are often ill-defined, and difficult to solve.
In this manuscript we propose a novel framework to bypass these issues by using
object attributes to augment the standard rank aggregation framework. We design
algorithms that learn joint models on both rank lists and object features to
obtain an aggregated rank ordering that is more accurate and robust, and also
helps weed out rank lists of dubious validity. We validate our techniques on
synthetic datasets where our algorithm is able to estimate the true rank
ordering even when the rank lists are corrupted. Experiments on three real
datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can
result in significant improvement in performance over existing rank aggregation
methods that do not use object information. Furthermore, when at least some of
the rank lists are of high quality, our methods are able to effectively exploit
their high expertise to output an aggregated rank ordering of great accuracy. | [
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] |
Databases in domains such as healthcare are routinely released to the public
in aggregated form. Unfortunately, naive modeling with aggregated data may
significantly diminish the accuracy of inferences at the individual level. This
paper addresses the scenario where features are provided at the individual
level, but the target variables are only available as histogram aggregates or
order statistics. We consider a limiting case of generalized linear modeling
when the target variables are only known up to permutation, and explore how
this relates to permutation testing; a standard technique for assessing
statistical dependency. Based on this relationship, we propose a simple
algorithm to estimate the model parameters and individual level inferences via
alternating imputation and standard generalized linear model fitting. Our
results suggest the effectiveness of the proposed approach when, in the
original data, permutation testing accurately ascertains the veracity of the
linear relationship. The framework is extended to general histogram data with
larger bins - with order statistics such as the median as a limiting case. Our
experimental results on simulated data and aggregated healthcare data suggest a
diminishing returns property with respect to the granularity of the histogram -
when a linear relationship holds in the original data, the targets can be
predicted accurately given relatively coarse histograms. | [
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] |
Large labeled training sets are the critical building blocks of supervised
learning methods and are key enablers of deep learning techniques. For some
applications, creating labeled training sets is the most time-consuming and
expensive part of applying machine learning. We therefore propose a paradigm
for the programmatic creation of training sets called data programming in which
users express weak supervision strategies or domain heuristics as labeling
functions, which are programs that label subsets of the data, but that are
noisy and may conflict. We show that by explicitly representing this training
set labeling process as a generative model, we can "denoise" the generated
training set, and establish theoretically that we can recover the parameters of
these generative models in a handful of settings. We then show how to modify a
discriminative loss function to make it noise-aware, and demonstrate our method
over a range of discriminative models including logistic regression and LSTMs.
Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data
programming would have led to a new winning score, and also show that applying
data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points
over a state-of-the-art LSTM baseline (and into second place in the
competition). Additionally, in initial user studies we observed that data
programming may be an easier way for non-experts to create machine learning
models when training data is limited or unavailable. | [
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] |
Bayesian optimization has become a fundamental global optimization algorithm
in many problems where sample efficiency is of paramount importance. Recently,
there has been proposed a large number of new applications in fields such as
robotics, machine learning, experimental design, simulation, etc. In this
paper, we focus on several problems that appear in robotics and autonomous
systems: algorithm tuning, automatic control and intelligent design. All those
problems can be mapped to global optimization problems. However, they become
hard optimization problems. Bayesian optimization internally uses a
probabilistic surrogate model (e.g.: Gaussian process) to learn from the
process and reduce the number of samples required. In order to generalize to
unknown functions in a black-box fashion, the common assumption is that the
underlying function can be modeled with a stationary process. Nonstationary
Gaussian process regression cannot generalize easily and it typically requires
prior knowledge of the function. Some works have designed techniques to
generalize Bayesian optimization to nonstationary functions in an indirect way,
but using techniques originally designed for regression, where the objective is
to improve the quality of the surrogate model everywhere. Instead optimization
should focus on improving the surrogate model near the optimum. In this paper,
we present a novel kernel function specially designed for Bayesian
optimization, that allows nonstationary behavior of the surrogate model in an
adaptive local region. In our experiments, we found that this new kernel
results in an improved local search (exploitation), without penalizing the
global search (exploration). We provide results in well-known benchmarks and
real applications. The new method outperforms the state of the art in Bayesian
optimization both in stationary and nonstationary problems. | [
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] |
Reinforcement learning holds the promise of enabling autonomous robots to
learn large repertoires of behavioral skills with minimal human intervention.
However, robotic applications of reinforcement learning often compromise the
autonomy of the learning process in favor of achieving training times that are
practical for real physical systems. This typically involves introducing
hand-engineered policy representations and human-supplied demonstrations. Deep
reinforcement learning alleviates this limitation by training general-purpose
neural network policies, but applications of direct deep reinforcement learning
algorithms have so far been restricted to simulated settings and relatively
simple tasks, due to their apparent high sample complexity. In this paper, we
demonstrate that a recent deep reinforcement learning algorithm based on
off-policy training of deep Q-functions can scale to complex 3D manipulation
tasks and can learn deep neural network policies efficiently enough to train on
real physical robots. We demonstrate that the training times can be further
reduced by parallelizing the algorithm across multiple robots which pool their
policy updates asynchronously. Our experimental evaluation shows that our
method can learn a variety of 3D manipulation skills in simulation and a
complex door opening skill on real robots without any prior demonstrations or
manually designed representations. | [
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] |
This research introduces a new strategy in cluster ensemble selection by
using Independency and Diversity metrics. In recent years, Diversity and
Quality, which are two metrics in evaluation procedure, have been used for
selecting basic clustering results in the cluster ensemble selection. Although
quality can improve the final results in cluster ensemble, it cannot control
the procedures of generating basic results, which causes a gap in prediction of
the generated basic results' accuracy. Instead of quality, this paper
introduces Independency as a supplementary method to be used in conjunction
with Diversity. Therefore, this paper uses a heuristic metric, which is based
on the procedure of converting code to graph in Software Testing, in order to
calculate the Independency of two basic clustering algorithms. Moreover, a new
modeling language, which we called as "Clustering Algorithms Independency
Language" (CAIL), is introduced in order to generate graphs which depict
Independency of algorithms. Also, Uniformity, which is a new similarity metric,
has been introduced for evaluating the diversity of basic results. As a
credential, our experimental results on varied different standard data sets
show that the proposed framework improves the accuracy of final results
dramatically in comparison with other cluster ensemble methods. | [
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] |
Autonomous driving is a multi-agent setting where the host vehicle must apply
sophisticated negotiation skills with other road users when overtaking, giving
way, merging, taking left and right turns and while pushing ahead in
unstructured urban roadways. Since there are many possible scenarios, manually
tackling all possible cases will likely yield a too simplistic policy.
