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When searching for objects in cluttered environments, it is often necessary
to perform complex interactions in order to move occluding objects out of the
way and fully reveal the object of interest and make it graspable. Due to the
complexity of the physics involved and the lack of accurate models of the
clutter, planning and controlling precise predefined interactions with accurate
outcome is extremely hard, when not impossible. In problems where accurate
(forward) models are lacking, Deep Reinforcement Learning (RL) has shown to be
a viable solution to map observations (e.g. images) to good interactions in the
form of close-loop visuomotor policies. However, Deep RL is sample inefficient
and fails when applied directly to the problem of unoccluding objects based on
images. In this work we present a novel Deep RL procedure that combines i)
teacher-aided exploration, ii) a critic with privileged information, and iii)
mid-level representations, resulting in sample efficient and effective learning
for the problem of uncovering a target object occluded by a heap of unknown
objects. Our experiments show that our approach trains faster and converges to
more efficient uncovering solutions than baselines and ablations, and that our
uncovering policies lead to an average improvement in the graspability of the
target object, facilitating downstream retrieval applications. | [
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] |
Relevant research has been highlighted in the computing community to develop
machine learning models capable of predicting the occurrence of crimes,
analyzing contexts of crimes, extracting profiles of individuals linked to
crime, and analyzing crimes over time. However, models capable of predicting
specific crimes, such as homicide, are not commonly found in the current
literature. This research presents a machine learning model to predict homicide
crimes, using a dataset that uses generic data (without study location
dependencies) based on incident report records for 34 different types of
crimes, along with time and space data from crime reports. Experimentally, data
from the city of Bel\'em - Par\'a, Brazil was used. These data were transformed
to make the problem generic, enabling the replication of this model to other
locations. In the research, analyses were performed with simple and robust
algorithms on the created dataset. With this, statistical tests were performed
with 11 different classification methods and the results are related to the
prediction's occurrence and non-occurrence of homicide crimes in the month
subsequent to the occurrence of other registered crimes, with 76% assertiveness
for both classes of the problem, using Random Forest. Results are considered as
a baseline for the proposed problem. | [
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] |
Autonomous car racing is a major challenge in robotics. It raises fundamental
problems for classical approaches such as planning minimum-time trajectories
under uncertain dynamics and controlling the car at the limits of its handling.
Besides, the requirement of minimizing the lap time, which is a sparse
objective, and the difficulty of collecting training data from human experts
have also hindered researchers from directly applying learning-based approaches
to solve the problem. In the present work, we propose a learning-based system
for autonomous car racing by leveraging a high-fidelity physical car
simulation, a course-progress proxy reward, and deep reinforcement learning. We
deploy our system in Gran Turismo Sport, a world-leading car simulator known
for its realistic physics simulation of different race cars and tracks, which
is even used to recruit human race car drivers. Our trained policy achieves
autonomous racing performance that goes beyond what had been achieved so far by
the built-in AI, and, at the same time, outperforms the fastest driver in a
dataset of over 50,000 human players. | [
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] |
Domain-specific intelligent systems are meant to help system users in their
decision-making process. Many systems aim to simultaneously support different
users with varying levels of domain expertise, but prior domain knowledge can
affect user trust and confidence in detecting system errors. While it is also
known that user trust can be influenced by first impressions with intelligent
systems, our research explores the relationship between ordering bias and
domain expertise when encountering errors in intelligent systems. In this
paper, we present a controlled user study to explore the role of domain
knowledge in establishing trust and susceptibility to the influence of first
impressions on user trust. Participants reviewed an explainable image
classifier with a constant accuracy and two different orders of observing
system errors (observing errors in the beginning of usage vs. in the end). Our
findings indicate that encountering errors early-on can cause negative first
impressions for domain experts, negatively impacting their trust over the
course of interactions. However, encountering correct outputs early helps more
knowledgable users to dynamically adjust their trust based on their
observations of system performance. In contrast, novice users suffer from
over-reliance due to their lack of proper knowledge to detect errors. | [
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] |
Ensuring quality of service (QoS) guarantees in service systems is a
challenging task, particularly when the system is composed of more fine-grained
services, such as service function chains. An important QoS metric in service
systems is the end-to-end delay, which becomes even more important in
delay-sensitive applications, where the jobs must be completed within a time
deadline. Admission control is one way of providing end-to-end delay guarantee,
where the controller accepts a job only if it has a high probability of meeting
the deadline. In this paper, we propose a reinforcement learning-based
admission controller that guarantees a probabilistic upper-bound on the
end-to-end delay of the service system, while minimizes the probability of
unnecessary rejections. Our controller only uses the queue length information
of the network and requires no knowledge about the network topology or system
parameters. Since long-term performance metrics are of great importance in
service systems, we take an average-reward reinforcement learning approach,
which is well suited to infinite horizon problems. Our evaluations verify that
the proposed RL-based admission controller is capable of providing
probabilistic bounds on the end-to-end delay of the network, without using
system model information. | [
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] |
We address the problem of counterfactual regression using causal inference
(CI) in observational studies consisting of high dimensional covariates and
high cardinality treatments. Confounding bias, which leads to inaccurate
treatment effect estimation, is attributed to covariates that affect both
treatments and outcome. The presence of high-dimensional co-variates
exacerbates the impact of bias as it is harder to isolate and measure the
