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The main contribution of this paper is to introduce and describe a new
recommender-systems dataset (RARD II). It is based on data from Mr. DLib, a
recommender-system as-a-service in the digital library and
reference-management-software domain. As such, RARD II complements datasets
from other domains such as books, movies, and music. The dataset encompasses
94m recommendations, delivered in the two years from September 2016 to
September 2018. The dataset covers an item-space of 24m unique items. RARD II
provides a range of rich recommendation data, beyond conventional ratings. For
example, in addition to the usual (implicit) ratings matrices, RARD II includes
the original recommendation logs, which provide a unique insight into many
aspects of the algorithms that generated the recommendations. The logs enable
researchers to conduct various analyses about a real-world recommender system.
This includes the evaluation of meta-learning approaches for predicting
algorithm performance. In this paper, we summarise the key features of this
dataset release, describe how it was generated and discuss some of its unique
features. Compared to its predecessor RARD, RARD II contains 64% more
recommendations, 187% more features (algorithms, parameters, and statistics),
50% more clicks, 140% more documents, and one additional service partner
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] |
The performance of many machine learning techniques depends on the choice of
an appropriate similarity or distance measure on the input space. Similarity
learning (or metric learning) aims at building such a measure from training
data so that observations with the same (resp. different) label are as close
(resp. far) as possible. In this paper, similarity learning is investigated
from the perspective of pairwise bipartite ranking, where the goal is to rank
the elements of a database by decreasing order of the probability that they
share the same label with some query data point, based on the similarity
scores. A natural performance criterion in this setting is pointwise ROC
optimization: maximize the true positive rate under a fixed false positive
rate. We study this novel perspective on similarity learning through a rigorous
probabilistic framework. The empirical version of the problem gives rise to a
constrained optimization formulation involving U-statistics, for which we
derive universal learning rates as well as faster rates under a noise
assumption on the data distribution. We also address the large-scale setting by
analyzing the effect of sampling-based approximations. Our theoretical results
are supported by illustrative numerical experiments. | [
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] |
Similarity and metric learning provides a principled approach to construct a
task-specific similarity from weakly supervised data. However, these methods
are subject to the curse of dimensionality: as the number of features grows
large, poor generalization is to be expected and training becomes intractable
due to high computational and memory costs. In this paper, we propose a
similarity learning method that can efficiently deal with high-dimensional
sparse data. This is achieved through a parameterization of similarity
functions by convex combinations of sparse rank-one matrices, together with the
use of a greedy approximate Frank-Wolfe algorithm which provides an efficient
way to control the number of active features. We show that the convergence rate
of the algorithm, as well as its time and memory complexity, are independent of
the data dimension. We further provide a theoretical justification of our
modeling choices through an analysis of the generalization error, which depends
logarithmically on the sparsity of the solution rather than on the number of
features. Our experiments on datasets with up to one million features
demonstrate the ability of our approach to generalize well despite the high
dimensionality as well as its superiority compared to several competing
methods. | [
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] |
In this manuscript, we introduce a real-time motion planning system based on
the Baidu Apollo (open source) autonomous driving platform. The developed
system aims to address the industrial level-4 motion planning problem while
considering safety, comfort and scalability. The system covers multilane and
single-lane autonomous driving in a hierarchical manner: (1) The top layer of
the system is a multilane strategy that handles lane-change scenarios by
comparing lane-level trajectories computed in parallel. (2) Inside the
lane-level trajectory generator, it iteratively solves path and speed
optimization based on a Frenet frame. (3) For path and speed optimization, a
combination of dynamic programming and spline-based quadratic programming is
proposed to construct a scalable and easy-to-tune framework to handle traffic
rules, obstacle decisions and smoothness simultaneously. The planner is
scalable to both highway and lower-speed city driving scenarios. We also
demonstrate the algorithm through scenario illustrations and on-road test
results.
The system described in this manuscript has been deployed to dozens of Baidu
Apollo autonomous driving vehicles since Apollo v1.5 was announced in September
2017. As of May 16th, 2018, the system has been tested under 3,380 hours and
approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous
driving under various urban scenarios.
The algorithm described in this manuscript is available at
https://github.com/ApolloAuto/apollo/tree/master/modules/planning. | [
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In recent years, with the popularization of deep learning frameworks and
large datasets, researchers have started parallelizing their models in order to
train faster. This is crucially important, because they typically explore many
hyperparameters in order to find the best ones for their applications. This
process is time consuming and, consequently, speeding up training improves
productivity. One approach to parallelize deep learning models followed by many
researchers is based on weak scaling. The minibatches increase in size as new
GPUs are added to the system. In addition, new learning rates schedules have
been proposed to fix optimization issues that occur with large minibatch sizes.
In this paper, however, we show that the recommendations provided by recent
work do not apply to models that lack large datasets. In fact, we argument in
favor of using strong scaling for achieving reliable performance in such cases.
We evaluated our approach with up to 32 GPUs and show that weak scaling not
only does not have the same accuracy as the sequential model, it also fails to
converge most of time. Meanwhile, strong scaling has good scalability while
having exactly the same accuracy of a sequential implementation. | [
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] |
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables. | [
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] |
Person-Job Fit is the process of matching the right talent for the right job
by identifying talent competencies that are required for the job. While many
qualitative efforts have been made in related fields, it still lacks of
quantitative ways of measuring talent competencies as well as the job's talent
requirements. To this end, in this paper, we propose a novel end-to-end
data-driven model based on Convolutional Neural Network (CNN), namely
Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to
the requirements of a job. To be specific, PJFNN is a bipartite neural network
which can effectively learn the joint representation of Person-Job fitness from
historical job applications. In particular, due to the design of a hierarchical
representation structure, PJFNN can not only estimate whether a candidate fits
a job, but also identify which specific requirement items in the job posting
are satisfied by the candidate by measuring the distances between corresponding
latent representations. Finally, the extensive experiments on a large-scale
real-world dataset clearly validate the performance of PJFNN in terms of
Person-Job Fit prediction. Also, we provide effective data visualization to
show some job and talent benchmark insights obtained by PJFNN. | [
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] |
Over the past decades, deep learning (DL) systems have achieved tremendous
success and gained great popularity in various applications, such as
intelligent machines, image processing, speech processing, and medical
diagnostics. Deep neural networks are the key driving force behind its recent
success, but still seem to be a magic black box lacking interpretability and
understanding. This brings up many open safety and security issues with
enormous and urgent demands on rigorous methodologies and engineering practice
for quality enhancement. A plethora of studies have shown that the
state-of-the-art DL systems suffer from defects and vulnerabilities that can
lead to severe loss and tragedies, especially when applied to real-world
safety-critical applications. In this paper, we perform a large-scale study and
construct a paper repository of 223 relevant works to the quality assurance,
security, and interpretation of deep learning. We, from a software quality
assurance perspective, pinpoint challenges and future opportunities towards
universal secure deep learning engineering. We hope this work and the
accompanied paper repository can pave the path for the software engineering
community towards addressing the pressing industrial demand of secure
intelligent applications. | [
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] |
Perceiving the surrounding environment in terms of objects is useful for any
general purpose intelligent agent. In this paper, we investigate a fundamental
mechanism making object perception possible, namely the identification of
spatio-temporally invariant structures in the sensorimotor experience of an
agent. We take inspiration from the Sensorimotor Contingencies Theory to define
a computational model of this mechanism through a sensorimotor, unsupervised
and predictive approach. Our model is based on processing the unsupervised
interaction of an artificial agent with its environment. We show how
spatio-temporally invariant structures in the environment induce regularities
in the sensorimotor experience of an agent, and how this agent, while building
a predictive model of its sensorimotor experience, can capture them as densely
connected subgraphs in a graph of sensory states connected by motor commands.
Our approach is focused on elementary mechanisms, and is illustrated with a set
of simple experiments in which an agent interacts with an environment. We show
how the agent can build an internal model of moving but spatio-temporally
invariant structures by performing a Spectral Clustering of the graph modeling
its overall sensorimotor experiences. We systematically examine properties of
the model, shedding light more globally on the specificities of the paradigm
with respect to methods based on the supervised processing of collections of
static images. | [
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] |
Training neural networks on large datasets can be accelerated by distributing
the workload over a network of machines. As datasets grow ever larger, networks
of hundreds or thousands of machines become economically viable. The time cost
of communicating gradients limits the effectiveness of using such large machine
counts, as may the increased chance of network faults. We explore a
particularly simple algorithm for robust, communication-efficient
learning---signSGD. Workers transmit only the sign of their gradient vector to
a server, and the overall update is decided by a majority vote. This algorithm
uses $32\times$ less communication per iteration than full-precision,
distributed SGD. Under natural conditions verified by experiment, we prove that
signSGD converges in the large and mini-batch settings, establishing
convergence for a parameter regime of Adam as a byproduct. Aggregating sign
gradients by majority vote means that no individual worker has too much power.
We prove that unlike SGD, majority vote is robust when up to 50% of workers
behave adversarially. The class of adversaries we consider includes as special
cases those that invert or randomise their gradient estimate. On the practical
side, we built our distributed training system in Pytorch. Benchmarking against
the state of the art collective communications library (NCCL), our
framework---with the parameter server housed entirely on one machine---led to a
25% reduction in time for training resnet50 on Imagenet when using 15 AWS
p3.2xlarge machines. | [
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] |
We study a general problem of allocating limited resources to heterogeneous
customers over time under model uncertainty. Each type of customer can be
serviced using different actions, each of which stochastically consumes some
combination of resources, and returns different rewards for the resources
consumed. We consider a general model where the resource consumption
distribution associated with each (customer type, action)-combination is not
known, but is consistent and can be learned over time. In addition, the
sequence of customer types to arrive over time is arbitrary and completely
unknown.
We overcome both the challenges of model uncertainty and customer
heterogeneity by judiciously synthesizing two algorithmic frameworks from the
literature: inventory balancing, which "reserves" a portion of each resource
for high-reward customer types which could later arrive, and online learning,
which shows how to "explore" the resource consumption distributions of each
customer type under different actions. We define an auxiliary problem, which
allows for existing competitive ratio and regret bounds to be seamlessly
integrated. Furthermore, we show that the performance guarantee generated by
our framework is tight, that is, we provide an information-theoretic lower
bound which shows that both the loss from competitive ratio and the loss for
regret are relevant in the combined problem.
Finally, we demonstrate the efficacy of our algorithms on a publicly
available hotel data set. Our framework is highly practical in that it requires
no historical data (no fitted customer choice models, nor forecasting of
customer arrival patterns) and can be used to initialize allocation strategies
in fast-changing environments. | [
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] |
Predicting the motion of a mobile agent from a third-person perspective is an
important component for many robotics applications, such as autonomous
navigation and tracking. With accurate motion prediction of other agents,
robots can plan for more intelligent behaviors to achieve specified objectives,
instead of acting in a purely reactive way. Previous work addresses motion
prediction by either only filtering kinematics, or using hand-designed and
learned representations of the environment. Instead of separating kinematic and
environmental context, we propose a novel approach to integrate both into an
inverse reinforcement learning (IRL) framework for trajectory prediction.
Instead of exponentially increasing the state-space complexity with kinematics,
we propose a two-stage neural network architecture that considers motion and
environment together to recover the reward function. The first-stage network
learns feature representations of the environment using low-level LiDAR
statistics and the second-stage network combines those learned features with
kinematics data. We collected over 30 km of off-road driving data and validated
experimentally that our method can effectively extract useful environmental and
kinematic features. We generate accurate predictions of the distribution of
future trajectories of the vehicle, encoding complex behaviors such as
multi-modal distributions at road intersections, and even show different
predictions at the same intersection depending on the vehicle's speed. | [
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] |
Appropriate traffic regulations, e.g. planned road closure, are important in
congested events. Crowd simulators have been used to find appropriate
regulations by simulating multiple scenarios with different regulations.
However, this approach requires multiple simulation runs, which are
time-consuming. In this paper, we propose a method to learn a function that
outputs regulation effects given the current traffic situation as inputs. If
the function is learned using the training data of many simulation runs in
advance, we can obtain an appropriate regulation efficiently by bypassing
simulations for the current situation. We use the graph convolutional networks
for modeling the function, which enable us to find regulations even for unseen
areas. With the proposed method, we construct a graph for each area, where a
node represents a road, and an edge represents the road connection. By running
crowd simulations with various regulations on various areas, we generate
traffic situations and regulation effects. The graph convolutional networks are
trained to output the regulation effects given the graph with the traffic
situation information as inputs. With experiments using real-world road
networks and a crowd simulator, we demonstrate that the proposed method can
find a road to close that reduces the average time needed to reach the
destination. | [
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] |
Common approaches to Reinforcement Learning (RL) are seriously challenged by
large-scale applications involving huge state spaces and sparse delayed reward
feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address
this scalability issue by learning action selection policies at multiple levels
of temporal abstraction. Abstraction can be had by identifying a relatively
small set of states that are likely to be useful as subgoals, in concert with
the learning of corresponding skill policies to achieve those subgoals. Many
approaches to subgoal discovery in HRL depend on the analysis of a model of the
environment, but the need to learn such a model introduces its own problems of
scale. Once subgoals are identified, skills may be learned through intrinsic
motivation, introducing an internal reward signal marking subgoal attainment.