Moreover, one must balance between unexpected behavior of other
drivers/pedestrians and at the same time not to be too defensive so that normal
traffic flow is maintained.
In this paper we apply deep reinforcement learning to the problem of forming
long term driving strategies. We note that there are two major challenges that
make autonomous driving different from other robotic tasks. First, is the
necessity for ensuring functional safety - something that machine learning has
difficulty with given that performance is optimized at the level of an
expectation over many instances. Second, the Markov Decision Process model
often used in robotics is problematic in our case because of unpredictable
behavior of other agents in this multi-agent scenario. We make three
contributions in our work. First, we show how policy gradient iterations can be
used without Markovian assumptions. Second, we decompose the problem into a
composition of a Policy for Desires (which is to be learned) and trajectory
planning with hard constraints (which is not learned). The goal of Desires is
to enable comfort of driving, while hard constraints guarantees the safety of
driving. Third, we introduce a hierarchical temporal abstraction we call an
"Option Graph" with a gating mechanism that significantly reduces the effective
horizon and thereby reducing the variance of the gradient estimation even
further. | [
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] |
Developing control policies in simulation is often more practical and safer
than directly running experiments in the real world. This applies to policies
obtained from planning and optimization, and even more so to policies obtained
from reinforcement learning, which is often very data demanding. However, a
policy that succeeds in simulation often doesn't work when deployed on a real
robot. Nevertheless, often the overall gist of what the policy does in
simulation remains valid in the real world. In this paper we investigate such
settings, where the sequence of states traversed in simulation remains
reasonable for the real world, even if the details of the controls are not, as
could be the case when the key differences lie in detailed friction, contact,
mass and geometry properties. During execution, at each time step our approach
computes what the simulation-based control policy would do, but then, rather
than executing these controls on the real robot, our approach computes what the
simulation expects the resulting next state(s) will be, and then relies on a
learned deep inverse dynamics model to decide which real-world action is most
suitable to achieve those next states. Deep models are only as good as their
training data, and we also propose an approach for data collection to
(incrementally) learn the deep inverse dynamics model. Our experiments shows
our approach compares favorably with various baselines that have been developed
for dealing with simulation to real world model discrepancy, including output
error control and Gaussian dynamics adaptation. | [
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] |
Deep neural networks have achieved impressive experimental results in image
classification, but can surprisingly be unstable with respect to adversarial
perturbations, that is, minimal changes to the input image that cause the
network to misclassify it. With potential applications including perception
modules and end-to-end controllers for self-driving cars, this raises concerns
about their safety. We develop a novel automated verification framework for
feed-forward multi-layer neural networks based on Satisfiability Modulo Theory
(SMT). We focus on safety of image classification decisions with respect to
image manipulations, such as scratches or changes to camera angle or lighting
conditions that would result in the same class being assigned by a human, and
define safety for an individual decision in terms of invariance of the
classification within a small neighbourhood of the original image. We enable
exhaustive search of the region by employing discretisation, and propagate the
analysis layer by layer. Our method works directly with the network code and,
in contrast to existing methods, can guarantee that adversarial examples, if
they exist, are found for the given region and family of manipulations. If
found, adversarial examples can be shown to human testers and/or used to
fine-tune the network. We implement the techniques using Z3 and evaluate them
on state-of-the-art networks, including regularised and deep learning networks.
We also compare against existing techniques to search for adversarial examples
and estimate network robustness. | [
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] |
Reinforcement learning (RL) in Markov decision processes (MDPs) with large
state spaces is a challenging problem. The performance of standard RL
algorithms degrades drastically with the dimensionality of state space.
However, in practice, these large MDPs typically incorporate a latent or hidden
low-dimensional structure. In this paper, we study the setting of
rich-observation Markov decision processes (ROMDP), where there are a small
number of hidden states which possess an injective mapping to the observation
states. In other words, every observation state is generated through a single
hidden state, and this mapping is unknown a priori. We introduce a spectral
decomposition method that consistently learns this mapping, and more
importantly, achieves it with low regret. The estimated mapping is integrated
into an optimistic RL algorithm (UCRL), which operates on the estimated hidden
space. We derive finite-time regret bounds for our algorithm with a weak
dependence on the dimensionality of the observed space. In fact, our algorithm
asymptotically achieves the same average regret as the oracle UCRL algorithm,
which has the knowledge of the mapping from hidden to observed spaces. Thus, we
derive an efficient spectral RL algorithm for ROMDPs. | [
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] |
Max-cut, clustering, and many other partitioning problems that are of
significant importance to machine learning and other scientific fields are
NP-hard, a reality that has motivated researchers to develop a wealth of
approximation algorithms and heuristics. Although the best algorithm to use
typically depends on the specific application domain, a worst-case analysis is
often used to compare algorithms. This may be misleading if worst-case
instances occur infrequently, and thus there is a demand for optimization
methods which return the algorithm configuration best suited for the given
application's typical inputs. We address this problem for clustering, max-cut,
and other partitioning problems, such as integer quadratic programming, by
designing computationally efficient and sample efficient learning algorithms
which receive samples from an application-specific distribution over problem
instances and learn a partitioning algorithm with high expected performance.
Our algorithms learn over common integer quadratic programming and clustering
algorithm families: SDP rounding algorithms and agglomerative clustering
algorithms with dynamic programming. For our sample complexity analysis, we
provide tight bounds on the pseudodimension of these algorithm classes, and
show that surprisingly, even for classes of algorithms parameterized by a
single parameter, the pseudo-dimension is superconstant. In this way, our work
both contributes to the foundations of algorithm configuration and pushes the
boundaries of learning theory, since the algorithm classes we analyze consist
of multi-stage optimization procedures and are significantly more complex than
classes typically studied in learning theory. | [
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Credit card plays a very important rule in today's economy. It becomes an
unavoidable part of household, business and global activities. Although using
credit cards provides enormous benefits when used carefully and
responsibly,significant credit and financial damages may be caused by
fraudulent activities. Many techniques have been proposed to confront the
growth in credit card fraud. However, all of these techniques have the same
goal of avoiding the credit card fraud; each one has its own drawbacks,
advantages and characteristics. In this paper, after investigating difficulties
of credit card fraud detection, we seek to review the state of the art in
credit card fraud detection techniques, data sets and evaluation criteria.The
advantages and disadvantages of fraud detection methods are enumerated and
compared.Furthermore, a classification of mentioned techniques into two main
fraud detection approaches, namely, misuses (supervised) and anomaly detection
(unsupervised) is presented. Again, a classification of techniques is proposed
based on capability to process the numerical and categorical data sets.