impact of these confounders. In the presence of high-cardinality treatment
variables, CI is rendered ill-posed due to the increase in the number of
counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural
network (DNN) based framework for estimating causal effects in the presence of
large number of covariates, and high-cardinal and continuous treatment
variables. The proposed architecture comprises of a decorrelation network and
an outcome prediction network. In the decorrelation network, we learn a data
representation in lower dimensions as compared to the original covariates and
addresses confounding bias alongside. Subsequently, in the outcome prediction
network, we learn an embedding of high-cardinality and continuous treatments,
jointly with the data representation. We demonstrate the efficacy of causal
effect prediction of the proposed Hi-CI network using synthetic and real-world
NEWS datasets. | [
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] |
Machine behavior that is based on learning algorithms can be significantly
influenced by the exposure to data of different qualities. Up to now, those
qualities are solely measured in technical terms, but not in ethical ones,
despite the significant role of training and annotation data in supervised
machine learning. This is the first study to fill this gap by describing new
dimensions of data quality for supervised machine learning applications. Based
on the rationale that different social and psychological backgrounds of
individuals correlate in practice with different modes of
human-computer-interaction, the paper describes from an ethical perspective how
varying qualities of behavioral data that individuals leave behind while using
digital technologies have socially relevant ramification for the development of
machine learning applications. The specific objective of this study is to
describe how training data can be selected according to ethical assessments of
the behavior it originates from, establishing an innovative filter regime to
transition from the big data rationale n = all to a more selective way of
processing data for training sets in machine learning. The overarching aim of
this research is to promote methods for achieving beneficial machine learning
applications that could be widely useful for industry as well as academia. | [
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] |
Modern Reinforcement Learning (RL) algorithms promise to solve difficult
motor control problems directly from raw sensory inputs. Their attraction is
due in part to the fact that they can represent a general class of methods that
allow to learn a solution with a reasonably set reward and minimal prior
knowledge, even in situations where it is difficult or expensive for a human
expert. For RL to truly make good on this promise, however, we need algorithms
and learning setups that can work across a broad range of problems with minimal
problem specific adjustments or engineering. In this paper, we study this idea
of generality in the locomotion domain. We develop a learning framework that
can learn sophisticated locomotion behavior for a wide spectrum of legged
robots, such as bipeds, tripeds, quadrupeds and hexapods, including wheeled
variants. Our learning framework relies on a data-efficient, off-policy
multi-task RL algorithm and a small set of reward functions that are
semantically identical across robots. To underline the general applicability of
the method, we keep the hyper-parameter settings and reward definitions
constant across experiments and rely exclusively on on-board sensing. For nine
different types of robots, including a real-world quadruped robot, we
demonstrate that the same algorithm can rapidly learn diverse and reusable
locomotion skills without any platform specific adjustments or additional
instrumentation of the learning setup. | [
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] |
In this paper, we introduce a novel deep learning based solution to the
Powered-Descent Guidance (PDG) problem, grounded in principles of nonlinear
Stochastic Optimal Control (SOC) and Feynman-Kac theory. Our algorithm solves
the PDG problem by framing it as an $\mathcal{L}^1$ SOC problem for minimum
fuel consumption. Additionally, it can handle practically useful control
constraints, nonlinear dynamics and enforces state constraints as
soft-constraints. This is achieved by building off of recent work on deep
Forward-Backward Stochastic Differential Equations (FBSDEs) and differentiable
non-convex optimization neural-network layers based on stochastic search. In
contrast to previous approaches, our algorithm does not require convexification
of the constraints or linearization of the dynamics and is empirically shown to
be robust to stochastic disturbances and the initial position of the
spacecraft. After training offline, our controller can be activated once the
spacecraft is within a pre-specified radius of the landing zone and at a
pre-specified altitude i.e., the base of an inverted cone with the tip at the
landing zone. We demonstrate empirically that our controller can successfully
and safely land all trajectories initialized at the base of this cone while
minimizing fuel consumption. | [
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] |
We study the computational complexity of two hard problems on determinantal
point processes (DPPs). One is maximum a posteriori (MAP) inference, i.e., to
find a principal submatrix having the maximum determinant. The other is
probabilistic inference on exponentiated DPPs (E-DPPs), which can sharpen or
weaken the diversity preference of DPPs with an exponent parameter $p$. We
present several complexity-theoretic hardness results that explain the
difficulty in approximating MAP inference and the normalizing constant for
E-DPPs. We first prove that unconstrained MAP inference for an $n \times n$
matrix is $\textsf{NP}$-hard to approximate within a factor of $2^{\beta n}$,
where $\beta = 10^{-10^{13}} $. This result improves upon the best-known
inapproximability factor of $(\frac{9}{8}-\epsilon)$, and rules out the
existence of any polynomial-factor approximation algorithm assuming $\textsf{P}
\neq \textsf{NP}$. We then show that log-determinant maximization is
$\textsf{NP}$-hard to approximate within a factor of $\frac{5}{4}$ for the
unconstrained case and within a factor of $1+10^{-10^{13}}$ for the
size-constrained monotone case. In particular, log-determinant maximization
does not admit a polynomial-time approximation scheme unless $\textsf{P} =
\textsf{NP}$. As a corollary of the first result, we demonstrate that the
normalizing constant for E-DPPs of any (fixed) constant exponent $p \geq
\beta^{-1} = 10^{10^{13}}$ is $\textsf{NP}$-hard to approximate within a factor
of $2^{\beta pn}$, which is in contrast to the case of $p \leq 1$ admitting a
fully polynomial-time randomized approximation scheme. | [
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] |
Mobile Edge Learning (MEL) is a collaborative learning paradigm that features
distributed training of Machine Learning (ML) models over edge devices (e.g.,
IoT devices). In MEL, possible coexistence of multiple learning tasks with
different datasets may arise. The heterogeneity in edge devices' capabilities
will require the joint optimization of the learners-orchestrator association