In this paper, we present a novel model-free method for subgoal discovery using
incremental unsupervised learning over a small memory of the most recent
experiences (trajectories) of the agent. When combined with an intrinsic
motivation learning mechanism, this method learns both subgoals and skills,
based on experiences in the environment. Thus, we offer an original approach to
HRL that does not require the acquisition of a model of the environment,
suitable for large-scale applications. We demonstrate the efficiency of our
method on two RL problems with sparse delayed feedback: a variant of the rooms
environment and the first screen of the ATARI 2600 Montezuma's Revenge game. | [
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] |
The Jacobian matrix (or the gradient for single-output networks) is directly
related to many important properties of neural networks, such as the function
landscape, stationary points, (local) Lipschitz constants and robustness to
adversarial attacks. In this paper, we propose a recursive algorithm, RecurJac,
to compute both upper and lower bounds for each element in the Jacobian matrix
of a neural network with respect to network's input, and the network can
contain a wide range of activation functions. As a byproduct, we can
efficiently obtain a (local) Lipschitz constant, which plays a crucial role in
neural network robustness verification, as well as the training stability of
GANs. Experiments show that (local) Lipschitz constants produced by our method
is of better quality than previous approaches, thus providing better robustness
verification results. Our algorithm has polynomial time complexity, and its
computation time is reasonable even for relatively large networks.
Additionally, we use our bounds of Jacobian matrix to characterize the
landscape of the neural network, for example, to determine whether there exist
stationary points in a local neighborhood. Source code available at
\url{http://github.com/huanzhang12/RecurJac-Jacobian-bounds}. | [
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] |
Surprisingly promising results have been achieved by deep learning (DL)
systems in recent years. Many of these achievements have been reached in
academic settings, or by large technology companies with highly skilled
research groups and advanced supporting infrastructure. For companies without
large research groups or advanced infrastructure, building high-quality
production-ready systems with DL components has proven challenging. There is a
clear lack of well-functioning tools and best practices for building DL
systems. It is the goal of this research to identify what the main challenges
are, by applying an interpretive research approach in close collaboration with
companies of varying size and type.
A set of seven projects have been selected to describe the potential with
this new technology and to identify associated main challenges. A set of 12
main challenges has been identified and categorized into the three areas of
development, production, and organizational challenges. Furthermore, a mapping
between the challenges and the projects is defined, together with selected
motivating descriptions of how and why the challenges apply to specific
projects.
Compared to other areas such as software engineering or database
technologies, it is clear that DL is still rather immature and in need of
further work to facilitate development of high-quality systems. The challenges
identified in this paper can be used to guide future research by the software
engineering and DL communities. Together, we could enable a large number of
companies to start taking advantage of the high potential of the DL technology. | [
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] |
Usually considered as a classification problem, entity resolution (ER) can be
very challenging on real data due to the prevalence of dirty values. The
state-of-the-art solutions for ER were built on a variety of learning models
(most notably deep neural networks), which require lots of accurately labeled
training data. Unfortunately, high-quality labeled data usually require
expensive manual work, and are therefore not readily available in many real
scenarios. In this paper, we propose a novel learning paradigm for ER, called
gradual machine learning, which aims to enable effective machine labeling
without the requirement for manual labeling effort. It begins with some easy
instances in a task, which can be automatically labeled by the machine with
high accuracy, and then gradually labels more challenging instances by
iterative factor graph inference. In gradual machine learning, the hard
instances in a task are gradually labeled in small stages based on the
estimated evidential certainty provided by the labeled easier instances. Our
extensive experiments on real data have shown that the performance of the
proposed approach is considerably better than its unsupervised alternatives,
and highly competitive compared to the state-of-the-art supervised techniques.
Using ER as a test case, we demonstrate that gradual machine learning is a
promising paradigm potentially applicable to other challenging classification
tasks requiring extensive labeling effort. | [
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] |
In this paper, we demonstrate how to learn the objective function of a
decision-maker while only observing the problem input data and the
decision-maker's corresponding decisions over multiple rounds. We present exact
algorithms for this online version of inverse optimization which converge at a
rate of $ \mathcal{O}(1/\sqrt{T}) $ in the number of observations~$T$ and
compare their further properties. Especially, they all allow taking decisions
which are essentially as good as those of the observed decision-maker already
after relatively few iterations, but are suited best for different settings
each. Our approach is based on online learning and works for linear objectives
over arbitrary feasible sets for which we have a linear optimization oracle. As
such, it generalizes previous approaches based on KKT-system decomposition and
dualization. We also introduce several generalizations, such as the approximate
learning of non-linear objective functions, dynamically changing as well as
parameterized objectives and the case of suboptimal observed decisions. When
applied to the stochastic offline case, our algorithms are able to give
guarantees on the quality of the learned objectives in expectation. Finally, we
show the effectiveness and possible applications of our methods in indicative
computational experiments. | [
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] |
Machine learning techniques have deeply rooted in our everyday life. However,
since it is knowledge- and labor-intensive to pursue good learning performance,
human experts are heavily involved in every aspect of machine learning. In
order to make machine learning techniques easier to apply and reduce the demand
for experienced human experts, automated machine learning (AutoML) has emerged
as a hot topic with both industrial and academic interest. In this paper, we
provide an up to date survey on AutoML. First, we introduce and define the
AutoML problem, with inspiration from both realms of automation and machine
learning. Then, we propose a general AutoML framework that not only covers most
existing approaches to date but also can guide the design for new methods.
Subsequently, we categorize and review the existing works from two aspects,
i.e., the problem setup and the employed techniques. Finally, we provide a
detailed analysis of AutoML approaches and explain the reasons underneath their
successful applications. We hope this survey can serve as not only an
insightful guideline for AutoML beginners but also an inspiration for future
research. | [
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] |
Gaussian processes are the leading class of distributions on random
functions, but they suffer from well known issues including difficulty scaling
and inflexibility with respect to certain shape constraints (such as
nonnegativity). Here we propose Deep Random Splines, a flexible class of random
functions obtained by transforming Gaussian noise through a deep neural network
whose output are the parameters of a spline. Unlike Gaussian processes, Deep
Random Splines allow us to readily enforce shape constraints while inheriting
the richness and tractability of deep generative models. We also present an
observational model for point process data which uses Deep Random Splines to
model the intensity function of each point process and apply it to neural
population data to obtain a low-dimensional representation of spiking activity.
Inference is performed via a variational autoencoder that uses a novel
recurrent encoder architecture that can handle multiple point processes as
input. We use a newly collected dataset where a primate completes a pedaling
task, and observe better dimensionality reduction with our model than with
competing alternatives. | [
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One of the fundamental challenges to realize massive Multiple-Input
Multiple-Output (MIMO) communications is the accurate acquisition of channel
state information for a plurality of users at the base station. This is usually
accomplished in the UpLink (UL) direction profiting from the time division
duplexing mode. In practical base station transceivers, there exist inevitably
nonlinear hardware components, like signal amplifiers and various analog
filters, which complicates the calibration task. To deal with this challenge,
we design a deep neural network for channel calibration between the UL and
DownLink (DL) directions. During the initial training phase, the deep neural
network is trained from both UL and DL channel measurements. We then leverage
the trained deep neural network with the instantaneously estimated UL channel
to calibrate the DL one, which is not observable during the UL transmission
phase. Our numerical results confirm the merits of the proposed approach, and
show that it can achieve performance comparable to conventional approaches,
like the Agros method and methods based on least squares, that however assume
linear hardware behavior models. More importantly, considering generic
nonlinear relationships between the UL and DL channels, it is demonstrated that
our deep neural network approach exhibits robust performance, even when the
number of training sequences is limited. | [
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] |
Much of the literature on robotic perception focuses on the visual modality.
Vision provides a global observation of a scene, making it broadly useful.
However, in the domain of robotic manipulation, vision alone can sometimes
prove inadequate: in the presence of occlusions or poor lighting, visual object
identification might be difficult. The sense of touch can provide robots with
an alternative mechanism for recognizing objects. In this paper, we study the
problem of touch-based instance recognition. We propose a novel framing of the
problem as multi-modal recognition: the goal of our system is to recognize,
given a visual and tactile observation, whether or not these observations
correspond to the same object. To our knowledge, our work is the first to
address this type of multi-modal instance recognition problem on such a
large-scale with our analysis spanning 98 different objects. We employ a robot
equipped with two GelSight touch sensors, one on each finger, and a
self-supervised, autonomous data collection procedure to collect a dataset of
tactile observations and images. Our experimental results show that it is
possible to accurately recognize object instances by touch alone, including
instances of novel objects that were never seen during training. Our learned
model outperforms other methods on this complex task, including that of human
volunteers. | [
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] |
Explainability and effectiveness are two key aspects for building recommender
systems. Prior efforts mostly focus on incorporating side information to
achieve better recommendation performance. However, these methods have some
weaknesses: (1) prediction of neural network-based embedding methods are hard
to explain and debug; (2) symbolic, graph-based approaches (e.g., meta
path-based models) require manual efforts and domain knowledge to define
patterns and rules, and ignore the item association types (e.g. substitutable
and complementary). In this paper, we propose a novel joint learning framework
to integrate \textit{induction of explainable rules from knowledge graph} with
\textit{construction of a rule-guided neural recommendation model}. The
framework encourages two modules to complement each other in generating
effective and explainable recommendation: 1) inductive rules, mined from
item-centric knowledge graphs, summarize common multi-hop relational patterns
for inferring different item associations and provide human-readable
explanation for model prediction; 2) recommendation module can be augmented by
induced rules and thus have better generalization ability dealing with the
cold-start issue. Extensive experiments\footnote{Code and data can be found at:
\url{https://github.com/THUIR/RuleRec}} show that our proposed method has
achieved significant improvements in item recommendation over baselines on
real-world datasets. Our model demonstrates robust performance over "noisy"
item knowledge graphs, generated by linking item names to related entities. | [
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] |
Bayesian Optimisation (BO) refers to a suite of techniques for global
optimisation of expensive black box functions, which use introspective Bayesian
models of the function to efficiently search for the optimum. While BO has been
applied successfully in many applications, modern optimisation tasks usher in
new challenges where conventional methods fail spectacularly. In this work, we
present Dragonfly, an open source Python library for scalable and robust BO.
Dragonfly incorporates multiple recently developed methods that allow BO to be
applied in challenging real world settings; these include better methods for
handling higher dimensional domains, methods for handling multi-fidelity
evaluations when cheap approximations of an expensive function are available,
methods for optimising over structured combinatorial spaces, such as the space
of neural network architectures, and methods for handling parallel evaluations.
Additionally, we develop new methodological improvements in BO for selecting
the Bayesian model, selecting the acquisition function, and optimising over
complex domains with different variable types and additional constraints. We
compare Dragonfly to a suite of other packages and algorithms for global
optimisation and demonstrate that when the above methods are integrated, they
enable significant improvements in the performance of BO. The Dragonfly library
is available at dragonfly.github.io. | [
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] |
Mental health conditions cause a great deal of distress or impairment;
depression alone will affect 11% of the world's population. The application of
Artificial Intelligence (AI) and big-data technologies to mental health has
great potential for personalizing treatment selection, prognosticating,
monitoring for relapse, detecting and helping to prevent mental health
conditions before they reach clinical-level symptomatology, and even delivering
some treatments. However, unlike similar applications in other fields of
medicine, there are several unique challenges in mental health applications
which currently pose barriers towards the implementation of these technologies.
Specifically, there are very few widely used or validated biomarkers in mental
health, leading to a heavy reliance on patient and clinician derived
questionnaire data as well as interpretation of new signals such as digital
phenotyping. In addition, diagnosis also lacks the same objective 'gold
standard' as in other conditions such as oncology, where clinicians and
researchers can often rely on pathological analysis for confirmation of
diagnosis. In this chapter we discuss the major opportunities, limitations and
techniques used for improving mental healthcare through AI and big-data. We
explore both the computational, clinical and ethical considerations and best
practices as well as lay out the major researcher directions for the near
future. | [
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] |
Despite its great success, machine learning can have its limits when dealing
with insufficient training data. A potential solution is the additional
integration of prior knowledge into the training process which leads to the
notion of informed machine learning. In this paper, we present a structured
overview of various approaches in this field. We provide a definition and
propose a concept for informed machine learning which illustrates its building
blocks and distinguishes it from conventional machine learning. We introduce a
taxonomy that serves as a classification framework for informed machine
learning approaches. It considers the source of knowledge, its representation,
and its integration into the machine learning pipeline. Based on this taxonomy,
we survey related research and describe how different knowledge representations
such as algebraic equations, logic rules, or simulation results can be used in
learning systems. This evaluation of numerous papers on the basis of our
taxonomy uncovers key methods in the field of informed machine learning. | [
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While machine-type communication (MTC) devices generate considerable amounts
of data, they often cannot process the data due to limited energy and
computational power. To empower MTC with intelligence, edge machine learning
has been proposed. However, power allocation in this paradigm requires
maximizing the learning performance instead of the communication throughput,
for which the celebrated water-filling and max-min fairness algorithms become
inefficient. To this end, this paper proposes learning centric power allocation
(LCPA), which provides a new perspective on radio resource allocation in
learning driven scenarios. By employing 1) an empirical classification error
model that is supported by learning theory and 2) an uncertainty sampling
method that accounts for different distributions at users, LCPA is formulated
as a nonconvex nonsmooth optimization problem, and is solved using a
majorization minimization (MM) framework. To get deeper insights into LCPA,
asymptotic analysis shows that the transmit powers are inversely proportional
to the channel gains, and scale exponentially with the learning parameters.