Different data sets used in literature are then described and grouped into real
and synthesized data and the effective and common attributes are extracted for
further usage.Moreover, evaluation employed criterions in literature are
collected and discussed.Consequently, open issues for credit card fraud
detection are explained as guidelines for new researchers. | [
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] |
Reinforcement learning is concerned with identifying reward-maximizing
behaviour policies in environments that are initially unknown. State-of-the-art
reinforcement learning approaches, such as deep Q-networks, are model-free and
learn to act effectively across a wide range of environments such as Atari
games, but require huge amounts of data. Model-based techniques are more
data-efficient, but need to acquire explicit knowledge about the environment.
In this paper, we take a step towards using model-based techniques in
environments with a high-dimensional visual state space by demonstrating that
it is possible to learn system dynamics and the reward structure jointly. Our
contribution is to extend a recently developed deep neural network for video
frame prediction in Atari games to enable reward prediction as well. To this
end, we phrase a joint optimization problem for minimizing both video frame and
reward reconstruction loss, and adapt network parameters accordingly. Empirical
evaluations on five Atari games demonstrate accurate cumulative reward
prediction of up to 200 frames. We consider these results as opening up
important directions for model-based reinforcement learning in complex,
initially unknown environments. | [
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] |
Mobile robots are increasingly being employed for performing complex tasks in
dynamic environments. Reinforcement learning (RL) methods are recognized to be
promising for specifying such tasks in a relatively simple manner. However, the
strong dependency between the learning method and the task to learn is a
well-known problem that restricts practical implementations of RL in robotics,
often requiring major modifications of parameters and adding other techniques
for each particular task. In this paper we present a practical core
implementation of RL which enables the learning process for multiple robotic
tasks with minimal per-task tuning or none. Based on value iteration methods,
this implementation includes a novel approach for action selection, called
Q-biased softmax regression (QBIASSR), which avoids poor performance of the
learning process when the robot reaches new unexplored states. Our approach
takes advantage of the structure of the state space by attending the physical
variables involved (e.g., distances to obstacles, X,Y,{\theta} pose, etc.),
thus experienced sets of states may favor the decision-making process of
unexplored or rarely-explored states. This improvement has a relevant role in
reducing the tuning of the algorithm for particular tasks. Experiments with
real and simulated robots, performed with the software framework also
introduced here, show that our implementation is effectively able to learn
different robotic tasks without tuning the learning method. Results also
suggest that the combination of true online SARSA({\lambda}) with QBIASSR can
outperform the existing RL core algorithms in low-dimensional robotic tasks. | [
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In statistical relational learning, knowledge graph completion deals with
automatically understanding the structure of large knowledge graphs---labeled
directed graphs---and predicting missing relationships---labeled edges.
State-of-the-art embedding models propose different trade-offs between modeling
expressiveness, and time and space complexity. We reconcile both expressiveness
and complexity through the use of complex-valued embeddings and explore the
link between such complex-valued embeddings and unitary diagonalization. We
corroborate our approach theoretically and show that all real square
matrices---thus all possible relation/adjacency matrices---are the real part of
some unitarily diagonalizable matrix. This results opens the door to a lot of
other applications of square matrices factorization. Our approach based on
complex embeddings is arguably simple, as it only involves a Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors, whereas other methods resort to more and more complicated composition
functions to increase their expressiveness. The proposed complex embeddings are
scalable to large data sets as it remains linear in both space and time, while
consistently outperforming alternative approaches on standard link prediction
benchmarks. | [
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] |
Classifiers deployed in the real world operate in a dynamic environment,
where the data distribution can change over time. These changes, referred to as
concept drift, can cause the predictive performance of the classifier to drop
over time, thereby making it obsolete. To be of any real use, these classifiers
need to detect drifts and be able to adapt to them, over time. Detecting drifts
has traditionally been approached as a supervised task, with labeled data
constantly being used for validating the learned model. Although effective in
detecting drifts, these techniques are impractical, as labeling is a difficult,
costly and time consuming activity. On the other hand, unsupervised change
detection techniques are unreliable, as they produce a large number of false
alarms. The inefficacy of the unsupervised techniques stems from the exclusion
of the characteristics of the learned classifier, from the detection process.
In this paper, we propose the Margin Density Drift Detection (MD3) algorithm,
which tracks the number of samples in the uncertainty region of a classifier,
as a metric to detect drift. The MD3 algorithm is a distribution independent,
application independent, model independent, unsupervised and incremental
algorithm for reliably detecting drifts from data streams. Experimental
evaluation on 6 drift induced datasets and 4 additional datasets from the
cybersecurity domain demonstrates that the MD3 approach can reliably detect
drifts, with significantly fewer false alarms compared to unsupervised feature
based drift detectors. The reduced false alarms enables the signaling of drifts
only when they are most likely to affect classification performance. As such,
the MD3 approach leads to a detection scheme which is credible, label efficient
and general in its applicability. | [
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] |
Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of downstream values for states in a decision tree. This information
is used to iteratively expand the tree towards regions of states and actions
that an optimal policy might visit. However, to guarantee convergence to the
optimal action, MCTS requires the entire tree to be expanded asymptotically. In
this paper, we propose a new technique called Primal-Dual MCTS that utilizes
sampled information relaxation upper bounds on potential actions, creating the
possibility of "ignoring" parts of the tree that stem from highly suboptimal
choices. This allows us to prove that despite converging to a partial decision
tree in the limit, the recommended action from Primal-Dual MCTS is optimal. The
new approach shows significant promise when used to optimize the behavior of a
single driver navigating a graph while operating on a ride-sharing platform.