and task allocation. To this end, we aim to develop an energy-efficient
framework for learners-orchestrator association and learning task allocation,
in which each orchestrator gets associated with a group of learners with the
same learning task based on their communication channel qualities and
computational resources, and allocate the tasks accordingly. Therein, a multi
objective optimization problem is formulated to minimize the total energy
consumption and maximize the learning tasks' accuracy. However, solving such
optimization problem requires centralization and the presence of the whole
environment information at a single entity, which becomes impractical in
large-scale systems. To reduce the solution complexity and to enable solution
decentralization, we propose lightweight heuristic algorithms that can achieve
near-optimal performance and facilitate the trade-offs between energy
consumption, accuracy, and solution complexity. Simulation results show that
the proposed approaches reduce the energy consumption significantly while
executing multiple learning tasks compared to recent state-of-the-art methods. | [
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] |
We study the problem of learning a range of vision-based manipulation tasks
from a large offline dataset of robot interaction. In order to accomplish this,
humans need easy and effective ways of specifying tasks to the robot. Goal
images are one popular form of task specification, as they are already grounded
in the robot's observation space. However, goal images also have a number of
drawbacks: they are inconvenient for humans to provide, they can over-specify
the desired behavior leading to a sparse reward signal, or under-specify task
information in the case of non-goal reaching tasks. Natural language provides a
convenient and flexible alternative for task specification, but comes with the
challenge of grounding language in the robot's observation space. To scalably
learn this grounding we propose to leverage offline robot datasets (including
highly sub-optimal, autonomously collected data) with crowd-sourced natural
language labels. With this data, we learn a simple classifier which predicts if
a change in state completes a language instruction. This provides a
language-conditioned reward function that can then be used for offline
multi-task RL. In our experiments, we find that on language-conditioned
manipulation tasks our approach outperforms both goal-image specifications and
language conditioned imitation techniques by more than 25%, and is able to
perform visuomotor tasks from natural language, such as "open the right drawer"
and "move the stapler", on a Franka Emika Panda robot. | [
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] |
Objective: To propose novel SSVEP classification methodologies using deep
neural networks (DNNs) and improve performances in single-channel and
user-independent brain-computer interfaces (BCIs) with small data lengths.
Approach: We propose the utilization of filter banks (creating sub-band
components of the EEG signal) in conjunction with DNNs. In this context, we
created three different models: a recurrent neural network (FBRNN) analyzing
the time domain, a 2D convolutional neural network (FBCNN-2D) processing
complex spectrum features and a 3D convolutional neural network (FBCNN-3D)
analyzing complex spectrograms, which we introduce in this study as possible
input for SSVEP classification. We tested our neural networks on three open
datasets and conceived them so as not to require calibration from the final
user, simulating a user-independent BCI. Results: The DNNs with the filter
banks surpassed the accuracy of similar networks without this preprocessing
step by considerable margins, and they outperformed common SSVEP classification
methods (SVM and FBCCA) by even higher margins. Conclusion and significance:
Filter banks allow different types of deep neural networks to more efficiently
analyze the harmonic components of SSVEP. Complex spectrograms carry more
information than complex spectrum features and the magnitude spectrum, allowing
the FBCNN-3D to surpass the other CNNs. The performances obtained in the
challenging classification problems indicates a strong potential for the
construction of portable, economical, fast and low-latency BCIs. | [
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] |
Most Fairness in AI research focuses on exposing biases in AI systems. A
broader lens on fairness reveals that AI can serve a greater aspiration:
rooting out societal inequities from their source. Specifically, we focus on
inequities in health information, and aim to reduce bias in that domain using
AI. The AI algorithms under the hood of search engines and social media, many
of which are based on recommender systems, have an outsized impact on the
quality of medical and health information online. Therefore, embedding bias
detection and reduction into these recommender systems serving up medical and
health content online could have an outsized positive impact on patient
outcomes and wellbeing.
In this position paper, we offer the following contributions: (1) we propose
a novel framework of Fairness via AI, inspired by insights from medical
education, sociology and antiracism; (2) we define a new term, bisinformation,
which is related to, but distinct from, misinformation, and encourage
researchers to study it; (3) we propose using AI to study, detect and mitigate
biased, harmful, and/or false health information that disproportionately hurts
minority groups in society; and (4) we suggest several pillars and pose several
open problems in order to seed inquiry in this new space. While part (3) of
this work specifically focuses on the health domain, the fundamental computer
science advances and contributions stemming from research efforts in bias
reduction and Fairness via AI have broad implications in all areas of society. | [
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] |
Many real-world data can be represented as heterogeneous graphs with
different types of nodes and connections. Heterogeneous graph neural network
model aims to embed nodes or subgraphs into low-dimensional vector space for
various downstream tasks such as node classification, link prediction, etc.
Although several models were proposed recently, they either only aggregate
information from the same type of neighbors, or just indiscriminately treat
homogeneous and heterogeneous neighbors in the same way. Based on these
observations, we propose a new heterogeneous graph neural network model named
HMSG to comprehensively capture structural, semantic and attribute information
from both homogeneous and heterogeneous neighbors. Specifically, we first
decompose the heterogeneous graph into multiple metapath-based homogeneous and
heterogeneous subgraphs, and each subgraph associates specific semantic and
structural information. Then message aggregation methods are applied to each
subgraph independently, so that information can be learned in a more targeted
and efficient manner. Through a type-specific attribute transformation, node
attributes can also be transferred among different types of nodes. Finally, we
fuse information from subgraphs together to get the complete representation.