This is in contrast to traditional power allocations where quality of wireless
channels is the only consideration. Last but not least, a large-scale
optimization algorithm termed mirror-prox LCPA is further proposed to enable
LCPA in large-scale settings. Extensive numerical results demonstrate that the
proposed LCPA algorithms outperform traditional power allocation algorithms,
and the large-scale optimization algorithm reduces the computation time by
orders of magnitude compared with MM-based LCPA but still achieves competing
learning performance. | [
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] |
Graph convolution is the core of most Graph Neural Networks (GNNs) and
usually approximated by message passing between direct (one-hop) neighbors. In
this work, we remove the restriction of using only the direct neighbors by
introducing a powerful, yet spatially localized graph convolution: Graph
diffusion convolution (GDC). GDC leverages generalized graph diffusion,
examples of which are the heat kernel and personalized PageRank. It alleviates
the problem of noisy and often arbitrarily defined edges in real graphs. We
show that GDC is closely related to spectral-based models and thus combines the
strengths of both spatial (message passing) and spectral methods. We
demonstrate that replacing message passing with graph diffusion convolution
consistently leads to significant performance improvements across a wide range
of models on both supervised and unsupervised tasks and a variety of datasets.
Furthermore, GDC is not limited to GNNs but can trivially be combined with any
graph-based model or algorithm (e.g. spectral clustering) without requiring any
changes to the latter or affecting its computational complexity. Our
implementation is available online. | [
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] |
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations
(ABSs) to assist terrestrial infrastructure for keeping wireless connectivity
in various emergency scenarios. To maximize the coverage rate of N ground users
(GUs) by jointly placing multiple ABSs with limited coverage range is known to
be a NP-hard problem with exponential complexity in N. The problem is further
complicated when the coverage range becomes irregular due to site-specific
blockage (e.g., buildings) on the air-ground channel in the 3-dimensional (3D)
space. To tackle this challenging problem, this paper applies the Deep
Reinforcement Learning (DRL) method by 1) representing the state by a coverage
bitmap to capture the spatial correlation of GUs/ABSs, whose dimension and
associated neural network complexity is invariant with arbitrarily large N; and
2) designing the action and reward for the DRL agent to effectively learn from
the dynamic interactions with the complicated propagation environment
represented by a 3D Terrain Map. Specifically, a novel two-level design
approach is proposed, consisting of a preliminary design based on the dominant
line-of-sight (LoS) channel model, and an advanced design to further refine the
ABS positions based on site-specific LoS/non-LoS channel states. The double
deep Q-network (DQN) with Prioritized Experience Replay (Prioritized Replay
DDQN) algorithm is applied to train the policy of multi-ABS placement decision.
Numerical results show that the proposed approach significantly improves the
coverage rate in complex environment, compared to the benchmark DQN and K-means
algorithms. | [
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We optimize a six degrees of freedom hovering policy using reinforcement
meta-learning. The policy maps flash LIDAR measurements directly to on/off
spacecraft body-frame thrust commands, allowing hovering at a fixed position
and attitude in the asteroid body-fixed reference frame. Importantly, the
policy does not require position and velocity estimates, and can operate in
environments with unknown dynamics, and without an asteroid shape model or
navigation aids. Indeed, during optimization the agent is confronted with a new
randomly generated asteroid for each episode, insuring that it does not learn
an asteroid's shape, texture, or environmental dynamics. This allows the
deployed policy to generalize well to novel asteroid characteristics, which we
demonstrate in our experiments. Moreover, our experiments show that the
optimized policy adapts to actuator failure and sensor noise. Although the
policy is optimized using randomly generated synthetic asteroids, it is tested
on two shape models from actual asteroids: Bennu and Itokawa. We find that the
policy generalizes well to these shape models. The hovering controller has the
potential to simplify mission planning by allowing asteroid body-fixed hovering
immediately upon the spacecraft's arrival to an asteroid. This in turn
simplifies shape model generation and allows resource mapping via remote
sensing immediately upon arrival at the target asteroid. | [
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] |
Large-scale screening for potential threats with limited resources and
capacity for screening is a problem of interest at airports, seaports, and
other ports of entry. Adversaries can observe screening procedures and arrive
at a time when there will be gaps in screening due to limited resource
capacities. To capture this game between ports and adversaries, this problem
has been previously represented as a Stackelberg game, referred to as a Threat
Screening Game (TSG). Given the significant complexity associated with solving
TSGs and uncertainty in arrivals of customers, existing work has assumed that
screenees arrive and are allocated security resources at the beginning of the
time window. In practice, screenees such as airport passengers arrive in bursts
correlated with flight time and are not bound by fixed time windows. To address
this, we propose an online threat screening model in which screening strategy
is determined adaptively as a passenger arrives while satisfying a hard bound
on acceptable risk of not screening a threat. To solve the online problem with
a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem
with constraints on the action space (hard bound on risk). We provide a novel
way to efficiently enforce linear inequality constraints on the action output
in Deep Reinforcement Learning. We show that our solution allows us to
significantly reduce screenee wait time while guaranteeing a bound on risk. | [
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] |
The unification of low-level perception and high-level reasoning is a
long-standing problem in artificial intelligence, which has the potential to
not only bring the areas of logic and learning closer together but also
demonstrate how abstract concepts might emerge from sensory data. Precisely
because deep learning methods dominate perception-based learning, including
vision, speech, and linguistic grammar, there is fast-growing literature on how
to integrate symbolic reasoning and deep learning. Broadly, efforts seem to
fall into three camps: those focused on defining a logic whose formulas capture
deep learning, ones that integrate symbolic constraints in deep learning, and
others that allow neural computations and symbolic reasoning to co-exist
separately, to enjoy the strengths of both worlds. In this paper, we identify
another dimension to this inquiry: what do the hidden layers really capture,
and how can we reason about that logically? In particular, we consider
autoencoders that are widely used for dimensionality reduction and inject a
symbolic generative framework onto the feature layer. This allows us, among
other things, to generate example images for a class to get a sense of what was
learned. Moreover, the modular structure of the proposed model makes it
possible to learn relations over multiple images at a time, as well as handle
noisy labels. Our empirical evaluations show the promise of this inquiry. | [
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Automated reasoning and theorem proving have recently become major challenges
for machine learning. In other domains, representations that are able to
abstract over unimportant transformations, such as abstraction over
translations and rotations in vision, are becoming more common. Standard
methods of embedding mathematical formulas for learning theorem proving are
however yet unable to handle many important transformations. In particular,
embedding previously unseen labels, that often arise in definitional encodings
and in Skolemization, has been very weak so far. Similar problems appear when
transferring knowledge between known symbols.
We propose a novel encoding of formulas that extends existing graph neural
network models. This encoding represents symbols only by nodes in the graph,
without giving the network any knowledge of the original labels. We provide
additional links between such nodes that allow the network to recover the
meaning and therefore correctly embed such nodes irrespective of the given
labels. We test the proposed encoding in an automated theorem prover based on
the tableaux connection calculus, and show that it improves on the best
characterizations used so far. The encoding is further evaluated on the premise
selection task and a newly introduced symbol guessing task, and shown to
correctly predict 65% of the symbol names. | [
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] |
Real data collected from different applications that have additional
topological structures and connection information are amenable to be
represented as a weighted graph. Considering the node labeling problem, Graph
Neural Networks (GNNs) is a powerful tool, which can mimic experts' decision on
node labeling. GNNs combine node features, connection patterns, and graph
structure by using a neural network to embed node information and pass it
through edges in the graph. We want to identify the patterns in the input data
used by the GNN model to make a decision and examine if the model works as we
desire. However, due to the complex data representation and non-linear
transformations, explaining decisions made by GNNs is challenging. In this
work, we propose new graph features' explanation methods to identify the
informative components and important node features. Besides, we propose a
pipeline to identify the key factors used for node classification. We use four
datasets (two synthetic and two real) to validate our methods. Our results
demonstrate that our explanation approach can mimic data patterns used for node
classification by human interpretation and disentangle different features in
the graphs. Furthermore, our explanation methods can be used for understanding
data, debugging GNN models, and examine model decisions. | [
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] |
Automated decision making based on big data and machine learning (ML)
algorithms can result in discriminatory decisions against certain protected
groups defined upon personal data like gender, race, sexual orientation etc.
Such algorithms designed to discover patterns in big data might not only pick
up any encoded societal biases in the training data, but even worse, they might
reinforce such biases resulting in more severe discrimination. The majority of
thus far proposed fairness-aware machine learning approaches focus solely on
the pre-, in- or post-processing steps of the machine learning process, that
is, input data, learning algorithms or derived models, respectively. However,
the fairness problem cannot be isolated to a single step of the ML process.
Rather, discrimination is often a result of complex interactions between big
data and algorithms, and therefore, a more holistic approach is required. The
proposed FAE (Fairness-Aware Ensemble) framework combines fairness-related
interventions at both pre- and postprocessing steps of the data analysis
process. In the preprocessing step, we tackle the problems of
under-representation of the protected group (group imbalance) and of
class-imbalance by generating balanced training samples. In the post-processing
step, we tackle the problem of class overlapping by shifting the decision
boundary in the direction of fairness. | [
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] |
With strict protections and regulations of data privacy and security,
conventional machine learning based on centralized datasets is confronted with
significant challenges, making artificial intelligence (AI) impractical in many
mission-critical and data-sensitive scenarios, such as finance, government, and
health. In the meantime, tremendous datasets are scattered in isolated silos in
various industries, organizations, different units of an organization, or
different branches of an international organization. These valuable data
resources are well underused. To advance AI theories and applications, we
propose a comprehensive framework (called Knowledge Federation - KF) to address
these challenges by enabling AI while preserving data privacy and ownership.
Beyond the concepts of federated learning and secure multi-party computation,
KF consists of four levels of federation: (1) information level, low-level
statistics and computation of data, meeting the requirements of simple queries,
searching and simplistic operators; (2) model level, supporting training,
learning, and inference; (3) cognition level, enabling abstract feature
representation at various levels of abstractions and contexts; (4) knowledge
level, fusing knowledge discovery, representation, and reasoning. We further
clarify the relationship and differentiation between knowledge federation and
other related research areas. We have developed a reference implementation of
KF, called iBond Platform, to offer a production-quality KF platform to enable
industrial applications in finance, insurance et al. The iBond platform will
also help establish the KF community and a comprehensive ecosystem and usher in
a novel paradigm shift towards secure, privacy-preserving and responsible AI.
As far as we know, knowledge federation is the first hierarchical and unified
framework for secure multi-party computing and learning. | [
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] |
Automatic music generation is an interdisciplinary research topic that
combines computational creativity and semantic analysis of music to create
automatic machine improvisations. An important property of such a system is
allowing the user to specify conditions and desired properties of the generated
music. In this paper we designed a model for composing melodies given a user
specified symbolic scenario combined with a previous music context. We add
manual labeled vectors denoting external music quality in terms of chord
function that provides a low dimensional representation of the harmonic tension
and resolution. Our model is capable of generating long melodies by regarding
8-beat note sequences as basic units, and shares consistent rhythm pattern
structure with another specific song. The model contains two stages and
requires separate training where the first stage adopts a Conditional
Variational Autoencoder (C-VAE) to build a bijection between note sequences and
their latent representations, and the second stage adopts long short-term
memory networks (LSTM) with structural conditions to continue writing future
melodies. We further exploit the disentanglement technique via C-VAE to allow
melody generation based on pitch contour information separately from
conditioning on rhythm patterns. Finally, we evaluate the proposed model using
quantitative analysis of rhythm and the subjective listening study. Results
show that the music generated by our model tends to have salient repetition
structures, rich motives, and stable rhythm patterns. The ability to generate
longer and more structural phrases from disentangled representations combined
with semantic scenario specification conditions shows a broad application of
our model. | [
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] |
In this paper we study the problem of correlation clustering under fairness
constraints. In the classic correlation clustering problem, we are given a
complete graph where each edge is labeled positive or negative. The goal is to
obtain a clustering of the vertices that minimizes disagreements -- the number
of negative edges trapped inside a cluster plus positive edges between
different clusters.
We consider two variations of fairness constraint for the problem of
correlation clustering where each node has a color, and the goal is to form
clusters that do not over-represent vertices of any color.
The first variant aims to generate clusters with minimum disagreements, where
the distribution of a feature (e.g. gender) in each cluster is same as the
global distribution. For the case of two colors when the desired ratio of the
number of colors in each cluster is $1:p$, we get
$\mathcal{O}(p^2)$-approximation algorithm. Our algorithm could be extended to
the case of multiple colors. We prove this problem is NP-hard.
The second variant considers relative upper and lower bounds on the number of
nodes of any color in a cluster. The goal is to avoid violating upper and lower
bounds corresponding to each color in each cluster while minimizing the total
number of disagreements. Along with our theoretical results, we show the
effectiveness of our algorithm to generate fair clusters by empirical
evaluation on real world data sets. | [
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] |
Perception of artificial agents is one the grand challenges of AI research.
Deep Learning and data-driven approaches are successful on constrained problems
where perception can be learned using supervision, but do not scale to
open-worlds. In such case, for autonomous embodied agents with first-person
sensors, perception can be learned end-to-end to solve particular tasks.