Numerical experiments on a real dataset of 7,000 trips in New Jersey suggest
that Primal-Dual MCTS improves upon standard MCTS by producing deeper decision
trees and exhibits a reduced sensitivity to the size of the action space. | [
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One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members. | [
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] |
Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods. | [
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] |
E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
billions of products. Machine learning (ML) algorithms involving products are
often used to improve the customer experience and increase revenue, e.g.,
product similarity, recommendation, and price estimation. The products are
required to be represented as features before training an ML algorithm. In this
paper, we propose an approach called MRNet-Product2Vec for creating generic
embeddings of products within an e-commerce ecosystem. We learn a dense and
low-dimensional embedding where a diverse set of signals related to a product
are explicitly injected into its representation. We train a Discriminative
Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a
product title fed through a Bidirectional RNN and at the output, product labels
corresponding to fifteen different tasks are predicted. The task set includes
several intrinsic characteristics about a product such as price, weight, size,
color, popularity, and material. We evaluate the proposed embedding
quantitatively and qualitatively. We demonstrate that they are almost as good
as sparse and extremely high-dimensional TF-IDF representation in spite of
having less than 3% of the TF-IDF dimension. We also use a multimodal
autoencoder for comparing products from different language-regions and show
preliminary yet promising qualitative results. | [
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] |
We present a method for efficient learning of control policies for multiple
related robotic motor skills. Our approach consists of two stages, joint
training and specialization training. During the joint training stage, a neural
network policy is trained with minimal information to disambiguate the motor
skills. This forces the policy to learn a common representation of the
different tasks. Then, during the specialization training stage we selectively
split the weights of the policy based on a per-weight metric that measures the
disagreement among the multiple tasks. By splitting part of the control policy,
it can be further trained to specialize to each task. To update the control
policy during learning, we use Trust Region Policy Optimization with
Generalized Advantage Function (TRPOGAE). We propose a modification to the
gradient update stage of TRPO to better accommodate multi-task learning
scenarios. We evaluate our approach on three continuous motor skill learning
problems in simulation: 1) a locomotion task where three single legged robots
with considerable difference in shape and size are trained to hop forward, 2) a
manipulation task where three robot manipulators with different sizes and joint
types are trained to reach different locations in 3D space, and 3) locomotion
of a two-legged robot, whose range of motion of one leg is constrained in
different ways. We compare our training method to three baselines. The first
baseline uses only joint training for the policy, the second trains independent
policies for each task, and the last randomly selects weights to split. We show
that our approach learns more efficiently than each of the baseline methods. | [
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] |
We present a robust multi-robot convoying approach that relies on visual
detection of the leading agent, thus enabling target following in unstructured
3-D environments. Our method is based on the idea of tracking-by-detection,
which interleaves efficient model-based object detection with temporal
filtering of image-based bounding box estimation. This approach has the
important advantage of mitigating tracking drift (i.e. drifting away from the
target object), which is a common symptom of model-free trackers and is
detrimental to sustained convoying in practice. To illustrate our solution, we
collected extensive footage of an underwater robot in ocean settings, and
hand-annotated its location in each frame. Based on this dataset, we present an
empirical comparison of multiple tracker variants, including the use of several
convolutional neural networks, both with and without recurrent connections, as
well as frequency-based model-free trackers. We also demonstrate the
practicality of this tracking-by-detection strategy in real-world scenarios by
successfully controlling a legged underwater robot in five degrees of freedom
to follow another robot's independent motion. | [
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In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems. | [
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] |
This paper proposes a novel latent semantic learning method for extracting
high-level features (i.e. latent semantics) from a large vocabulary of abundant
mid-level features (i.e. visual keywords) with structured sparse
representation, which can help to bridge the semantic gap in the challenging
task of human action recognition. To discover the manifold structure of
midlevel features, we develop a spectral embedding approach to latent semantic
learning based on L1-graph, without the need to tune any parameter for graph
construction as a key step of manifold learning. More importantly, we construct
the L1-graph with structured sparse representation, which can be obtained by
structured sparse coding with its structured sparsity ensured by novel L1-norm
hypergraph regularization over mid-level features. In the new embedding space,
we learn latent semantics automatically from abundant mid-level features
through spectral clustering. The learnt latent semantics can be readily used
for human action recognition with SVM by defining a histogram intersection
kernel. Different from the traditional latent semantic analysis based on topic
models, our latent semantic learning method can explore the manifold structure
of mid-level features in both L1-graph construction and spectral embedding,
which results in compact but discriminative high-level features. The
experimental results on the commonly used KTH action dataset and unconstrained
YouTube action dataset show the superior performance of our method. | [
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] |
It is a high-quality algorithm for hierarchical clustering of large software
source code. This effectively allows to break the complexity of tens of
millions lines of source code, so that a human software engineer can comprehend
a software system at high level by means of looking at its architectural
diagram that is reconstructed automatically from the source code of the
software system. The architectural diagram shows a tree of subsystems having
OOP classes in its leaves (in the other words, a nested software
decomposition). The tool reconstructs the missing
(inconsistent/incomplete/inexistent) architectural documentation for a software
system from its source code. This facilitates software maintenance: change
requests can be performed substantially faster. Simply speaking, this unique
tool allows to lift the comprehensible grain of object-oriented software
systems from OOP class-level to subsystem-level. It is estimated that a
commercial tool, developed on the basis of this work, will reduce software
maintenance expenses 10 times on the current needs, and will allow to implement
next-generation software systems which are currently too complex to be within
the range of human comprehension, therefore can't yet be designed or
implemented. Implemented prototype in Open Source:
http://sourceforge.net/p/insoar/code-0/1/tree/ | [
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] |
Finding the most probable assignment (MAP) in a general graphical model is
known to be NP hard but good approximations have been attained with max-product
belief propagation (BP) and its variants. In particular, it is known that using
BP on a single-cycle graph or tree reweighted BP on an arbitrary graph will
give the MAP solution if the beliefs have no ties. In this paper we extend the
setting under which BP can be used to provably extract the MAP. We define
Convex BP as BP algorithms based on a convex free energy approximation and show
that this class includes ordinary BP with single-cycle, tree reweighted BP and
many other BP variants. We show that when there are no ties, fixed-points of
convex max-product BP will provably give the MAP solution. We also show that
convex sum-product BP at sufficiently small temperatures can be used to solve
linear programs that arise from relaxing the MAP problem. Finally, we derive a
novel condition that allows us to derive the MAP solution even if some of the
convex BP beliefs have ties. In experiments, we show that our theorems allow us
to find the MAP in many real-world instances of graphical models where exact
inference using junction-tree is impossible. | [
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] |
In many cases it makes sense to model a relationship symmetrically, not
implying any particular directionality. Consider the classical example of a
recommendation system where the rating of an item by a user should
symmetrically be dependent on the attributes of both the user and the item. The
attributes of the (known) relationships are also relevant for predicting
attributes of entities and for predicting attributes of new relations. In
recommendation systems, the exploitation of relational attributes is often
referred to as collaborative filtering. Again, in many applications one might
prefer to model the collaborative effect in a symmetrical way. In this paper we
present a relational model, which is completely symmetrical. The key innovation
is that we introduce for each entity (or object) an infinite-dimensional latent
variable as part of a Dirichlet process (DP) model. We discuss inference in the
model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant
process. We extend the Chinese restaurant process to be applicable to
relational modeling. Our approach is evaluated in three applications. One is a
recommendation system based on the MovieLens data set. The second application
concerns the prediction of the function of yeast genes/proteins on the data set
of KDD Cup 2001 using a multi-relational model. The third application involves
a relational medical domain. The experimental results show that our model gives
significantly improved estimates of attributes describing relationships or
entities in complex relational models. | [
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] |
A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data. | [
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] |
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP)
interpretation. In particular, we present a constraint based algorithm for
learning an AMP chain graph a given probability distribution is faithful to.