Extensive experiments on several datasets for node classification, node
clustering and link prediction tasks show that HMSG achieves the best
performance in all evaluation metrics than state-of-the-art baselines. | [
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] |
Odometer has been proven to significantly improve the accuracy of the Global
Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated
vehicle navigation in GNSS-challenged environments. However, the odometer is
inaccessible in many applications, especially for aftermarket devices. To apply
forward speed aiding without hardware wheeled odometer, we propose OdoNet, an
untethered one-dimensional Convolution Neural Network (CNN)-based
pseudo-odometer model learning from a single Inertial Measurement Unit (IMU),
which can act as an alternative to the wheeled odometer. Dedicated experiments
have been conducted to verify the feasibility and robustness of the OdoNet. The
results indicate that the IMU individuality, the vehicle loads, and the road
conditions have little impact on the robustness and precision of the OdoNet,
while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus,
a data-cleaning procedure is added to effectively mitigate the impacts of the
IMU biases and the mounting angles. Compared to the process using only
non-holonomic constraint (NHC), after employing the pseudo-odometer, the
positioning error is reduced by around 68%, while the percentage is around 74%
for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be
employed as an untethered pseudo-odometer for vehicle navigation, which can
efficiently improve the accuracy and reliability of the positioning in
GNSS-denied environments. | [
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] |
Internet of Things (IoT) based applications face an increasing number of
potential security risks, which need to be systematically assessed and
addressed. Expert-based manual assessment of IoT security is a predominant
approach, which is usually inefficient. To address this problem, we propose an
automated security assessment framework for IoT networks. Our framework first
leverages machine learning and natural language processing to analyze
vulnerability descriptions for predicting vulnerability metrics. The predicted
metrics are then input into a two-layered graphical security model, which
consists of an attack graph at the upper layer to present the network
connectivity and an attack tree for each node in the network at the bottom
layer to depict the vulnerability information. This security model
automatically assesses the security of the IoT network by capturing potential
attack paths. We evaluate the viability of our approach using a
proof-of-concept smart building system model which contains a variety of
real-world IoT devices and potential vulnerabilities. Our evaluation of the
proposed framework demonstrates its effectiveness in terms of automatically
predicting the vulnerability metrics of new vulnerabilities with more than 90%
accuracy, on average, and identifying the most vulnerable attack paths within
an IoT network. The produced assessment results can serve as a guideline for
cybersecurity professionals to take further actions and mitigate risks in a
timely manner. | [
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] |
Supervised Deep Learning requires plenty of labeled data to converge, and
hence perform optimally for task-specific learning. Therefore, we propose a
novel mechanism named DRo (for Deep Routing) for data-scarce domains like
security. The DRo approach builds upon some of the recent developments in
Deep-Clustering. In particular, it exploits the self-augmented training
mechanism using synthetically generated local perturbations. DRo not only
allays the challenges with sparse-labeled data but also offers many unique
advantages. We also developed a system named DRoID that uses the DRo mechanism
for enhancing the performance of an existing Malware Detection System that uses
(low information features like the) Android implicit Intent(s) as the only
features. We conduct experiments on DRoID using a popular and standardized
Android malware dataset and found that the DRo mechanism could successfully
reduce the false-alarms generated by the downstream classifier by 67.9%, and
also simultaneously boosts its accuracy by 11.3%. This is significant not only
because the gains achieved are unparalleled but also because the features used
were never considered rich enough to train a classifier on; and hence no decent
performance could ever be reported by any malware classification system
till-date using these features in isolation. Owing to the results achieved, the
DRo mechanism claims a dominant position amongst all known systems that aims to
enhance the classification performance of deep learning models with
sparse-labeled data. | [
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] |
We explore an online reinforcement learning (RL) paradigm to dynamically
optimize parallel particle tracing performance in distributed-memory systems.
Our method combines three novel components: (1) a work donation algorithm, (2)
a high-order workload estimation model, and (3) a communication cost model.