However, literature shows that perception is not a purely passive compression
mechanism, and that actions play an important role in the formulation of
abstract representations. We propose to study perception for these embodied
agents, under the mathematical formalism of group theory in order to make the
link between perception and action. In particular, we consider the commutative
properties of continuous action sequences with respect to sensory information
perceived by such an embodied agent. We introduce the Sensory Commutativity
Probability (SCP) criterion which measures how much an agent's degree of
freedom affects the environment in embodied scenarios. We show how to compute
this criterion in different environments, including realistic robotic setups.
We empirically illustrate how SCP and the commutative properties of action
sequences can be used to learn about objects in the environment and improve
sample-efficiency in Reinforcement Learning. | [
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] |
The AI community is increasingly putting its attention towards combining
symbolic and neural approaches, as it is often argued that the strengths and
weaknesses of these approaches are complementary. One recent trend in the
literature are weakly supervised learning techniques that employ operators from
fuzzy logics. In particular, these use prior background knowledge described in
such logics to help the training of a neural network from unlabeled and noisy
data. By interpreting logical symbols using neural networks, this background
knowledge can be added to regular loss functions, hence making reasoning a part
of learning. We study, both formally and empirically, how a large collection of
logical operators from the fuzzy logic literature behave in a differentiable
learning setting. We find that many of these operators, including some of the
most well-known, are highly unsuitable in this setting. A further finding
concerns the treatment of implication in these fuzzy logics, and shows a strong
imbalance between gradients driven by the antecedent and the consequent of the
implication. Furthermore, we introduce a new family of fuzzy implications
(called sigmoidal implications) to tackle this phenomenon. Finally, we
empirically show that it is possible to use Differentiable Fuzzy Logics for
semi-supervised learning, and compare how different operators behave in
practice. We find that, to achieve the largest performance improvement over a
supervised baseline, we have to resort to non-standard combinations of logical
operators which perform well in learning, but no longer satisfy the usual
logical laws. | [
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] |
To make decisions based on a model fit with auto-encoding variational Bayes
(AEVB), practitioners often let the variational distribution serve as a
surrogate for the posterior distribution. This approach yields biased estimates
of the expected risk, and therefore leads to poor decisions for two reasons.
First, the model fit with AEVB may not equal the underlying data distribution.
Second, the variational distribution may not equal the posterior distribution
under the fitted model. We explore how fitting the variational distribution
based on several objective functions other than the ELBO, while continuing to
fit the generative model based on the ELBO, affects the quality of downstream
decisions. For the probabilistic principal component analysis model, we
investigate how importance sampling error, as well as the bias of the model
parameter estimates, varies across several approximate posteriors when used as
proposal distributions. Our theoretical results suggest that a posterior
approximation distinct from the variational distribution should be used for
making decisions. Motivated by these theoretical results, we propose learning
several approximate proposals for the best model and combining them using
multiple importance sampling for decision-making. In addition to toy examples,
we present a full-fledged case study of single-cell RNA sequencing. In this
challenging instance of multiple hypothesis testing, our proposed approach
surpasses the current state of the art. | [
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] |
I wrote this paper because technology can really improve people's lives. With
it, we can live longer in a healthy body, save time through increased
efficiency and automation, and make better decisions. To get to the next level,
we need to start looking at intelligence from a much broader perspective, and
promote international interdisciplinary collaborations. Section 1 of this paper
delves into sociology and social psychology to explain that the mechanisms
underlying intelligence are inherently social. Section 2 proposes a method to
classify intelligence, and describes the differences between weak and strong
intelligence. Section 3 examines the Chinese Room argument from a different
perspective. It demonstrates that a Turing-complete machine cannot have strong
intelligence, and considers the modifications necessary for a computer to be
intelligent and have understanding. Section 4 argues that the existential risk
caused by the technological explosion of a single agent should not be of
serious concern. Section 5 looks at the AI control problem and argues that it
is impossible to build a super-intelligent machine that will do what it
creators want. By using insights from biology, it also proposes a solution to
the control problem. Section 6 discusses some of the implications of strong
intelligence. Section 7 lists the main challenges with deep learning, and
asserts that radical changes will be required to reach strong intelligence.
Section 8 examines a neuroscience framework that could help explain how a
cortical column works. Section 9 lays out the broad strokes of a road map
towards strong intelligence. Finally, section 10 analyzes the impacts and the
challenges of greater intelligence. | [
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] |
Federated learning enables training a global machine learning model from data
distributed across multiple sites, without having to move the data. This is
particularly relevant in healthcare applications, where data is rife with
personal, highly-sensitive information, and data analysis methods must provably
comply with regulatory guidelines. Although federated learning prevents sharing
raw data, it is still possible to launch privacy attacks on the model
parameters that are exposed during the training process, or on the generated
machine learning model. In this paper, we propose the first syntactic approach
for offering privacy in the context of federated learning. Unlike the
state-of-the-art differential privacy-based frameworks, our approach aims to
maximize utility or model performance, while supporting a defensible level of
privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical
evaluation on two important problems in the healthcare domain, using real-world
electronic health data of 1 million patients. The results demonstrate the
effectiveness of our approach in achieving high model performance, while
offering the desired level of privacy. Through comparative studies, we also
show that, for varying datasets, experimental setups, and privacy budgets, our
approach offers higher model performance than differential privacy-based
techniques in federated learning. | [
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] |
In this paper I argue that the search for explainable models and
interpretable decisions in AI must be reformulated in terms of the broader
project of offering a pragmatic and naturalistic account of understanding in
AI. Intuitively, the purpose of providing an explanation of a model or a
decision is to make it understandable to its stakeholders. But without a
previous grasp of what it means to say that an agent understands a model or a
decision, the explanatory strategies will lack a well-defined goal. Aside from
providing a clearer objective for XAI, focusing on understanding also allows us
to relax the factivity condition on explanation, which is impossible to fulfill
in many machine learning models, and to focus instead on the pragmatic
conditions that determine the best fit between a model and the methods and
devices deployed to understand it. After an examination of the different types
of understanding discussed in the philosophical and psychological literature, I
conclude that interpretative or approximation models not only provide the best
way to achieve the objectual understanding of a machine learning model, but are
also a necessary condition to achieve post-hoc interpretability. This
conclusion is partly based on the shortcomings of the purely functionalist
approach to post-hoc interpretability that seems to be predominant in most
recent literature. | [
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] |
Network embedding methods map a network's nodes to vectors in an embedding
space, in such a way that these representations are useful for estimating some
notion of similarity or proximity between pairs of nodes in the network. The
quality of these node representations is then showcased through results of
downstream prediction tasks. Commonly used benchmark tasks such as link
prediction, however, present complex evaluation pipelines and an abundance of
design choices. This, together with a lack of standardized evaluation setups
can obscure the real progress in the field. In this paper, we aim to shed light
on the state-of-the-art of network embedding methods for link prediction and
show, using a consistent evaluation pipeline, that only thin progress has been
made over the last years. The newly conducted benchmark that we present here,
including 17 embedding methods, also shows that many approaches are
outperformed even by simple heuristics. Finally, we argue that standardized
evaluation tools can repair this situation and boost future progress in this
field. | [
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] |
Recent advances in the interconnectedness and digitization of industrial
machines, known as Industry 4.0, pave the way for new analytical techniques.
Indeed, the availability and the richness of production-related data enables
new data-driven methods. In this paper, we propose a process mining approach
augmented with artificial intelligence that (1) reconstructs the historical
workload of a company and (2) predicts the workload using neural networks. Our
method relies on logs, representing the history of business processes related
to manufacturing. These logs are used to quantify the supply and demand and are
fed into a recurrent neural network model to predict customer orders. The
corresponding activities to fulfill these orders are then sampled from history
with a replay mechanism, based on criteria such as trace frequency and
activities similarity. An evaluation and illustration of the method is
performed on the administrative processes of Heraeus Materials SA. The workload
prediction on a one-year test set achieves an MAPE score of 19% for a one-week
forecast. The case study suggests a reasonable accuracy and confirms that a
good understanding of the historical workload combined to articulated
predictions are of great help for supporting management decisions and can
decrease costs with better resources planning on a medium-term level. | [
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Many AI problems, in robotics and other domains, are goal-based, essentially
seeking trajectories leading to various goal states. Reinforcement learning
(RL), building on Bellman's optimality equation, naturally optimizes for a
single goal, yet can be made multi-goal by augmenting the state with the goal.
Instead, we propose a new RL framework, derived from a dynamic programming
equation for the all pairs shortest path (APSP) problem, which naturally solves
multi-goal queries. We show that this approach has computational benefits for
both standard and approximate dynamic programming. Interestingly, our
formulation prescribes a novel protocol for computing a trajectory: instead of
predicting the next state given its predecessor, as in standard RL, a
goal-conditioned trajectory is constructed by first predicting an intermediate
state between start and goal, partitioning the trajectory into two. Then,
recursively, predicting intermediate points on each sub-segment, until a
complete trajectory is obtained. We call this trajectory structure a sub-goal
tree. Building on it, we additionally extend the policy gradient methodology to
recursively predict sub-goals, resulting in novel goal-based algorithms.
Finally, we apply our method to neural motion planning, where we demonstrate
significant improvements compared to standard RL on navigating a 7-DoF robot
arm between obstacles. | [
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] |
The intuitive collaboration of humans and intelligent robots (embodied AI) in
the real-world is an essential objective for many desirable applications of
robotics. Whilst there is much research regarding explicit communication, we
focus on how humans and robots interact implicitly, on motor adaptation level.
We present a real-world setup of a human-robot collaborative maze game,
designed to be non-trivial and only solvable through collaboration, by limiting
the actions to rotations of two orthogonal axes, and assigning each axes to one
player. This results in neither the human nor the agent being able to solve the
game on their own. We use deep reinforcement learning for the control of the
robotic agent, and achieve results within 30 minutes of real-world play,
without any type of pre-training. We then use this setup to perform systematic
experiments on human/agent behaviour and adaptation when co-learning a policy
for the collaborative game. We present results on how co-policy learning occurs
over time between the human and the robotic agent resulting in each
participant's agent serving as a representation of how they would play the
game. This allows us to relate a person's success when playing with different
agents than their own, by comparing the policy of the agent with that of their
own agent. | [
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] |
In this paper we provide a comprehensive introduction to knowledge graphs,
which have recently garnered significant attention from both industry and
academia in scenarios that require exploiting diverse, dynamic, large-scale
collections of data. After some opening remarks, we motivate and contrast
various graph-based data models and query languages that are used for knowledge
graphs. We discuss the roles of schema, identity, and context in knowledge
graphs. We explain how knowledge can be represented and extracted using a
combination of deductive and inductive techniques. We summarise methods for the
creation, enrichment, quality assessment, refinement, and publication of
knowledge graphs. We provide an overview of prominent open knowledge graphs and
enterprise knowledge graphs, their applications, and how they use the
aforementioned techniques. We conclude with high-level future research
directions for knowledge graphs. | [
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] |
With the advent of consumer-grade products for presenting an immersive
virtual environment (VE), there is a growing interest in utilizing VEs for
testing human navigation behavior. However, preparing a VE still requires a
high level of technical expertise in computer graphics and virtual reality,
posing a significant hurdle to embracing the emerging technology. To address
this issue, this paper presents Delayed Feedback based Immersive Navigation
Environment (DeFINE), a framework that allows for easy creation and
administration of navigation tasks within customizable VEs via intuitive
graphical user interfaces and simple settings files. Importantly, DeFINE has a
built-in capability to provide performance feedback to participants during an
experiment, a feature that is critically missing in other similar frameworks.