Moreover, we show that the extension of Meek's conjecture to AMP chain graphs
does not hold, which compromises the development of efficient and correct
score+search learning algorithms under assumptions weaker than faithfulness.
We also introduce a new family of graphical models that consists of
undirected and bidirected edges. We name this new family maximal
covariance-concentration graphs (MCCGs) because it includes both covariance and
concentration graphs as subfamilies. However, every MCCG can be seen as the
result of marginalizing out some nodes in an AMP CG. We describe global, local
and pairwise Markov properties for MCCGs and prove their equivalence. We
characterize when two MCCGs are Markov equivalent, and show that every Markov
equivalence class of MCCGs has a distinguished member. We present a constraint
based algorithm for learning a MCCG a given probability distribution is
faithful to.
Finally, we present a graphical criterion for reading dependencies from a
MCCG of a probability distribution that satisfies the graphoid properties, weak
transitivity and composition. We prove that the criterion is sound and complete
in certain sense. | [
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] |
Bayesian Reinforcement Learning (RL) is capable of not only incorporating
domain knowledge, but also solving the exploration-exploitation dilemma in a
natural way. As Bayesian RL is intractable except for special cases, previous
work has proposed several approximation methods. However, these methods are
usually too sensitive to parameter values, and finding an acceptable parameter
setting is practically impossible in many applications. In this paper, we
propose a new algorithm that greedily approximates Bayesian RL to achieve
robustness in parameter space. We show that for a desired learning behavior,
our proposed algorithm has a polynomial sample complexity that is lower than
those of existing algorithms. We also demonstrate that the proposed algorithm
naturally outperforms other existing algorithms when the prior distributions
are not significantly misleading. On the other hand, the proposed algorithm
cannot handle greatly misspecified priors as well as the other algorithms can.
This is a natural consequence of the fact that the proposed algorithm is
greedier than the other algorithms. Accordingly, we discuss a way to select an
appropriate algorithm for different tasks based on the algorithms' greediness.
We also introduce a new way of simplifying Bayesian planning, based on which
future work would be able to derive new algorithms. | [
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] |
Learning the Markov network structure from data is a problem that has
received considerable attention in machine learning, and in many other
application fields. This work focuses on a particular approach for this purpose
called independence-based learning. Such approach guarantees the learning of
the correct structure efficiently, whenever data is sufficient for representing
the underlying distribution. However, an important issue of such approach is
that the learned structures are encoded in an undirected graph. The problem
with graphs is that they cannot encode some types of independence relations,
such as the context-specific independences. They are a particular case of
conditional independences that is true only for a certain assignment of its
conditioning set, in contrast to conditional independences that must hold for
all its assignments. In this work we present CSPC, an independence-based
algorithm for learning structures that encode context-specific independences,
and encoding them in a log-linear model, instead of a graph. The central idea
of CSPC is combining the theoretical guarantees provided by the
independence-based approach with the benefits of representing complex
structures by using features in a log-linear model. We present experiments in a
synthetic case, showing that CSPC is more accurate than the state-of-the-art IB
algorithms when the underlying distribution contains CSIs. | [
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Time-series classification has attracted considerable research attention due
to the various domains where time-series data are observed, ranging from
medicine to econometrics. Traditionally, the focus of time-series
classification has been on short time-series data composed of a unique pattern
with intraclass pattern distortions and variations, while recently there have
been attempts to focus on longer series composed of various local patterns.
This study presents a novel method which can detect local patterns in long
time-series via fitting local polynomial functions of arbitrary degrees. The
coefficients of the polynomial functions are converted to symbolic words via
equivolume discretizations of the coefficients' distributions. The symbolic
polynomial words enable the detection of similar local patterns by assigning
the same words to similar polynomials. Moreover, a histogram of the frequencies
of the words is constructed from each time-series' bag of words. Each row of
the histogram enables a new representation for the series and symbolize the
existence of local patterns and their frequencies. Experimental evidence
demonstrates outstanding results of our method compared to the state-of-art
baselines, by exhibiting the best classification accuracies in all the datasets
and having statistically significant improvements in the absolute majority of
experiments. | [
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] |
Knowledge Representation and Reasoning and Machine Learning are two important
fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming
(ASP) provide formal languages for representing and reasoning with commonsense
knowledge and realize declarative problem solving in AI. On the other side,
Inductive Logic Programming (ILP) realizes Machine Learning in logic
programming, which provides a formal background to inductive learning and the
techniques have been applied to the fields of relational learning and data
mining. Generally speaking, NMLP and ASP realize nonmonotonic reasoning while
lack the ability of learning. By contrast, ILP realizes inductive learning
while most techniques have been developed under the classical monotonic logic.