First, we design an RL-based work donation algorithm. Our algorithm monitors
workloads of processes and creates RL agents to donate data blocks and
particles from high-workload processes to low-workload processes to minimize
program execution time. The agents learn the donation strategy on the fly based
on reward and cost functions designed to consider processes' workload changes
and data transfer costs of donation actions. Second, we propose a workload
estimation model, helping RL agents estimate the workload distribution of
processes in future computations. Third, we design a communication cost model
that considers both block and particle data exchange costs, helping RL agents
make effective decisions with minimized communication costs. We demonstrate
that our algorithm adapts to different flow behaviors in large-scale fluid
dynamics, ocean, and weather simulation data. Our algorithm improves parallel
particle tracing performance in terms of parallel efficiency, load balance, and
costs of I/O and communication for evaluations with up to 16,384 processors. | [
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] |
Advanced Driver-Assistance Systems (ADAS) have been thriving and widely
deployed in recent years. In general, these systems receive sensor data,
compute driving decisions, and output control signals to the vehicles. To
smooth out the uncertainties brought by sensor outputs, they usually leverage
multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more
reliable understanding of the surroundings. However, MSF cannot completely
eliminate the uncertainties since it lacks the knowledge about which sensor
provides the most accurate data and how to optimally integrate the data
provided by the sensors. As a result, critical consequences might happen
unexpectedly. In this work, we observed that the popular MSF methods in an
industry-grade ADAS can mislead the car control and result in serious safety
hazards. We define the failures (e.g., car crashes) caused by the faulty MSF as
fusion errors and develop a novel evolutionary-based domain-specific search
framework, FusED, for the efficient detection of fusion errors. We further
apply causality analysis to show that the found fusion errors are indeed caused
by the MSF method. We evaluate our framework on two widely used MSF methods in
two driving environments. Experimental results show that FusED identifies more
than 150 fusion errors. Finally, we provide several suggestions to improve the
MSF methods we study. | [
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] |
The rapidly increasing prevalence of debilitating breathing disorders, such
as chronic obstructive pulmonary disease (COPD), calls for a meaningful
integration of artificial intelligence (AI) into healthcare. While this
promises improved detection and monitoring of breathing disorders, AI
techniques are almost invariably "data hungry" which highlights the importance
of generating physically meaningful surrogate data. Indeed, domain aware
surrogates would enable both an improved understanding of respiratory waveform
changes with different breathing disorders, and enhance the training of machine
learning algorithms. To this end, we introduce an apparatus comprising of PVC
tubes and 3D printed parts as a simple yet effective method of simulating both
obstructive and restrictive respiratory waveforms in healthy subjects.
Independent control over both inspiratory and expiratory resistances allows for
the simulation of obstructive breathing disorders through the whole spectrum of
FEV1/FVC spirometry ratios (used to classify COPD), ranging from healthy values
to values seen in severe chronic obstructive pulmonary disease. Moreover,
waveform characteristics of breathing disorders, such as a change in
inspiratory duty cycle or peak flow are also observed in the waveforms
resulting from use of the artificial breathing disorder simulation apparatus.
Overall, the proposed apparatus provides us with a simple, effective and
physically meaningful way to generate faithful surrogate breathing disorder
waveforms, a prerequisite for the use of artificial intelligence in respiratory
health. | [
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] |
Pandemics and natural disasters over the years have changed the behavior of
people, which has had a tremendous impact on all life aspects. With the
technologies available in each era, governments, organizations, and companies
have used these technologies to track, control, and influence the behavior of
individuals for a benefit. Nowadays, the use of the Internet of Things (IoT),
cloud computing, and artificial intelligence (AI) have made it easier to track
and change the behavior of users through changing IoT behavior. This article
introduces and discusses the concept of the Internet of Behavior (IoB) and its
integration with Explainable AI (XAI) techniques to provide trusted and evident
experience in the process of changing IoT behavior to ultimately improving
users' behavior. Therefore, a system based on IoB and XAI has been proposed in
a use case scenario of electrical power consumption that aims to influence user
consuming behavior to reduce power consumption and cost. The scenario results
showed a decrease of 522.2 kW of active power when compared to original
consumption over a 200-hours period. It also showed a total power cost saving
of 95.04 Euro for the same period. Moreover, decreasing the global active power
will reduce the power intensity through the positive correlation. | [
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] |
Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA)
method for graph-based learning tasks. However, it still remains prohibitively
challenging to inference GNNs over large graph datasets, limiting their
application to large-scale real-world tasks. While end-to-end jointly
optimizing GNNs and their accelerators is promising in boosting GNNs' inference
efficiency and expediting the design process, it is still underexplored due to
the vast and distinct design spaces of GNNs and their accelerators. In this
work, we propose G-CoS, a GNN and accelerator co-search framework that can
automatically search for matched GNN structures and accelerators to maximize
both task accuracy and acceleration efficiency. Specifically, GCoS integrates
two major enabling components: (1) a generic GNN accelerator search space which
is applicable to various GNN structures and (2) a one-shot GNN and accelerator
co-search algorithm that enables simultaneous and efficient search for optimal
GNN structures and their matched accelerators. To the best of our knowledge,
G-CoS is the first co-search framework for GNNs and their accelerators.