To show the usability of DeFINE from both experimentalists' and participants'
perspectives, a demonstration was made in which participants navigated to a
hidden goal location with feedback that differentially weighted speed and
accuracy of their responses. In addition, the participants evaluated DeFINE in
terms of its ease of use, required workload, and proneness to induce
cybersickness. The demonstration exemplified typical experimental manipulations
DeFINE accommodates and what types of data it can collect for characterizing
participants' task performance. With its out-of-the-box functionality and
potential customizability due to open-source licensing, DeFINE makes VEs more
accessible to many researchers. | [
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] |
The recent advancements in wireless technology enable connected autonomous
vehicles (CAVs) to gather information about their environment by
vehicle-to-vehicle (V2V) communication. In this work, we design an
information-sharing-based multi-agent reinforcement learning (MARL) framework
for CAVs, to take advantage of the extra information when making decisions to
improve traffic efficiency and safety. The safe actor-critic algorithm we
propose has two new techniques: the truncated Q-function and safe action
mapping. The truncated Q-function utilizes the shared information from
neighboring CAVs such that the joint state and action spaces of the Q-function
do not grow in our algorithm for a large-scale CAV system. We prove the bound
of the approximation error between the truncated-Q and global Q-functions. The
safe action mapping provides a provable safety guarantee for both the training
and execution based on control barrier functions. Using the CARLA simulator for
experiments, we show that our approach can improve the CAV system's efficiency
in terms of average velocity and comfort under different CAV ratios and
different traffic densities. We also show that our approach avoids the
execution of unsafe actions and always maintains a safe distance from other
vehicles. We construct an obstacle-at-corner scenario to show that the shared
vision can help CAVs to observe obstacles earlier and take action to avoid
traffic jams. | [
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] |
Meta-learning algorithms can accelerate the model-based reinforcement
learning (MBRL) algorithms by finding an initial set of parameters for the
dynamical model such that the model can be trained to match the actual dynamics
of the system with only a few data-points. However, in the real world, a robot
might encounter any situation starting from motor failures to finding itself in
a rocky terrain where the dynamics of the robot can be significantly different
from one another. In this paper, first, we show that when meta-training
situations (the prior situations) have such diverse dynamics, using a single
set of meta-trained parameters as a starting point still requires a large
number of observations from the real system to learn a useful model of the
dynamics. Second, we propose an algorithm called FAMLE that mitigates this
limitation by meta-training several initial starting points (i.e., initial
parameters) for training the model and allows the robot to select the most
suitable starting point to adapt the model to the current situation with only a
few gradient steps. We compare FAMLE to MBRL, MBRL with a meta-trained model
with MAML, and model-free policy search algorithm PPO for various simulated and
real robotic tasks, and show that FAMLE allows the robots to adapt to novel
damages in significantly fewer time-steps than the baselines. | [
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] |
Imitation learning is an effective and safe technique to train robot policies
in the real world because it does not depend on an expensive random exploration
process. However, due to the lack of exploration, learning policies that
generalize beyond the demonstrated behaviors is still an open challenge. We
present a novel imitation learning framework to enable robots to 1) learn
complex real world manipulation tasks efficiently from a small number of human
demonstrations, and 2) synthesize new behaviors not contained in the collected
demonstrations. Our key insight is that multi-task domains often present a
latent structure, where demonstrated trajectories for different tasks intersect
at common regions of the state space. We present Generalization Through
Imitation (GTI), a two-stage offline imitation learning algorithm that exploits
this intersecting structure to train goal-directed policies that generalize to
unseen start and goal state combinations. In the first stage of GTI, we train a
stochastic policy that leverages trajectory intersections to have the capacity
to compose behaviors from different demonstration trajectories together. In the
second stage of GTI, we collect a small set of rollouts from the unconditioned
stochastic policy of the first stage, and train a goal-directed agent to
generalize to novel start and goal configurations. We validate GTI in both
simulated domains and a challenging long-horizon robotic manipulation domain in
the real world. Additional results and videos are available at
https://sites.google.com/view/gti2020/ . | [
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] |
In solving real-world problems like changing healthcare-seeking behaviors,
designing interventions to improve downstream outcomes requires an
understanding of the causal links within the system. Causal Bayesian Networks
(BN) have been proposed as one such powerful method. In real-world
applications, however, confidence in the results of BNs are often moderate at
best. This is due in part to the inability to validate against some ground
truth, as the DAG is not available. This is especially problematic if the
learned DAG conflicts with pre-existing domain doctrine. At the policy level,
one must justify insights generated by such analysis, preferably accompanying
them with uncertainty estimation. Here we propose a causal extension to the
datasheet concept proposed by Gebru et al (2018) to include approximate BN
performance expectations for any given dataset. To generate the results for a
prototype Causal Datasheet, we constructed over 30,000 synthetic datasets with
properties mirroring characteristics of real data. We then recorded the results
given by state-of-the-art structure learning algorithms. These results were
used to populate the Causal Datasheet, and recommendations were automatically
generated dependent on expected performance. As a proof of concept, we used our
Causal Datasheet Generation Tool (CDG-T) to assign expected performance
expectations to a maternal health survey we conducted in Uttar Pradesh, India. | [
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With the rapid advances in computing and information technologies,
traditional access control models have become inadequate in terms of capturing
fine-grained, and expressive security requirements of newly emerging
applications. An attribute-based access control (ABAC) model provides a more
flexible approach for addressing the authorization needs of complex and dynamic
systems. While organizations are interested in employing newer authorization
models, migrating to such models pose as a significant challenge. Many
large-scale businesses need to grant authorization to their user populations
that are potentially distributed across disparate and heterogeneous computing
environments. Each of these computing environments may have its own access
control model. The manual development of a single policy framework for an
entire organization is tedious, costly, and error-prone.
In this paper, we present a methodology for automatically learning ABAC
policy rules from access logs of a system to simplify the policy development
process. The proposed approach employs an unsupervised learning-based algorithm
for detecting patterns in access logs and extracting ABAC authorization rules
from these patterns. In addition, we present two policy improvement algorithms,
including rule pruning and policy refinement algorithms to generate a higher
quality mined policy. Finally, we implement a prototype of the proposed
approach to demonstrate its feasibility. | [
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] |
Interest in the field of Explainable Artificial Intelligence has been growing
for decades and has accelerated recently. As Artificial Intelligence models
have become more complex, and often more opaque, with the incorporation of
complex machine learning techniques, explainability has become more critical.
Recently, researchers have been investigating and tackling explainability with
a user-centric focus, looking for explanations to consider trustworthiness,
comprehensibility, explicit provenance, and context-awareness. In this chapter,
we leverage our survey of explanation literature in Artificial Intelligence and
closely related fields and use these past efforts to generate a set of
explanation types that we feel reflect the expanded needs of explanation for
today's artificial intelligence applications. We define each type and provide
an example question that would motivate the need for this style of explanation.
We believe this set of explanation types will help future system designers in
their generation and prioritization of requirements and further help generate
explanations that are better aligned to users' and situational needs. | [
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] |
Bayesian Optimization is the state of the art technique for the optimization
of black boxes, i.e., functions where we do not have access to their analytical
expression nor its gradients, they are expensive to evaluate and its evaluation
is noisy. The most popular application of bayesian optimization is the
automatic hyperparameter tuning of machine learning algorithms, where we obtain
the best configuration of machine learning algorithms by optimizing the
estimation of the generalization error of these algorithms. Despite being
applied with success, bayesian optimization methodologies also have
hyperparameters that need to be configured such as the probabilistic surrogate
model or the acquisition function used. A bad decision over the configuration
of these hyperparameters implies obtaining bad quality results. Typically,
these hyperparameters are tuned by making assumptions of the objective function
that we want to evaluate but there are scenarios where we do not have any prior
information about the objective function. In this paper, we propose a first
attempt over automatic bayesian optimization by exploring several heuristics
that automatically tune the acquisition function of bayesian optimization. We
illustrate the effectiveness of these heurisitcs in a set of benchmark problems
and a hyperparameter tuning problem of a machine learning algorithm. | [
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] |
With the widespread adoption of the quantified self movement, an increasing
number of users rely on mobile applications to monitor their physical activity
through their smartphones. Granting to applications a direct access to sensor
data expose users to privacy risks. Indeed, usually these motion sensor data
are transmitted to analytics applications hosted on the cloud leveraging
machine learning models to provide feedback on their health to users. However,
nothing prevents the service provider to infer private and sensitive
information about a user such as health or demographic attributes.In this
paper, we present DySan, a privacy-preserving framework to sanitize motion
sensor data against unwanted sensitive inferences (i.e., improving privacy)
while limiting the loss of accuracy on the physical activity monitoring (i.e.,
maintaining data utility). To ensure a good trade-off between utility and
privacy, DySan leverages on the framework of Generative Adversarial Network
(GAN) to sanitize the sensor data. More precisely, by learning in a competitive
manner several networks, DySan is able to build models that sanitize motion
data against inferences on a specified sensitive attribute (e.g., gender) while
maintaining a high accuracy on activity recognition. In addition, DySan
dynamically selects the sanitizing model which maximize the privacy according
to the incoming data. Experiments conducted on real datasets demonstrate that
DySan can drasticallylimit the gender inference to 47% while only reducing the
accuracy of activity recognition by 3%. | [
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] |
In order to increase productivity, capability, and data exploitation,
numerous defense applications are experiencing an integration of
state-of-the-art machine learning and AI into their architectures. Especially
for defense applications, having a human analyst in the loop is of high
interest due to quality control, accountability, and complex subject matter
expertise not readily automated or replicated by AI. However, many applications
are suffering from a very slow transition. This may be in large part due to
lack of trust, usability, and productivity, especially when adapting to
unforeseen classes and changes in mission context. Interactive machine learning
is a newly emerging field in which machine learning implementations are
trained, optimized, evaluated, and exploited through an intuitive
human-computer interface. In this paper, we introduce interactive machine
learning and explain its advantages and limitations within the context of
defense applications. Furthermore, we address several of the shortcomings of
interactive machine learning by discussing how cognitive feedback may inform
features, data, and results in the state of the art. We define the three
techniques by which cognitive feedback may be employed: self reporting,
implicit cognitive feedback, and modeled cognitive feedback. The advantages and
disadvantages of each technique are discussed. | [
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] |
To equip robots with dexterous skills, an effective approach is to first
transfer the desired skill via Learning from Demonstration (LfD), then let the
robot improve it by self-exploration via Reinforcement Learning (RL). In this
paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural
Movement Primitives (ACNMP), that allows efficient policy improvement in novel
environments and effective skill transfer between different agents. This is
achieved through exploiting the latent representation learned by the underlying
Conditional Neural Process (CNP) model, and simultaneous training of the model
with supervised learning (SL) for acquiring the demonstrated trajectories and
via RL for new trajectory discovery. Through simulation experiments, we show
that (i) ACNMP enables the system to extrapolate to situations where pure LfD
fails; (ii) Simultaneous training of the system through SL and RL preserves the
shape of demonstrations while adapting to novel situations due to the shared
representations used by both learners; (iii) ACNMP enables order-of-magnitude
sample-efficient RL in extrapolation of reaching tasks compared to the existing
approaches; (iv) ACNMPs can be used to implement skill transfer between robots
having different morphology, with competitive learning speeds and importantly
with less number of assumptions compared to the state-of-the-art approaches.
Finally, we show the real-world suitability of ACNMPs through real robot
experiments that involve obstacle avoidance, pick and place and pouring
actions. | [
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] |
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a
pandemic by the World Health Organization, which has reported over 18 million
confirmed cases as of August 5, 2020. In this review, we present an overview of
recent studies using Machine Learning and, more broadly, Artificial
Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified
applications that address challenges posed by COVID-19 at different scales,
including: molecular, by identifying new or existing drugs for treatment;
clinical, by supporting diagnosis and evaluating prognosis based on medical
imaging and non-invasive measures; and societal, by tracking both the epidemic
and the accompanying infodemic using multiple data sources. We also review
datasets, tools, and resources needed to facilitate Artificial Intelligence
research, and discuss strategic considerations related to the operational
implementation of multidisciplinary partnerships and open science. We highlight
the need for international cooperation to maximize the potential of AI in this
and future pandemics. | [
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] |
In this paper, we propose a model-free reinforcement learning method to
synthesize control policies for motion planning problems with continuous states
and actions. The robot is modelled as a labeled discrete-time Markov decision
process (MDP) with continuous state and action spaces. Linear temporal logics
(LTL) are used to specify high-level tasks. We then train deep neural networks
to approximate the value function and policy using an actor-critic
reinforcement learning method. The LTL specification is converted into an
annotated limit-deterministic B\"uchi automaton (LDBA) for continuously shaping
the reward so that dense rewards are available during training. A na\"ive way
of solving a motion planning problem with LTL specifications using
reinforcement learning is to sample a trajectory and then assign a high reward
for training if the trajectory satisfies the entire LTL formula. However, the
sampling complexity needed to find such a trajectory is too high when we have a
complex LTL formula for continuous state and action spaces. As a result, it is
very unlikely that we get enough reward for training if all sample trajectories
start from the initial state in the automata. In this paper, we propose a
method that samples not only an initial state from the state space, but also an
arbitrary state in the automata at the beginning of each training episode. We
test our algorithm in simulation using a car-like robot and find out that our
method can learn policies for different working configurations and LTL
specifications successfully. | [
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] |
Causal inference is at the heart of empirical research in natural and social
sciences and is critical for scientific discovery and informed decision making.
The gold standard in causal inference is performing randomized controlled
trials; unfortunately these are not always feasible due to ethical, legal, or
cost constraints. As an alternative, methodologies for causal inference from
observational data have been developed in statistical studies and social
sciences. However, existing methods critically rely on restrictive assumptions
such as the study population consisting of homogeneous elements that can be
represented in a single flat table, where each row is referred to as a unit. In
contrast, in many real-world settings, the study domain naturally consists of
heterogeneous elements with complex relational structure, where the data is
naturally represented in multiple related tables. In this paper, we present a
formal framework for causal inference from such relational data. We propose a
declarative language called CaRL for capturing causal background knowledge and
assumptions and specifying causal queries using simple Datalog-like rules.CaRL
provides a foundation for inferring causality and reasoning about the effect of
complex interventions in relational domains. We present an extensive
experimental evaluation on real relational data to illustrate the applicability
of CaRL in social sciences and healthcare. | [
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] |
The large integration of variable energy resources is expected to shift a
large part of the energy exchanges closer to real-time, where more accurate
forecasts are available. In this context, the short-term electricity markets
and in particular the intraday market are considered a suitable trading floor
for these exchanges to occur. A key component for the successful renewable
energy sources integration is the usage of energy storage. In this paper, we
propose a novel modelling framework for the strategic participation of energy
storage in the European continuous intraday market where exchanges occur
through a centralized order book. The goal of the storage device operator is
the maximization of the profits received over the entire trading horizon, while
taking into account the operational constraints of the unit. The sequential
decision-making problem of trading in the intraday market is modelled as a
Markov Decision Process. An asynchronous distributed version of the fitted Q
iteration algorithm is chosen for solving this problem due to its sample
efficiency. The large and variable number of the existing orders in the order
book motivates the use of high-level actions and an alternative state
representation. Historical data are used for the generation of a large number
of artificial trajectories in order to address exploration issues during the
learning process. The resulting policy is back-tested and compared against a
benchmark strategy that is the current industrial standard. Results indicate
that the agent converges to a policy that achieves in average higher total
revenues than the benchmark strategy. | [
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] |
The evaluation of constitutive models, especially for high-risk and
high-regret engineering applications, requires efficient and rigorous
third-party calibration, validation and falsification. While there are numerous
efforts to develop paradigms and standard procedures to validate models,
difficulties may arise due to the sequential, manual and often biased nature of
the commonly adopted calibration and validation processes, thus slowing down
data collections, hampering the progress towards discovering new physics,
increasing expenses and possibly leading to misinterpretations of the
credibility and application ranges of proposed models. This work attempts to
introduce concepts from game theory and machine learning techniques to overcome
many of these existing difficulties. We introduce an automated meta-modeling
game where two competing AI agents systematically generate experimental data to
calibrate a given constitutive model and to explore its weakness, in order to
improve experiment design and model robustness through competition. The two
agents automatically search for the Nash equilibrium of the meta-modeling game
in an adversarial reinforcement learning framework without human intervention.