With this background, some researchers attempt to combine techniques in the
context of nonmonotonic ILP. Such combination will introduce a learning
mechanism to programs and would exploit new applications on the NMLP side,
while on the ILP side it will extend the representation language and enable us
to use existing solvers. Cross-fertilization between learning and nonmonotonic
reasoning can also occur in such as the use of answer set solvers for ILP,
speed-up learning while running answer set solvers, learning action theories,
learning transition rules in dynamical systems, abductive learning, learning
biological networks with inhibition, and applications involving default and
negation. This workshop is the first attempt to provide an open forum for the
identification of problems and discussion of possible collaborations among
researchers with complementary expertise. The workshop was held on September
15th of 2013 in Corunna, Spain. This post-proceedings contains five technical
papers (out of six accepted papers) and the abstract of the invited talk by Luc
De Raedt. | [
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] |
There are plenty of problems where the data available is scarce and
expensive. We propose a generator of semi-artificial data with similar
properties to the original data which enables development and testing of
different data mining algorithms and optimization of their parameters. The
generated data allow a large scale experimentation and simulations without
danger of overfitting. The proposed generator is based on RBF networks, which
learn sets of Gaussian kernels. These Gaussian kernels can be used in a
generative mode to generate new data from the same distributions. To assess
quality of the generated data we evaluated the statistical properties of the
generated data, structural similarity and predictive similarity using
supervised and unsupervised learning techniques. To determine usability of the
proposed generator we conducted a large scale evaluation using 51 UCI data
sets. The results show a considerable similarity between the original and
generated data and indicate that the method can be useful in several
development and simulation scenarios. We analyze possible improvements in
classification performance by adding different amounts of generated data to the
training set, performance on high dimensional data sets, and conditions when
the proposed approach is successful. | [
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] |
Syndromic surveillance systems continuously monitor multiple pre-diagnostic
daily streams of indicators from different regions with the aim of early
detection of disease outbreaks. The main objective of these systems is to
detect outbreaks hours or days before the clinical and laboratory confirmation.
The type of data that is being generated via these systems is usually
multivariate and seasonal with spatial and temporal dimensions. The algorithm
What's Strange About Recent Events (WSARE) is the state-of-the-art method for
such problems. It exhaustively searches for contrast sets in the multivariate
data and signals an alarm when find statistically significant rules. This
bottom-up approach presents a much lower detection delay comparing the existing
top-down approaches. However, WSARE is very sensitive to the small-scale
changes and subsequently comes with a relatively high rate of false alarms. We
propose a new approach called EigenEvent that is neither fully top-down nor
bottom-up. In this method, we instead of top-down or bottom-up search, track
changes in data correlation structure via eigenspace techniques. This new
methodology enables us to detect both overall changes (via eigenvalue) and
dimension-level changes (via eigenvectors). Experimental results on hundred
sets of benchmark data reveals that EigenEvent presents a better overall
performance comparing state-of-the-art, in particular in terms of the false
alarm rate. | [
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] |
Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
$O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the
Bayesian network is $n$ and the in-degree (the number of parents) per node is
bounded by a constant $d$. Here we present a parallel algorithm capable of
computing the exact posterior probabilities for all $n(n-1)$ edges with optimal
parallel space efficiency and nearly optimal parallel time efficiency. That is,
if $p=2^k$ processors are used, the run-time reduces to
$O(5(d+1)n2^{n-k}+k(n-k)^d)$ and the space usage becomes $O(n2^{n-k})$ per
processor. Our algorithm is based the observation that the subproblems in the
sequential DP algorithm constitute a $n$-$D$ hypercube. We take a delicate way
to coordinate the computation of correlated DP procedures such that large
amount of data exchange is suppressed. Further, we develop parallel techniques
for two variants of the well-known \emph{zeta transform}, which have
applications outside the context of Bayesian networks. We demonstrate the
capability of our algorithm on datasets with up to 33 variables and its
scalability on up to 2048 processors. We apply our algorithm to a biological
data set for discovering the yeast pheromone response pathways. | [
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] |
Many people suffer from the loss of a limb. Learning to get by without an arm
or hand can be very challenging, and existing prostheses do not yet fulfil the
needs of individuals with amputations. One promising solution is to provide
greater communication between a prosthesis and its user. Towards this end, we
present a simple machine learning interface to supplement the control of a
robotic limb with feedback to the user about what the limb will be experiencing
in the near future. A real-time prediction learner was implemented to predict
impact-related electrical load experienced by a robot limb; the learning
system's predictions were then communicated to the device's user to aid in
their interactions with a workspace. We tested this system with five
able-bodied subjects. Each subject manipulated the robot arm while receiving
different forms of vibrotactile feedback regarding the arm's contact with its
workspace. Our trials showed that communicable predictions could be learned
quickly during human control of the robot arm. Using these predictions as a
basis for feedback led to a statistically significant improvement in task
performance when compared to purely reactive feedback from the device. Our
study therefore contributes initial evidence that prediction learning and
machine intelligence can benefit not just control, but also feedback from an
artificial limb. We expect that a greater level of acceptance and ownership can
be achieved if the prosthesis itself takes an active role in transmitting
learned knowledge about its state and its situation of use. | [
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The automatic design of controllers for mobile robots usually requires two
stages. In the first stage,sensorial data are preprocessed or transformed into
high level and meaningful values of variables whichare usually defined from
expert knowledge. In the second stage, a machine learning technique is applied
toobtain a controller that maps these high level variables to the control
commands that are actually sent tothe robot. This paper describes an algorithm
that is able to embed the preprocessing stage into the learningstage in order
to get controllers directly starting from sensorial raw data with no expert
knowledgeinvolved. Due to the high dimensionality of the sensorial data, this
approach uses Quantified Fuzzy Rules(QFRs), that are able to transform
low-level input variables into high-level input variables, reducingthe
dimensionality through summarization. The proposed learning algorithm, called
Iterative QuantifiedFuzzy Rule Learning (IQFRL), is based on genetic
programming. IQFRL is able to learn rules with differentstructures, and can
manage linguistic variables with multiple granularities. The algorithm has been
testedwith the implementation of the wall-following behavior both in several
realistic simulated environmentswith different complexity and on a Pioneer 3-AT
robot in two real environments. Results have beencompared with several
well-known learning algorithms combined with different data
preprocessingtechniques, showing that IQFRL exhibits a better and statistically
significant performance. Moreover,three real world applications for which IQFRL
plays a central role are also presented: path and objecttracking with static
and moving obstacles avoidance. | [
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] |
In order to distribute the best arm identification task as close as possible
to the user's devices, on the edge of the Radio Access Network, we propose a
new problem setting, where distributed players collaborate to find the best
arm. This architecture guarantees privacy to end-users since no events are
stored. The only thing that can be observed by an adversary through the core
network is aggregated information across users. We provide a first algorithm,
Distributed Median Elimination, which is optimal in term of number of
transmitted bits and near optimal in term of speed-up factor with respect to an
optimal algorithm run independently on each player. In practice, this first
algorithm cannot handle the trade-off between the communication cost and the
speed-up factor, and requires some knowledge about the distribution of players.