Extensive experiments and ablation studies show that the GNNs and accelerators
generated by G-CoS consistently outperform SOTA GNNs and GNN accelerators in
terms of both task accuracy and hardware efficiency, while only requiring a few
hours for the end-to-end generation of the best matched GNNs and their
accelerators. | [
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Recent interest in Knowledge Base Completion (KBC) has led to a plethora of
approaches based on reinforcement learning, inductive logic programming and
graph embeddings. In particular, rule-based KBC has led to interpretable rules
while being comparable in performance with graph embeddings. Even within
rule-based KBC, there exist different approaches that lead to rules of varying
quality and previous work has not always been precise in highlighting these
differences. Another issue that plagues most rule-based KBC is the
non-uniformity of relation paths: some relation sequences occur in very few
paths while others appear very frequently. In this paper, we show that not all
rule-based KBC models are the same and propose two distinct approaches that
learn in one case: 1) a mixture of relations and the other 2) a mixture of
paths. When implemented on top of neuro-symbolic AI, which learns rules by
extending Boolean logic to real-valued logic, the latter model leads to
superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in
terms of mean reciprocal rank. Furthermore, to address the non-uniformity of
relation paths, we combine rule-based KBC with graph embeddings thus improving
our results even further and achieving the best of both worlds. | [
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] |
Many real-life scenarios require humans to make difficult trade-offs: do we
always follow all the traffic rules or do we violate the speed limit in an
emergency? These scenarios force us to evaluate the trade-off between
collective norms and our own personal objectives. To create effective AI-human
teams, we must equip AI agents with a model of how humans make trade-offs in
complex, constrained environments. These agents will be able to mirror human
behavior or to draw human attention to situations where decision making could
be improved. To this end, we propose a novel inverse reinforcement learning
(IRL) method for learning implicit hard and soft constraints from
demonstrations, enabling agents to quickly adapt to new settings. In addition,
learning soft constraints over states, actions, and state features allows
agents to transfer this knowledge to new domains that share similar aspects. We
then use the constraint learning method to implement a novel system
architecture that leverages a cognitive model of human decision making,
multi-alternative decision field theory (MDFT), to orchestrate competing
objectives. We evaluate the resulting agent on trajectory length, number of
violated constraints, and total reward, demonstrating that our agent
architecture is both general and achieves strong performance. Thus we are able
to capture and replicate human-like trade-offs from demonstrations in
environments when constraints are not explicit. | [
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] |
Neural network implementations are known to be vulnerable to physical attack
vectors such as fault injection attacks. As of now, these attacks were only
utilized during the inference phase with the intention to cause a
misclassification. In this work, we explore a novel attack paradigm by
injecting faults during the training phase of a neural network in a way that
the resulting network can be attacked during deployment without the necessity
of further faulting. In particular, we discuss attacks against ReLU activation
functions that make it possible to generate a family of malicious inputs, which
are called fooling inputs, to be used at inference time to induce controlled
misclassifications. Such malicious inputs are obtained by mathematically
solving a system of linear equations that would cause a particular behaviour on
the attacked activation functions, similar to the one induced in training
through faulting. We call such attacks fooling backdoors as the fault attacks
at the training phase inject backdoors into the network that allow an attacker
to produce fooling inputs. We evaluate our approach against multi-layer
perceptron networks and convolutional networks on a popular image
classification task obtaining high attack success rates (from 60% to 100%) and
high classification confidence when as little as 25 neurons are attacked while
preserving high accuracy on the originally intended classification task. | [
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] |
Deception is a crucial tool in the cyberdefence repertoire, enabling
defenders to leverage their informational advantage to reduce the likelihood of
successful attacks. One way deception can be employed is through obscuring, or
masking, some of the information about how systems are configured, increasing
attacker's uncertainty about their targets. We present a novel game-theoretic
model of the resulting defender-attacker interaction, where the defender
chooses a subset of attributes to mask, while the attacker responds by choosing
an exploit to execute. The strategies of both players have combinatorial
structure with complex informational dependencies, and therefore even
representing these strategies is not trivial. First, we show that the problem
of computing an equilibrium of the resulting zero-sum defender-attacker game
can be represented as a linear program with a combinatorial number of system
configuration variables and constraints, and develop a constraint generation
approach for solving this problem. Next, we present a novel highly scalable
approach for approximately solving such games by representing the strategies of
both players as neural networks. The key idea is to represent the defender's
mixed strategy using a deep neural network generator, and then using
alternating gradient-descent-ascent algorithm, analogous to the training of
Generative Adversarial Networks. Our experiments, as well as a case study,
demonstrate the efficacy of the proposed approach. | [
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] |
Causal effect estimation for dynamic treatment regimes (DTRs) contributes to
sequential decision making. However, censoring and time-dependent confounding
under DTRs are challenging as the amount of observational data declines over
time due to a reducing sample size but the feature dimension increases over
time. Long-term follow-up compounds these challenges. Another challenge is the
highly complex relationships between confounders, treatments, and outcomes,
which causes the traditional and commonly used linear methods to fail. We
combine outcome regression models with treatment models for high dimensional
features using uncensored subjects that are small in sample size and we fit
deep Bayesian models for outcome regression models to reveal the complex
relationships between confounders, treatments, and outcomes. Also, the
developed deep Bayesian models can model uncertainty and output the prediction
variance which is essential for the safety-aware applications, such as
self-driving cars and medical treatment design. The experimental results on
medical simulations of HIV treatment show the ability of the proposed method to
obtain stable and accurate dynamic causal effect estimation from observational
data, especially with long-term follow-up. Our technique provides practical
guidance for sequential decision making, and policy-making. | [
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] |
Crash localization, an important step in debugging crashes, is challenging
when dealing with an extremely large number of diverse applications and
platforms and underlying root causes. Large-scale error reporting systems,
e.g., Windows Error Reporting (WER), commonly rely on manually developed rules
and heuristics to localize blamed frames causing the crashes. As new
applications and features are routinely introduced and existing applications
are run under new environments, developing new rules and maintaining existing
ones become extremely challenging. We propose a data-driven solution to address
the problem. We start with the first large-scale empirical study of 362K
crashes and their blamed methods reported to WER by tens of thousands of
applications running in the field. The analysis provides valuable insights on
where and how the crashes happen and what methods to blame for the crashes.