By capturing all possible design options of the laboratory experiments into a
single decision tree, we recast the design of experiments as a game of
combinatorial moves that can be resolved through deep reinforcement learning by
the two competing players. Our adversarial framework emulates idealized
scientific collaborations and competitions among researchers to achieve a
better understanding of the application range of the learned material laws and
prevent misinterpretations caused by conventional AI-based third-party
validation. | [
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] |
AI practitioners typically strive to develop the most accurate systems,
making an implicit assumption that the AI system will function autonomously.
However, in practice, AI systems often are used to provide advice to people in
domains ranging from criminal justice and finance to healthcare. In such
AI-advised decision making, humans and machines form a team, where the human is
responsible for making final decisions. But is the most accurate AI the best
teammate? We argue "No" -- predictable performance may be worth a slight
sacrifice in AI accuracy. Instead, we argue that AI systems should be trained
in a human-centered manner, directly optimized for team performance. We study
this proposal for a specific type of human-AI teaming, where the human overseer
chooses to either accept the AI recommendation or solve the task themselves. To
optimize the team performance for this setting we maximize the team's expected
utility, expressed in terms of the quality of the final decision, cost of
verifying, and individual accuracies of people and machines. Our experiments
with linear and non-linear models on real-world, high-stakes datasets show that
the most accuracy AI may not lead to highest team performance and show the
benefit of modeling teamwork during training through improvements in expected
team utility across datasets, considering parameters such as human skill and
the cost of mistakes. We discuss the shortcoming of current optimization
approaches beyond well-studied loss functions such as log-loss, and encourage
future work on AI optimization problems motivated by human-AI collaboration. | [
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] |
Detection of anomalous behaviors in data centers is crucial to predictive
maintenance and data safety. With data centers, we mean any computer network
that allows users to transmit and exchange data and information. In particular,
we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics
(INFN), which supports the high-energy physics experiments at the Large Hadron
Collider (LHC) in Geneva. The center provides resources and services needed for
data processing, storage, analysis, and distribution. Log records in the data
center is a stochastic and non-stationary phenomenon in nature. We propose a
real-time approach to monitor and classify log records based on sliding time
windows, and a time-varying evolving fuzzy-rule-based classification model. The
most frequent log pattern according to a control chart is taken as the normal
system status. We extract attributes from time windows to gradually develop and
update an evolving Gaussian Fuzzy Classifier (eGFC) on the fly. The real-time
anomaly monitoring system has to provide encouraging results in terms of
accuracy, compactness, and real-time operation. | [
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We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a
stable rule learning algorithm which takes the form of a short and simple list
of rules. State-of-the-art learning algorithms are often referred to as "black
boxes" because of the high number of operations involved in their prediction
process. Despite their powerful predictivity, this lack of interpretability may
be highly restrictive for applications with critical decisions at stake. On the
other hand, algorithms with a simple structure-typically decision trees, rule
algorithms, or sparse linear models-are well known for their instability. This
undesirable feature makes the conclusions of the data analysis unreliable and
turns out to be a strong operational limitation. This motivates the design of
SIRUS, which combines a simple structure with a remarkable stable behavior when
data is perturbed. The algorithm is based on random forests, the predictive
accuracy of which is preserved. We demonstrate the efficiency of the method
both empirically (through experiments) and theoretically (with the proof of its
asymptotic stability). Our R/C++ software implementation sirus is available
from CRAN. | [
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] |
The primary objective of this report is to determine what influences the
success rates of students who have studied in Colombia, analyzing the Saber 11,
the test done at the last school year, some socioeconomic aspects and comparing
the Saber Pro results with the national average. The problem this faces is to
find what influences success, but it also provides an insight in the countries
education dynamics and predicts one's opportunities to be prosperous. The
opposite situation to the one presented in this paper could be the desertion
levels, in the sense that by detecting what makes someone outstanding, these
factors can say what makes one unsuccessful. The solution proposed to solve
this problem was to implement a CART decision tree algorithm that helps to
predict the probability that a student has of scoring higher than the mean
value, based on different socioeconomic and academic factors, such as the
profession of the parents of the subject parents and the results obtained on
Saber 11. It was discovered that one of the most influential factors is the
score in the Saber 11, on the topic of Social Studies, and that the gender of
the subject is not as influential as it is usually portrayed as. The algorithm
designed provided significant insight into which factors most affect the
probability of success of any given person and if further pursued could be used
in many given situations such as deciding which subject in school should be
given more intensity to and academic curriculum in general. | [
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] |
Averting the effects of anthropogenic climate change requires a transition
from fossil fuels to low-carbon technology. A way to achieve this is to
decarbonize the electricity grid. However, further efforts must be made in
other fields such as transport and heating for full decarbonization. This would
reduce carbon emissions due to electricity generation, and also help to
decarbonize other sources such as automotive and heating by enabling a
low-carbon alternative. Carbon taxes have been shown to be an efficient way to
aid in this transition. In this paper, we demonstrate how to to find optimal
carbon tax policies through a genetic algorithm approach, using the electricity
market agent-based model ElecSim. To achieve this, we use the NSGA-II genetic
algorithm to minimize average electricity price and relative carbon intensity
of the electricity mix. We demonstrate that it is possible to find a range of
carbon taxes to suit differing objectives. Our results show that we are able to
minimize electricity cost to below \textsterling10/MWh as well as carbon
intensity to zero in every case. In terms of the optimal carbon tax strategy,
we found that an increasing strategy between 2020 and 2035 was preferable. Each
of the Pareto-front optimal tax strategies are at least above
\textsterling81/tCO2 for every year. The mean carbon tax strategy was
\textsterling240/tCO2. | [
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As voice-based assistants such as Alexa, Siri, and Google Assistant become
ubiquitous, users increasingly expect to maintain natural and informative
conversations with such systems. However, for an open-domain conversational
system to be coherent and engaging, it must be able to maintain the user's
interest for extended periods, without sounding boring or annoying. In this
paper, we investigate one natural approach to this problem, of modulating
response prosody, i.e., changing the pitch and cadence of the response to
indicate delight, sadness or other common emotions, as well as using
pre-recorded interjections. Intuitively, this approach should improve the
naturalness of the conversation, but attempts to quantify the effects of
prosodic modulation on user satisfaction and engagement remain challenging. To
accomplish this, we report results obtained from a large-scale empirical study
that measures the effects of prosodic modulation on user behavior and
engagement across multiple conversation domains, both immediately after each
turn, and at the overall conversation level. Our results indicate that the
prosody modulation significantly increases both immediate and overall user
satisfaction. However, since the effects vary across different domains, we
verify that prosody modulations do not substitute for coherent, informative
content of the responses. Together, our results provide useful tools and
insights for improving the naturalness of responses in conversational systems. | [
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] |
Predicting user satisfaction in conversational systems has become critical,
as spoken conversational assistants operate in increasingly complex domains.
Online satisfaction prediction (i.e., predicting satisfaction of the user with
the system after each turn) could be used as a new proxy for implicit user
feedback, and offers promising opportunities to create more responsive and
effective conversational agents, which adapt to the user's engagement with the
agent. To accomplish this goal, we propose a conversational satisfaction
prediction model specifically designed for open-domain spoken conversational
agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates
multiple representations of the conversation, namely the conversation history,
utterance and response content, and system- and user-oriented behavioral
signals. We first calibrate ConvSAT performance against state of the art
methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an
online regime, and then evaluate ConvSAT on a large dataset of conversations
with real users, collected as part of the Alexa Prize competition. Our
experimental results show that ConvSAT significantly improves satisfaction
prediction for both offline and online setting on both datasets, compared to
the previously reported state-of-the-art approaches. The insights from our
study can enable more intelligent conversational systems, which could adapt in
real-time to the inferred user satisfaction and engagement. | [
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] |
Exploration in novel settings can be challenging without prior experience in
similar domains. However, humans are able to build on prior experience quickly
and efficiently. Children exhibit this behavior when playing with toys. For
example, given a toy with a yellow and blue door, a child will explore with no
clear objective, but once they have discovered how to open the yellow door,
they will most likely be able to open the blue door much faster. Adults also
exhibit this behavior when entering new spaces such as kitchens. We develop a
method, Contextual Prior Prediction, which provides a means of transferring
knowledge between interactions in similar domains through vision. We develop
agents that exhibit exploratory behavior with increasing efficiency, by
learning visual features that are shared across environments, and how they
correlate to actions. Our problem is formulated as a Contextual Multi-Armed
Bandit where the contexts are images, and the robot has access to a
parameterized action space. Given a novel object, the objective is to maximize
reward with few interactions. A domain which strongly exhibits correlations
between visual features and motion is kinemetically constrained mechanisms. We
evaluate our method on simulated prismatic and revolute joints. | [
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] |
Knowledge Graphs (KGs) extracted from text sources are often noisy and lead
to poor performance in downstream application tasks such as KG-based question
answering.While much of the recent activity is focused on addressing the
sparsity of KGs by using embeddings for inferring new facts, the issue of
cleaning up of noise in KGs through KG refinement task is not as actively
studied. Most successful techniques for KG refinement make use of inference
rules and reasoning over ontologies. Barring a few exceptions, embeddings do
not make use of ontological information, and their performance in KG refinement
task is not well understood. In this paper, we present a KG refinement
framework called IterefinE which iteratively combines the two techniques - one
which uses ontological information and inferences rules, PSL-KGI, and the KG
embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is
able to exploit not only the ontological information to improve the quality of
predictions, but also the power of KG embeddings which (implicitly) perform
longer chains of reasoning. The IterefinE framework, operates in a co-training
mode and results in explicit type-supervised embedding of the refined KG from
PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks
show that the embeddings that we produce are able to reject noisy facts from KG
and at the same time infer higher quality new facts resulting in up to 9%
improvement of overall weighted F1 score | [
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] |
Industry~4.0 (I4.0) standards and standardization frameworks have been
proposed with the goal of \emph{empowering interoperability} in smart
factories. These standards enable the description and interaction of the main
components, systems, and processes inside of a smart factory. Due to the
growing number of frameworks and standards, there is an increasing need for
approaches that automatically analyze the landscape of I4.0 standards.
Standardization frameworks classify standards according to their functions into
layers and dimensions. However, similar standards can be classified differently
across the frameworks, producing, thus, interoperability conflicts among them.
Semantic-based approaches that rely on ontologies and knowledge graphs, have
been proposed to represent standards, known relations among them, as well as
their classification according to existing frameworks. Albeit informative, the
structured modeling of the I4.0 landscape only provides the foundations for
detecting interoperability issues. Thus, graph-based analytical methods able to
exploit knowledge encoded by these approaches, are required to uncover
alignments among standards. We study the relatedness among standards and
frameworks based on community analysis to discover knowledge that helps to cope
with interoperability conflicts between standards. We use knowledge graph
embeddings to automatically create these communities exploiting the meaning of
the existing relationships. In particular, we focus on the identification of
similar standards, i.e., communities of standards, and analyze their properties
to detect unknown relations. We empirically evaluate our approach on a
knowledge graph of I4.0 standards using the Trans$^*$ family of embedding
models for knowledge graph entities. Our results are promising and suggest that
relations among standards can be detected accurately. | [
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] |
In many real-world scenarios, decision makers seek to efficiently optimize
multiple competing objectives in a sample-efficient fashion. Multi-objective
Bayesian optimization (BO) is a common approach, but many of the
best-performing acquisition functions do not have known analytic gradients and
suffer from high computational overhead. We leverage recent advances in
programming models and hardware acceleration for multi-objective BO using
Expected Hypervolume Improvement (EHVI)---an algorithm notorious for its high
computational complexity. We derive a novel formulation of q-Expected
Hypervolume Improvement (qEHVI), an acquisition function that extends EHVI to
the parallel, constrained evaluation setting. qEHVI is an exact computation of
the joint EHVI of q new candidate points (up to Monte-Carlo (MC) integration
error). Whereas previous EHVI formulations rely on gradient-free acquisition
optimization or approximated gradients, we compute exact gradients of the MC
estimator via auto-differentiation, thereby enabling efficient and effective
optimization using first-order and quasi-second-order methods. Our empirical
evaluation demonstrates that qEHVI is computationally tractable in many
practical scenarios and outperforms state-of-the-art multi-objective BO
algorithms at a fraction of their wall time. | [
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Policy and guideline proposals for ethical artificial-intelligence research
have proliferated in recent years. These are supposed to guide the
socially-responsible development of AI for the common good. However, there
typically exist incentives for non-cooperation (i.e., non-adherence to such
policies and guidelines); and, these proposals often lack effective mechanisms
to enforce their own normative claims. The situation just described constitutes
a social dilemma; namely, a situation where no one has an individual incentive
to cooperate, though mutual cooperation would lead to the best outcome for all
involved. In this paper, we use stochastic evolutionary game dynamics to model
this social dilemma in the context of the ethical development of artificial
intelligence. This formalism allows us to isolate variables that may be
intervened upon, thus providing actionable suggestions for increased
cooperation amongst numerous stakeholders in AI. Our results show how
stochastic effects can help make cooperation viable in such a scenario. They
suggest that coordination for a common good should be attempted in smaller
groups in which the cost for cooperation is low, and the perceived risk of
failure is high. This provides insight into the conditions under which we
should expect such ethics proposals to be successful with regard to their
scope, scale, and content. | [
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] |
Parsing natural language to corresponding SQL (NL2SQL) with data driven
approaches like deep neural networks attracts much attention in recent years.