Extended Distributed Median Elimination overcomes these limitations, by playing
in parallel different instances of Distributed Median Elimination and selecting
the best one. Experiments illustrate and complete the analysis. According to
the analysis, in comparison to Median Elimination performed on each player, the
proposed algorithm shows significant practical improvements. | [
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] |
Complex network topologies and hyperbolic geometry seem specularly connected,
and one of the most fascinating and challenging problems of recent complex
network theory is to map a given network to its hyperbolic space. The
Popularity Similarity Optimization (PSO) model represents - at the moment - the
climax of this theory. It suggests that the trade-off between node popularity
and similarity is a mechanism to explain how complex network topologies emerge
- as discrete samples - from the continuous world of hyperbolic geometry. The
hyperbolic space seems appropriate to represent real complex networks. In fact,
it preserves many of their fundamental topological properties, and can be
exploited for real applications such as, among others, link prediction and
community detection. Here, we observe for the first time that a
topological-based machine learning class of algorithms - for nonlinear
unsupervised dimensionality reduction - can directly approximate the network's
node angular coordinates of the hyperbolic model into a two-dimensional space,
according to a similar topological organization that we named angular
coalescence. On the basis of this phenomenon, we propose a new class of
algorithms that offers fast and accurate coalescent embedding of networks in
the hyperbolic space even for graphs with thousands of nodes. | [
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] |
Ordinal peer grading has been proposed as a simple and scalable solution for
computing reliable information about student performance in massive open online
courses. The idea is to outsource the grading task to the students themselves
as follows. After the end of an exam, each student is asked to rank -- in terms
of quality -- a bundle of exam papers by fellow students. An aggregation rule
then combines the individual rankings into a global one that contains all
students. We define a broad class of simple aggregation rules, which we call
type-ordering aggregation rules, and present a theoretical framework for
assessing their effectiveness. When statistical information about the grading
behaviour of students is available (in terms of a noise matrix that
characterizes the grading behaviour of the average student from a student
population), the framework can be used to compute the optimal rule from this
class with respect to a series of performance objectives that compare the
ranking returned by the aggregation rule to the underlying ground truth
ranking. For example, a natural rule known as Borda is proved to be optimal
when students grade correctly. In addition, we present extensive simulations
that validate our theory and prove it to be extremely accurate in predicting
the performance of aggregation rules even when only rough information about
grading behaviour (i.e., an approximation of the noise matrix) is available.
Both in the application of our theoretical framework and in our simulations, we
exploit data about grading behaviour of students that have been extracted from
two field experiments in the University of Patras. | [
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] |
Learning models of artificial intelligence can nowadays perform very well on
a large variety of tasks. However, in practice different task environments are
best handled by different learning models, rather than a single, universal,
approach. Most non-trivial models thus require the adjustment of several to
many learning parameters, which is often done on a case-by-case basis by an
external party. Meta-learning refers to the ability of an agent to autonomously
and dynamically adjust its own learning parameters, or meta-parameters. In this
work we show how projective simulation, a recently developed model of
artificial intelligence, can naturally be extended to account for meta-learning
in reinforcement learning settings. The projective simulation approach is based
on a random walk process over a network of clips. The suggested meta-learning
scheme builds upon the same design and employs clip networks to monitor the
agent's performance and to adjust its meta-parameters "on the fly". We
distinguish between "reflexive adaptation" and "adaptation through learning",
and show the utility of both approaches. In addition, a trade-off between
flexibility and learning-time is addressed. The extended model is examined on
three different kinds of reinforcement learning tasks, in which the agent has
different optimal values of the meta-parameters, and is shown to perform well,
reaching near-optimal to optimal success rates in all of them, without ever
needing to manually adjust any meta-parameter. | [
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] |
For an autonomous agent, executing a poor policy may be costly or even
dangerous. For such agents, it is desirable to determine confidence interval
lower bounds on the performance of any given policy without executing said
policy. Current methods for exact high confidence off-policy evaluation that
use importance sampling require a substantial amount of data to achieve a tight
lower bound. Existing model-based methods only address the problem in discrete
state spaces. Since exact bounds are intractable for many domains we trade off
strict guarantees of safety for more data-efficient approximate bounds. In this
context, we propose two bootstrapping off-policy evaluation methods which use
learned MDP transition models in order to estimate lower confidence bounds on
policy performance with limited data in both continuous and discrete state
spaces. Since direct use of a model may introduce bias, we derive a theoretical
upper bound on model bias for when the model transition function is estimated
with i.i.d. trajectories. This bound broadens our understanding of the
conditions under which model-based methods have high bias. Finally, we
empirically evaluate our proposed methods and analyze the settings in which
different bootstrapping off-policy confidence interval methods succeed and
fail. | [
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] |
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically
instrumented with numerous sensors to capture the behavior and health of the
machine. However, there are often external factors or variables which are not
captured by sensors leading to time-series which are inherently unpredictable.
For instance, manual controls and/or unmonitored environmental conditions or
load may lead to inherently unpredictable time-series. Detecting anomalies in
such scenarios becomes challenging using standard approaches based on
mathematical models that rely on stationarity, or prediction models that
utilize prediction errors to detect anomalies. We propose a Long Short Term
Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD)
that learns to reconstruct 'normal' time-series behavior, and thereafter uses
reconstruction error to detect anomalies. We experiment with three publicly
available quasi predictable time-series datasets: power demand, space shuttle,
and ECG, and two real-world engine datasets with both predictive and
unpredictable behavior. We show that EncDec-AD is robust and can detect
anomalies from predictable, unpredictable, periodic, aperiodic, and
quasi-periodic time-series. Further, we show that EncDec-AD is able to detect
anomalies from short time-series (length as small as 30) as well as long
time-series (length as large as 500). | [
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] |
One of the main obstacles to broad application of reinforcement learning
methods is the parameter sensitivity of our core learning algorithms. In many
large-scale applications, online computation and function approximation
represent key strategies in scaling up reinforcement learning algorithms. In
this setting, we have effective and reasonably well understood algorithms for
adapting the learning-rate parameter, online during learning. Such
meta-learning approaches can improve robustness of learning and enable
specialization to current task, improving learning speed. For
temporal-difference learning algorithms which we study here, there is yet
another parameter, $\lambda$, that similarly impacts learning speed and
stability in practice. Unfortunately, unlike the learning-rate parameter,
$\lambda$ parametrizes the objective function that temporal-difference methods
optimize. Different choices of $\lambda$ produce different fixed-point
solutions, and thus adapting $\lambda$ online and characterizing the
optimization is substantially more complex than adapting the learning-rate
parameter. There are no meta-learning method for $\lambda$ that can achieve (1)
incremental updating, (2) compatibility with function approximation, and (3)
maintain stability of learning under both on and off-policy sampling. In this
paper we contribute a novel objective function for optimizing $\lambda$ as a
function of state rather than time. We derive a new incremental, linear
complexity $\lambda$-adaption algorithm that does not require offline batch
updating or access to a model of the world, and present a suite of experiments
illustrating the practicality of our new algorithm in three different settings.