These insights enable us to develop DeepAnalyze, a novel multi-task sequence
labeling approach for identifying blamed frames in stack traces. We evaluate
our model with over a million real-world crashes from four popular Microsoft
applications and show that DeepAnalyze, trained with crashes from one set of
applications, not only accurately localizes crashes of the same applications,
but also bootstraps crash localization for other applications with zero to very
little additional training data. | [
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By constructing digital twins (DT) of an integrated energy system (IES), one
can benefit from DT's predictive capabilities to improve coordinations among
various energy converters, hence enhancing energy efficiency, cost savings and
carbon emission reduction. This paper is motivated by the fact that practical
IESs suffer from multiple uncertainty sources, and complicated surrounding
environment. To address this problem, a novel DT-based day-ahead scheduling
method is proposed. The physical IES is modelled as a multi-vector energy
system in its virtual space that interacts with the physical IES to manipulate
its operations. A deep neural network is trained to make statistical
cost-saving scheduling by learning from both historical forecasting errors and
day-ahead forecasts. Case studies of IESs show that the proposed DT-based
method is able to reduce the operating cost of IES by 63.5%, comparing to the
existing forecast-based scheduling methods. It is also found that both electric
vehicles and thermal energy storages play proactive roles in the proposed
method, highlighting their importance in future energy system integration and
decarbonisation. | [
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] |
What is learning? 20$^{st}$ century formalizations of learning theory --
which precipitated revolutions in artificial intelligence -- focus primarily on
$\mathit{in-distribution}$ learning, that is, learning under the assumption
that the training data are sampled from the same distribution as the evaluation
distribution. This assumption renders these theories inadequate for
characterizing 21$^{st}$ century real world data problems, which are typically
characterized by evaluation distributions that differ from the training data
distributions (referred to as out-of-distribution learning). We therefore make
a small change to existing formal definitions of learnability by relaxing that
assumption. We then introduce $\mathbf{learning\ efficiency}$ (LE) to quantify
the amount a learner is able to leverage data for a given problem, regardless
of whether it is an in- or out-of-distribution problem. We then define and
prove the relationship between generalized notions of learnability, and show
how this framework is sufficiently general to characterize transfer, multitask,
meta, continual, and lifelong learning. We hope this unification helps bridge
the gap between empirical practice and theoretical guidance in real world
problems. Finally, because biological learning continues to outperform machine
learning algorithms on certain OOD challenges, we discuss the limitations of
this framework vis-\'a-vis its ability to formalize biological learning,
suggesting multiple avenues for future research. | [
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] |
The maximum mean discrepancy (MMD) is a recently proposed test statistic for
two-sample test. Its quadratic time complexity, however, greatly hampers its
availability to large-scale applications. To accelerate the MMD calculation, in
this study we propose an efficient method called FastMMD. The core idea of
FastMMD is to equivalently transform the MMD with shift-invariant kernels into
the amplitude expectation of a linear combination of sinusoid components based
on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking
advantage of sampling of Fourier transform, FastMMD decreases the time
complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$
are the size and dimension of the sample set, respectively. Here $L$ is the
number of basis functions for approximating kernels which determines the
approximation accuracy. For kernels that are spherically invariant, the
computation can be further accelerated to $O(L N \log d)$ by using the Fastfood
technique (Le et al., 2013). The uniform convergence of our method has also
been theoretically proved in both unbiased and biased estimates. We have
further provided a geometric explanation for our method, namely ensemble of
circular discrepancy, which facilitates us to understand the insight of MMD,
and is hopeful to help arouse more extensive metrics for assessing two-sample
test. Experimental results substantiate that FastMMD is with similar accuracy
as exact MMD, while with faster computation speed and lower variance than the
existing MMD approximation methods. | [
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We consider the infinite-horizon discounted optimal control problem
formalized by Markov Decision Processes. We focus on several approximate
variations of the Policy Iteration algorithm: Approximate Policy Iteration,
Conservative Policy Iteration (CPI), a natural adaptation of the Policy Search
by Dynamic Programming algorithm to the infinite-horizon case (PSDP$_\infty$),
and the recently proposed Non-Stationary Policy iteration (NSPI(m)). For all
algorithms, we describe performance bounds, and make a comparison by paying a
particular attention to the concentrability constants involved, the number of
iterations and the memory required. Our analysis highlights the following
points: 1) The performance guarantee of CPI can be arbitrarily better than that
of API/API($\alpha$), but this comes at the cost of a relative---exponential in
$\frac{1}{\epsilon}$---increase of the number of iterations. 2) PSDP$_\infty$
enjoys the best of both worlds: its performance guarantee is similar to that of
CPI, but within a number of iterations similar to that of API. 3) Contrary to
API that requires a constant memory, the memory needed by CPI and PSDP$_\infty$
is proportional to their number of iterations, which may be problematic when
the discount factor $\gamma$ is close to 1 or the approximation error
$\epsilon$ is close to $0$; we show that the NSPI(m) algorithm allows to make
an overall trade-off between memory and performance. Simulations with these
schemes confirm our analysis. | [
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] |
We present a framework for learning human user models from joint-action
demonstrations that enables the robot to compute a robust policy for a
collaborative task with a human. The learning takes place completely
automatically, without any human intervention. First, we describe the
clustering of demonstrated action sequences into different human types using an
unsupervised learning algorithm. These demonstrated sequences are also used by
the robot to learn a reward function that is representative for each type,
through the employment of an inverse reinforcement learning algorithm. The
learned model is then used as part of a Mixed Observability Markov Decision
Process formulation, wherein the human type is a partially observable variable.