Existing NL2SQL datasets assume that condition values should appear exactly in
natural language questions and the queries are answerable given the table.
However, these assumptions may fail in practical scenarios, because user may
use different expressions for the same content in the table, and query
information outside the table without the full picture of contents in table.
Therefore we present TableQA, a large-scale cross-domain Natural Language to
SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique
SQL queries on over 6,000 tables. Different from exisiting NL2SQL datasets,
TableQA requires to generalize well not only to SQL skeletons of different
questions and table schemas, but also to the various expressions for condition
values. Experiment results show that the state-of-the-art model with 95.1%
condition value accuracy on WikiSQL only gets 46.8% condition value accuracy
and 43.0% logic form accuracy on TableQA, indicating the proposed dataset is
challenging and necessary to handle. Two table-aware approaches are proposed to
alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4%
accuracy on the condition value and logic form tasks, with improvement of 4.7%
and 3.4% respectively. | [
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] |
For an intelligent agent to flexibly and efficiently operate in complex
environments, they must be able to reason at multiple levels of temporal,
spatial, and conceptual abstraction. At the lower levels, the agent must
interpret their proprioceptive inputs and control their muscles, and at the
higher levels, the agent must select goals and plan how they will achieve those
goals. It is clear that each of these types of reasoning is amenable to
different types of representations, algorithms, and inputs. In this work, we
introduce a simple, three-level hierarchical architecture that reflects these
distinctions. The low-level controller operates on the continuous
proprioceptive inputs, using model-free learning to acquire useful behaviors.
These in turn induce a set of mid-level dynamics, which are learned by the
mid-level controller and used for model-predictive control, to select a
behavior to activate at each timestep. The high-level controller leverages a
discrete, graph representation for goal selection and path planning to specify
targets for the mid-level controller. We apply our method to a series of
navigation tasks in the Mujoco Ant environment, consistently demonstrating
significant improvements in sample-efficiency compared to prior model-free,
model-based, and hierarchical RL methods. Finally, as an illustrative example
of the advantages of our architecture, we apply our method to a complex maze
environment that requires efficient exploration and long-horizon planning. | [
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] |
Measuring user satisfaction level is a challenging task, and a critical
component in developing large-scale conversational agent systems serving the
needs of real users. An widely used approach to tackle this is to collect human
annotation data and use them for evaluation or modeling. Human annotation based
approaches are easier to control, but hard to scale. A novel alternative
approach is to collect user's direct feedback via a feedback elicitation system
embedded to the conversational agent system, and use the collected user
feedback to train a machine-learned model for generalization. User feedback is
the best proxy for user satisfaction, but is not available for some ineligible
intents and certain situations. Thus, these two types of approaches are
complementary to each other. In this work, we tackle the user satisfaction
assessment problem with a hybrid approach that fuses explicit user feedback,
user satisfaction predictions inferred by two machine-learned models, one
trained on user feedback data and the other human annotation data. The hybrid
approach is based on a waterfall policy, and the experimental results with
Amazon Alexa's large-scale datasets show significant improvements in inferring
user satisfaction. A detailed hybrid architecture, an in-depth analysis on user
feedback data, and an algorithm that generates data sets to properly simulate
the live traffic are presented in this paper. | [
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] |
We are interested in the autonomous acquisition of repertoires of skills.
Language-conditioned reinforcement learning (LC-RL) approaches are great tools
in this quest, as they allow to express abstract goals as sets of constraints
on the states. However, most LC-RL agents are not autonomous and cannot learn
without external instructions and feedback. Besides, their direct language
condition cannot account for the goal-directed behavior of pre-verbal infants
and strongly limits the expression of behavioral diversity for a given language
input. To resolve these issues, we propose a new conceptual approach to
language-conditioned RL: the Language-Goal-Behavior architecture (LGB). LGB
decouples skill learning and language grounding via an intermediate semantic
representation of the world. To showcase the properties of LGB, we present a
specific implementation called DECSTR. DECSTR is an intrinsically motivated
learning agent endowed with an innate semantic representation describing
spatial relations between physical objects. In a first stage (G -> B), it
freely explores its environment and targets self-generated semantic
configurations. In a second stage (L -> G), it trains a language-conditioned
goal generator to generate semantic goals that match the constraints expressed
in language-based inputs. We showcase the additional properties of LGB w.r.t.
both an end-to-end LC-RL approach and a similar approach leveraging
non-semantic, continuous intermediate representations. Intermediate semantic
representations help satisfy language commands in a diversity of ways, enable
strategy switching after a failure and facilitate language grounding. | [
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] |
Clinical predictions using clinical data by computational methods are common
in bioinformatics. However, clinical predictions using information from
genomics datasets as well is not a frequently observed phenomenon in research.
Precision medicine research requires information from all available datasets to
provide intelligent clinical solutions. In this paper, we have attempted to
create a prediction model which uses information from both clinical and
genomics datasets. We have demonstrated multiclass disease predictions based on
combined clinical and genomics datasets using machine learning methods. We have
created an integrated dataset, using a clinical (ClinVar) and a genomics (gene
expression) dataset, and trained it using instance-based learner to predict
clinical diseases. We have used an innovative but simple way for multiclass
classification, where the number of output classes is as high as 75. We have
used Principal Component Analysis for feature selection. The classifier
predicted diseases with 73\% accuracy on the integrated dataset. The results
were consistent and competent when compared with other classification models.
The results show that genomics information can be reliably included in datasets
for clinical predictions and it can prove to be valuable in clinical
diagnostics and precision medicine. | [
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] |
Path Following and Collision Avoidance, be it for unmanned surface vessels or
other autonomous vehicles, are two fundamental guidance problems in robotics.
For many decades, they have been subject to academic study, leading to a vast
number of proposed approaches. However, they have mostly been treated as
separate problems, and have typically relied on non-linear first-principles
models with parameters that can only be determined experimentally. The rise of
Deep Reinforcement Learning (DRL) in recent years suggests an alternative
approach: end-to-end learning of the optimal guidance policy from scratch by
means of a trial-and-error based approach. In this article, we explore the
potential of Proximal Policy Optimization (PPO), a DRL algorithm with
demonstrated state-of-the-art performance on Continuous Control tasks, when
applied to the dual-objective problem of controlling an underactuated
Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows
an a priori known desired path while avoiding collisions with other vessels
along the way. Based on high-fidelity elevation and AIS tracking data from the
Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's
performance in challenging, dynamic real-world scenarios where the ultimate
success of the agent rests upon its ability to navigate non-uniform marine
terrain while handling challenging, but realistic vessel encounters. | [
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] |
Modern large-scale systems such as recommender system and online advertising
system are built upon computation-intensive infrastructure. The typical
objective in these applications is to maximize the total revenue, e.g.
GMV~(Gross Merchandise Volume), under a limited computation resource. Usually,
the online serving system follows a multi-stage cascade architecture, which
consists of several stages including retrieval, pre-ranking, ranking, etc.
These stages usually allocate resource manually with specific computing power
budgets, which requires the serving configuration to adapt accordingly. As a
result, the existing system easily falls into suboptimal solutions with respect
to maximizing the total revenue. The limitation is due to the face that,
although the value of traffic requests vary greatly, online serving system
still spends equal computing power among them.
In this paper, we introduce a novel idea that online serving system could
treat each traffic request differently and allocate "personalized" computation
resource based on its value. We formulate this resource allocation problem as a
knapsack problem and propose a Dynamic Computation Allocation Framework~(DCAF).
Under some general assumptions, DCAF can theoretically guarantee that the
system can maximize the total revenue within given computation budget. DCAF
brings significant improvement and has been deployed in the display advertising
system of Taobao for serving the main traffic. With DCAF, we are able to
maintain the same business performance with 20\% computation resource
reduction. | [
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] |
Automating algorithm configuration is growing increasingly necessary as
algorithms come with more and more tunable parameters. It is common to tune
parameters using machine learning, optimizing performance metrics such as
runtime and solution quality. The training set consists of problem instances
from the specific domain at hand. We investigate a fundamental question about
these techniques: how large should the training set be to ensure that a
parameter's average empirical performance over the training set is close to its
expected, future performance? We answer this question for algorithm
configuration problems that exhibit a widely-applicable structure: the
algorithm's performance as a function of its parameters can be approximated by
a "simple" function. We show that if this approximation holds under the
L-infinity norm, we can provide strong sample complexity bounds. On the flip
side, if the approximation holds only under the L-p norm for p smaller than
infinity, it is not possible to provide meaningful sample complexity bounds in
the worst case. We empirically evaluate our bounds in the context of integer
programming, one of the most powerful tools in computer science. Via
experiments, we obtain sample complexity bounds that are up to 700 times
smaller than the previously best-known bounds. | [
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] |
Commercial companies that collect user data on a large scale have been the
main beneficiaries of this trend since the success of deep learning techniques
is directly proportional to the amount of data available for training. Massive
data collection required for deep learning presents obvious privacy issues.
Users personal, highly sensitive data such as photos and voice recordings are
kept indefinitely by the companies that collect it. Users can neither delete it
nor restrict the purposes for which it is used. So, data privacy has been a
very important concern for governments and companies these days. It gives rise
to a very interesting challenge since on the one hand, we are pushing further
and further for high-quality models and accessible data, but on the other hand,
we need to keep data safe from both intentional and accidental leakage. The
more personal the data is it is more restricted it means some of the most
important social issues cannot be addressed using machine learning because
researchers do not have access to proper training data. But by learning how to
machine learning that protects privacy we can make a huge difference in solving
many social issues like curing disease etc. Deep neural networks are
susceptible to various inference attacks as they remember information about
their training data. In this chapter, we introduce differential privacy, which
ensures that different kinds of statistical analyses dont compromise privacy
and federated learning, training a machine learning model on a data to which we
do not have access to. | [
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In countries that enabled patients to choose their own providers, a common
problem is that the patients did not make rational decisions, and hence, fail
to use healthcare resources efficiently. This might cause problems such as
overwhelming tertiary facilities with mild condition patients, thus limiting
their capacity of treating acute and critical patients. To address such
maldistributed patient volume, it is essential to oversee patients choices
before further evaluation of a policy or resource allocation. This study used
nationwide insurance data, accumulated possible features discussed in existing
literature, and used a deep neural network to predict the patients choices of
hospital levels. This study also used explainable artificial intelligence
methods to interpret the contribution of features for the general public and
individuals. In addition, we explored the effectiveness of changing data
representations. The results showed that the model was able to predict with
high area under the receiver operating characteristics curve (AUC) (0.90),
accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly
imbalanced label. Generally, social approval of the provider by the general
public (positive or negative) and the number of practicing physicians serving
per ten thousand people of the located area are listed as the top effecting
features. The changing data representation had a positive effect on the
prediction improvement. Deep learning methods can process highly imbalanced
data and achieve high accuracy. The effecting features affect the general
public and individuals differently. Addressing the sparsity and discrete nature
of insurance data leads to better prediction. Applications using deep learning
technology are promising in health policy making. More work is required to
interpret models and practice implementation. | [
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] |
In the past decades, we have witnessed significant progress in the domain of
autonomous driving. Advanced techniques based on optimization and reinforcement
learning (RL) become increasingly powerful at solving the forward problem:
given designed reward/cost functions, how should we optimize them and obtain
driving policies that interact with the environment safely and efficiently.
Such progress has raised another equally important question: \emph{what should
we optimize}? Instead of manually specifying the reward functions, it is
desired that we can extract what human drivers try to optimize from real
traffic data and assign that to autonomous vehicles to enable more naturalistic
and transparent interaction between humans and intelligent agents. To address
this issue, we present an efficient sampling-based maximum-entropy inverse
reinforcement learning (IRL) algorithm in this paper. Different from existing
IRL algorithms, by introducing an efficient continuous-domain trajectory
sampler, the proposed algorithm can directly learn the reward functions in the
continuous domain while considering the uncertainties in demonstrated
trajectories from human drivers. We evaluate the proposed algorithm on real
driving data, including both non-interactive and interactive scenarios. The
experimental results show that the proposed algorithm achieves more accurate
prediction performance with faster convergence speed and better generalization
compared to other baseline IRL algorithms. | [
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] |
Reward functions are a common way to specify the objective of a robot. As
designing reward functions can be extremely challenging, a more promising
approach is to directly learn reward functions from human teachers.