Taken together, our contributions represent a concrete step towards black-box
application of temporal-difference learning methods in real world problems. | [
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Recommendation systems usually involve exploiting the relations among known
features and content that describe items (content-based filtering) or the
overlap of similar users who interacted with or rated the target item
(collaborative filtering). To combine these two filtering approaches, current
model-based hybrid recommendation systems typically require extensive feature
engineering to construct a user profile. Statistical Relational Learning (SRL)
provides a straightforward way to combine the two approaches. However, due to
the large scale of the data used in real world recommendation systems, little
research exists on applying SRL models to hybrid recommendation systems, and
essentially none of that research has been applied on real big-data-scale
systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL
learning approaches to construct a real hybrid recommendation system.
Furthermore, in order to satisfy a common requirement in recommendation systems
(i.e. that false positives are more undesirable and therefore penalized more
harshly than false negatives), our approach can also allow tuning the trade-off
between the precision and recall of the system in a principled way. Our
experimental results demonstrate the efficiency of our proposed approach as
well as its improved performance on recommendation precision. | [
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] |
Protein quality assessment (QA) by ranking and selecting protein models has
long been viewed as one of the major challenges for protein tertiary structure
prediction. Especially, estimating the quality of a single protein model, which
is important for selecting a few good models out of a large model pool
consisting of mostly low-quality models, is still a largely unsolved problem.
We introduce a novel single-model quality assessment method DeepQA based on
deep belief network that utilizes a number of selected features describing the
quality of a model from different perspectives, such as energy, physio-chemical
characteristics, and structural information. The deep belief network is trained
on several large datasets consisting of models from the Critical Assessment of
Protein Structure Prediction (CASP) experiments, several publicly available
datasets, and models generated by our in-house ab initio method. Our experiment
demonstrate that deep belief network has better performance compared to Support
Vector Machines and Neural Networks on the protein model quality assessment
problem, and our method DeepQA achieves the state-of-the-art performance on
CASP11 dataset. It also outperformed two well-established methods in selecting
good outlier models from a large set of models of mostly low quality generated
by ab initio modeling methods. DeepQA is a useful tool for protein single model
quality assessment and protein structure prediction. The source code,
executable, document and training/test datasets of DeepQA for Linux is freely
available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/. | [
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] |
Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like Gaussian processes to approximate and minimize the
validation error function of hyperparameter values. However, probabilistic
surrogates require accurate estimates of sufficient statistics (e.g.,
covariance) of the error distribution and thus need many function evaluations
with a sizeable number of hyperparameters. This makes them inefficient for
optimizing hyperparameters of deep learning algorithms, which are highly
expensive to evaluate. In this work, we propose a new deterministic and
efficient hyperparameter optimization method that employs radial basis
functions as error surrogates. The proposed mixed integer algorithm, called
HORD, searches the surrogate for the most promising hyperparameter values
through dynamic coordinate search and requires many fewer function evaluations.
HORD does well in low dimensions but it is exceptionally better in higher
dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural
networks demonstrate HORD significantly outperforms the well-established
Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on
average, HORD is more than 6 times faster than GP-EI in obtaining the best
configuration of 19 hyperparameters. | [
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] |
Multi-view data clustering refers to categorizing a data set by making good
use of related information from multiple representations of the data. It
becomes important nowadays because more and more data can be collected in a
variety of ways, in different settings and from different sources, so each data
set can be represented by different sets of features to form different views of
it. Many approaches have been proposed to improve clustering performance by
exploring and integrating heterogeneous information underlying different views.
In this paper, we propose a new multi-view fuzzy clustering approach called
MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In
MinimaxFCM the consensus clustering results are generated based on minimax
optimization in which the maximum disagreements of different weighted views are
minimized. Moreover, the weight of each view can be learned automatically in
the clustering process. In addition, there is only one parameter to be set
besides the fuzzifier. The detailed problem formulation, updating rules
derivation, and the in-depth analysis of the proposed MinimaxFCM are provided
here. Experimental studies on nine multi-view data sets including real world
image and document data sets have been conducted. We observed that MinimaxFCM
outperforms related multi-view clustering approaches in terms of clustering
accuracy, demonstrating the great potential of MinimaxFCM for multi-view data
analysis. | [
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] |
We present a loss function for neural networks that encompasses an idea of
trivial versus non-trivial predictions, such that the network jointly
determines its own prediction goals and learns to satisfy them. This permits
the network to choose sub-sets of a problem which are most amenable to its
abilities to focus on solving, while discarding 'distracting' elements that
interfere with its learning. To do this, the network first transforms the raw
data into a higher-level categorical representation, and then trains a
predictor from that new time series to its future. To prevent a trivial
solution of mapping the signal to zero, we introduce a measure of
non-triviality via a contrast between the prediction error of the learned model
with a naive model of the overall signal statistics. The transform can learn to
discard uninformative and unpredictable components of the signal in favor of
the features which are both highly predictive and highly predictable. This
creates a coarse-grained model of the time-series dynamics, focusing on
predicting the slowly varying latent parameters which control the statistics of
the time-series, rather than predicting the fast details directly. The result
is a semi-supervised algorithm which is capable of extracting latent
parameters, segmenting sections of time-series with differing statistics, and
building a higher-level representation of the underlying dynamics from
unlabeled data. | [
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