Importantly, data from human teachers can be collected either passively or
actively in a variety of forms: passive data sources include demonstrations,
(e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings)
are actively elicited. Prior research has independently applied reward learning
to these different data sources. However, there exist many domains where
multiple sources are complementary and expressive. Motivated by this general
problem, we present a framework to integrate multiple sources of information,
which are either passively or actively collected from human users. In
particular, we present an algorithm that first utilizes user demonstrations to
initialize a belief about the reward function, and then actively probes the
user with preference queries to zero-in on their true reward. This algorithm
not only enables us combine multiple data sources, but it also informs the
robot when it should leverage each type of information. Further, our approach
accounts for the human's ability to provide data: yielding user-friendly
preference queries which are also theoretically optimal. Our extensive
simulated experiments and user studies on a Fetch mobile manipulator
demonstrate the superiority and the usability of our integrated framework. | [
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] |
People can learn rich, general-purpose conceptual representations from only
raw perceptual inputs. Current machine learning approaches fall well short of
these human standards, although different modeling traditions often have
complementary strengths. Symbolic models can capture the compositional and
causal knowledge that enables flexible generalization, but they struggle to
learn from raw inputs, relying on strong abstractions and simplifying
assumptions. Neural network models can learn directly from raw data, but they
struggle to capture compositional and causal structure and typically must
retrain to tackle new tasks. We bring together these two traditions to learn
generative models of concepts that capture rich compositional and causal
structure, while learning from raw data. We develop a generative neuro-symbolic
(GNS) model of handwritten character concepts that uses the control flow of a
probabilistic program, coupled with symbolic stroke primitives and a symbolic
image renderer, to represent the causal and compositional processes by which
characters are formed. The distributions of parts (strokes), and correlations
between parts, are modeled with neural network subroutines, allowing the model
to learn directly from raw data and express nonparametric statistical
relationships. We apply our model to the Omniglot challenge of human-level
concept learning, using a background set of alphabets to learn an expressive
prior distribution over character drawings. In a subsequent evaluation, our GNS
model uses probabilistic inference to learn rich conceptual representations
from a single training image that generalize to 4 unique tasks, succeeding
where previous work has fallen short. | [
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] |
One difficulty in using artificial agents for human-assistive applications
lies in the challenge of accurately assisting with a person's goal(s). Existing
methods tend to rely on inferring the human's goal, which is challenging when
there are many potential goals or when the set of candidate goals is difficult
to identify. We propose a new paradigm for assistance by instead increasing the
human's ability to control their environment, and formalize this approach by
augmenting reinforcement learning with human empowerment. This task-agnostic
objective preserves the person's autonomy and ability to achieve any eventual
state. We test our approach against assistance based on goal inference,
highlighting scenarios where our method overcomes failure modes stemming from
goal ambiguity or misspecification. As existing methods for estimating
empowerment in continuous domains are computationally hard, precluding its use
in real time learned assistance, we also propose an efficient
empowerment-inspired proxy metric. Using this, we are able to successfully
demonstrate our method in a shared autonomy user study for a challenging
simulated teleoperation task with human-in-the-loop training. | [
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] |
Many systems have been developed in recent years to mine logical rules from
large-scale Knowledge Graphs (KGs), on the grounds that representing
regularities as rules enables both the interpretable inference of new facts,
and the explanation of known facts. Among these systems, the walk-based methods
that generate the instantiated rules containing constants by abstracting
sampled paths in KGs demonstrate strong predictive performance and
expressivity. However, due to the large volume of possible rules, these systems
do not scale well where computational resources are often wasted on generating
and evaluating unpromising rules. In this work, we address such scalability
issues by proposing new methods for pruning unpromising rules using rule
hierarchies. The approach consists of two phases. Firstly, since rule
hierarchies are not readily available in walk-based methods, we have built a
Rule Hierarchy Framework (RHF), which leverages a collection of subsumption
frameworks to build a proper rule hierarchy from a set of learned rules. And
secondly, we adapt RHF to an existing rule learner where we design and
implement two methods for Hierarchical Pruning (HPMs), which utilize the
generated hierarchies to remove irrelevant and redundant rules. Through
experiments over four public benchmark datasets, we show that the application
of HPMs is effective in removing unpromising rules, which leads to significant
reductions in the runtime as well as in the number of learned rules, without
compromising the predictive performance. | [
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] |
Block-based visual programming environments play a critical role in
introducing computing concepts to K-12 students. One of the key pedagogical
challenges in these environments is in designing new practice tasks for a
student that match a desired level of difficulty and exercise specific
programming concepts. In this paper, we formalize the problem of synthesizing
visual programming tasks. In particular, given a reference visual task $\rm
T^{in}$ and its solution code $\rm C^{in}$, we propose a novel methodology to
automatically generate a set $\{(\rm T^{out}, \rm C^{out})\}$ of new tasks
along with solution codes such that tasks $\rm T^{in}$ and $\rm T^{out}$ are
conceptually similar but visually dissimilar. Our methodology is based on the
realization that the mapping from the space of visual tasks to their solution
codes is highly discontinuous; hence, directly mutating reference task $\rm
T^{in}$ to generate new tasks is futile. Our task synthesis algorithm operates
by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$.
Then, the algorithm performs symbolic execution over a code $\rm C^{out}$ to
obtain a visual task $\rm T^{out}$; this step uses the Monte Carlo Tree Search
(MCTS) procedure to guide the search in the symbolic tree. We demonstrate the
effectiveness of our algorithm through an extensive empirical evaluation and
user study on reference tasks taken from the \emph{Hour of Code: Classic Maze}
challenge by \emph{Code.org} and the \emph{Intro to Programming with Karel}
course by \emph{CodeHS.com}. | [
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] |
Knowledge tracing (KT) which aims at predicting learner's knowledge mastery
plays an important role in the computer-aided educational system. In recent
years, many deep learning models have been applied to tackle the KT task, which
have shown promising results. However, limitations still exist. Most existing
methods simplify the exercising records as knowledge sequences, which fail to
explore rich information that existed in exercises. Besides, the existing
diagnosis results of knowledge tracing are not convincing enough since they
neglect prior relations between exercises. To solve the above problems, we
propose a hierarchical graph knowledge tracing model called HGKT to explore the
latent hierarchical relations between exercises. Specifically, we introduce the
concept of problem schema to construct a hierarchical exercise graph that could
model the exercise learning dependencies. Moreover, we employ two attention
mechanisms to highlight the important historical states of learners. In the
testing stage, we present a K&S diagnosis matrix that could trace the
transition of mastery of knowledge and problem schema, which can be more easily
applied to different applications. Extensive experiments show the effectiveness
and interpretability of our proposed models. | [
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] |
Given a set of ideas collected from crowds with regard to an open-ended
question, how can we organize and prioritize them in order to determine the
preferred ones based on preference comparisons by crowd evaluators? As there
are diverse latent criteria for the value of an idea, multiple ideas can be
considered as "the best". In addition, evaluators can have different preference
criteria, and their comparison results often disagree.
In this paper, we propose an analysis method for obtaining a subset of ideas,
which we call frontier ideas, that are the best in terms of at least one latent
evaluation criterion. We propose an approach, called CrowDEA, which estimates
the embeddings of the ideas in the multiple-criteria preference space, the best
viewpoint for each idea, and preference criterion for each evaluator, to obtain
a set of frontier ideas. Experimental results using real datasets containing
numerous ideas or designs demonstrate that the proposed approach can
effectively prioritize ideas from multiple viewpoints, thereby detecting
frontier ideas. The embeddings of ideas learned by the proposed approach
provide a visualization that facilitates observation of the frontier ideas. In
addition, the proposed approach prioritizes ideas from a wider variety of
viewpoints, whereas the baselines tend to use to the same viewpoints; it can
also handle various viewpoints and prioritize ideas in situations where only a
limited number of evaluators or labels are available. | [
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] |
This paper investigates the optimization problem of an infinite stage
discrete time Markov decision process (MDP) with a long-run average metric
considering both mean and variance of rewards together. Such performance metric
is important since the mean indicates average returns and the variance
indicates risk or fairness. However, the variance metric couples the rewards at
all stages, the traditional dynamic programming is inapplicable as the
principle of time consistency fails. We study this problem from a new
perspective called the sensitivity-based optimization theory. A performance
difference formula is derived and it can quantify the difference of the
mean-variance combined metrics of MDPs under any two different policies. The
difference formula can be utilized to generate new policies with strictly
improved mean-variance performance. A necessary condition of the optimal policy
and the optimality of deterministic policies are derived. We further develop an
iterative algorithm with a form of policy iteration, which is proved to
converge to local optima both in the mixed and randomized policy space.
Specially, when the mean reward is constant in policies, the algorithm is
guaranteed to converge to the global optimum. Finally, we apply our approach to
study the fluctuation reduction of wind power in an energy storage system,
which demonstrates the potential applicability of our optimization method. | [
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This work adds on to the on-going efforts to provide more autonomy to space
robots. Here the concept of programming by demonstration or imitation learning
is used for trajectory planning of manipulators mounted on small spacecraft.
For greater autonomy in future space missions and minimal human intervention
through ground control, a robot arm having 7-Degrees of Freedom (DoF) is
envisaged for carrying out multiple tasks like debris removal, on-orbit
servicing and assembly. Since actual hardware implementation of microgravity
environment is extremely expensive, the demonstration data for trajectory
learning is generated using a model predictive controller (MPC) in a physics
based simulator. The data is then encoded compactly by Probabilistic Movement
Primitives (ProMPs). This offline trajectory learning allows faster
reproductions and also avoids any computationally expensive optimizations after
deployment in a space environment. It is shown that the probabilistic
distribution can be used to generate trajectories to previously unseen
situations by conditioning the distribution. The motion of the robot (or
manipulator) arm induces reaction forces on the spacecraft hub and hence its
attitude changes prompting the Attitude Determination and Control System (ADCS)
to take large corrective action that drains energy out of the system. By having
a robot arm with redundant DoF helps in finding several possible trajectories
from the same start to the same target. This allows the ProMP trajectory
generator to sample out the trajectory which is obstacle free as well as having
minimal attitudinal disturbances thereby reducing the load on ADCS. | [
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] |
Massive multiple-input multiple-output (MIMO) communication systems have a
huge potential both in terms of data rate and energy efficiency, although
channel estimation becomes challenging for a large number of antennas. Using a
physical model allows to ease the problem by injecting a priori information
based on the physics of propagation. However, such a model rests on simplifying
assumptions and requires to know precisely the configuration of the system,
which is unrealistic in practice.In this paper we present mpNet, an unfolded
neural network specifically designed for massive MIMO channel estimation. It is
trained online in an unsupervised way. Moreover, mpNet is computationally
efficient and automatically adapts its depth to the signal-to-noise ratio
(SNR). The method we propose adds flexibility to physical channel models by
allowing a base station (BS) to automatically correct its channel estimation
algorithm based on incoming data, without the need for a separate offline
training phase.It is applied to realistic millimeter wave channels and shows
great performance, achieving a channel estimation error almost as low as one
would get with a perfectly calibrated system. It also allows incident detection
and automatic correction, making the BS resilient and able to automatically
adapt to changes in its environment. | [
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A core task in process mining is process discovery which aims to learn an
accurate process model from event log data. In this paper, we propose to use
(block-) structured programs directly as target process models so as to
establish connections to the field of program synthesis and facilitate the
translation from abstract process models to executable processes, e.g., for
robotic process automation. Furthermore, we develop a novel bottom-up
agglomerative approach to the discovery of such structured program process
models. In comparison with the popular top-down recursive inductive miner, our
proposed agglomerative miner enjoys the similar theoretical guarantee to
produce sound process models (without deadlocks and other anomalies) while
exhibiting some advantages like avoiding silent activities and accommodating
duplicate activities. The proposed algorithm works by iteratively applying a
few graph rewriting rules to the directly-follows-graph of activities. For
real-world (sparse) directly-follows-graphs, the algorithm has quadratic
computational complexity with respect to the number of distinct activities. To
our knowledge, this is the first process discovery algorithm that is made for
the purpose of program synthesis. Experiments on the BPI-Challenge 2020 dataset
and the Karel programming dataset have demonstrated that our proposed algorithm
can outperform the inductive miner not only according to the traditional
process discovery metrics but also in terms of the effectiveness in finding out
the true underlying structured program from a small number of its execution
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Creativity is a deeply debated topic, as this concept is arguably
quintessential to our humanity. Across different epochs, it has been infused
with an extensive variety of meanings relevant to that era. Along these, the
evolution of technology have provided a plurality of novel tools for creative
purposes. Recently, the advent of Artificial Intelligence (AI), through deep
learning approaches, have seen proficient successes across various
applications. The use of such technologies for creativity appear in a natural
continuity to the artistic trend of this century. However, the aura of a
technological artefact labeled as intelligent has unleashed passionate and
somewhat unhinged debates on its implication for creative endeavors. In this
paper, we aim to provide a new perspective on the question of creativity at the
era of AI, by blurring the frontier between social and computational sciences.
To do so, we rely on reflections from social science studies of creativity to
view how current AI would be considered through this lens. As creativity is a
highly context-prone concept, we underline the limits and deficiencies of
current AI, requiring to move towards artificial creativity. We argue that the
objective of trying to purely mimic human creative traits towards a
self-contained ex-nihilo generative machine would be highly counterproductive,
putting us at risk of not harnessing the almost unlimited possibilities offered
by the sheer computational power of artificial agents. | [
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