With this framework, we can infer, either offline or online, the human type of
a new user that was not included in the training set, and can compute a policy
for the robot that will be aligned to the preference of this new user and will
be robust to deviations of the human actions from prior demonstrations. Finally
we validate the approach using data collected in human subject experiments, and
conduct proof-of-concept demonstrations in which a person performs a
collaborative task with a small industrial robot. | [
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] |
Counterexample-guided inductive synthesis CEGIS is used to synthesize
programs from a candidate space of programs. The technique is guaranteed to
terminate and synthesize the correct program if the space of candidate programs
is finite. But the technique may or may not terminate with the correct program
if the candidate space of programs is infinite. In this paper, we perform a
theoretical analysis of counterexample-guided inductive synthesis technique. We
investigate whether the set of candidate spaces for which the correct program
can be synthesized using CEGIS depends on the counterexamples used in inductive
synthesis, that is, whether there are good mistakes which would increase the
synthesis power. We investigate whether the use of minimal counterexamples
instead of arbitrary counterexamples expands the set of candidate spaces of
programs for which inductive synthesis can successfully synthesize a correct
program. We consider two kinds of counterexamples: minimal counterexamples and
history bounded counterexamples. The history bounded counterexample used in any
iteration of CEGIS is bounded by the examples used in previous iterations of
inductive synthesis. We examine the relative change in power of inductive
synthesis in both cases. We show that the synthesis technique using minimal
counterexamples MinCEGIS has the same synthesis power as CEGIS but the
synthesis technique using history bounded counterexamples HCEGIS has different
power than that of CEGIS, but none dominates the other. | [
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] |
We present a novel hybrid algorithm for Bayesian network structure learning,
called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian
network and then performs a Bayesian-scoring greedy hill-climbing search to
orient the edges. It is based on a subroutine called HPC, that combines ideas
from incremental and divide-and-conquer constraint-based methods to learn the
parents and children of a target variable. We conduct an experimental
comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the
most powerful state-of-the-art algorithm for Bayesian network structure
learning, on several benchmarks with various data sizes. Our extensive
experiments show that H2PC outperforms MMHC both in terms of goodness of fit to
new data and in terms of the quality of the network structure itself, which is
closer to the true dependence structure of the data. The source code (in R) of
H2PC as well as all data sets used for the empirical tests are publicly
available. | [
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] |
Recent applications of Stackelberg Security Games (SSG), from wildlife crime
to urban crime, have employed machine learning tools to learn and predict
adversary behavior using available data about defender-adversary interactions.
Given these recent developments, this paper commits to an approach of directly
learning the response function of the adversary. Using the PAC model, this
paper lays a firm theoretical foundation for learning in SSGs (e.g.,
theoretically answer questions about the numbers of samples required to learn
adversary behavior) and provides utility guarantees when the learned adversary
model is used to plan the defender's strategy. The paper also aims to answer
practical questions such as how much more data is needed to improve an
adversary model's accuracy. Additionally, we explain a recently observed
phenomenon that prediction accuracy of learned adversary behavior is not enough
to discover the utility maximizing defender strategy. We provide four main
contributions: (1) a PAC model of learning adversary response functions in
SSGs; (2) PAC-model analysis of the learning of key, existing bounded
rationality models in SSGs; (3) an entirely new approach to adversary modeling
based on a non-parametric class of response functions with PAC-model analysis
and (4) identification of conditions under which computing the best defender
strategy against the learned adversary behavior is indeed the optimal strategy.
Finally, we conduct experiments with real-world data from a national park in
Uganda, showing the benefit of our new adversary modeling approach and
verification of our PAC model predictions. | [
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] |
Learning about the social structure of hidden and hard-to-reach populations
--- such as drug users and sex workers --- is a major goal of epidemiological
and public health research on risk behaviors and disease prevention.
Respondent-driven sampling (RDS) is a peer-referral process widely used by many
health organizations, where research subjects recruit other subjects from their
social network. In such surveys, researchers observe who recruited whom, along
with the time of recruitment and the total number of acquaintances (network
degree) of respondents. However, due to privacy concerns, the identities of
acquaintances are not disclosed. In this work, we show how to reconstruct the
underlying network structure through which the subjects are recruited. We
formulate the dynamics of RDS as a continuous-time diffusion process over the
underlying graph and derive the likelihood for the recruitment time series
under an arbitrary recruitment time distribution. We develop an efficient
stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork
Reconstruction) that finds the network that best explains the collected data.
We support our analytical results through an exhaustive set of experiments on
both synthetic and real data. | [
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Two architectures that generalize convolutional neural networks (CNNs) for
the processing of signals supported on graphs are introduced. We start with the
selection graph neural network (GNN), which replaces linear time invariant
filters with linear shift invariant graph filters to generate convolutional
features and reinterprets pooling as a possibly nonlinear subsampling stage
where nearby nodes pool their information in a set of preselected sample nodes.
A key component of the architecture is to remember the position of sampled
nodes to permit computation of convolutional features at deeper layers. The
second architecture, dubbed aggregation GNN, diffuses the signal through the
graph and stores the sequence of diffused components observed by a designated
node. This procedure effectively aggregates all components into a stream of
information having temporal structure to which the convolution and pooling
stages of regular CNNs can be applied. A multinode version of aggregation GNNs
is further introduced for operation in large scale graphs. An important
property of selection and aggregation GNNs is that they reduce to conventional
CNNs when particularized to time signals reinterpreted as graph signals in a
circulant graph. Comparative numerical analyses are performed in a source
localization application over synthetic and real-world networks. Performance is
also evaluated for an authorship attribution problem and text category
classification. Multinode aggregation GNNs are consistently the best performing
GNN architecture. | [
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] |
Knowledge distillation (KD) consists of transferring knowledge from one
machine learning model (the teacher}) to another (the student). Commonly, the
teacher is a high-capacity model with formidable performance, while the student
is more compact. By transferring knowledge, one hopes to benefit from the
student's compactness. %we desire a compact model with performance close to the
teacher's. We study KD from a new perspective: rather than compressing models,
we train students parameterized identically to their teachers. Surprisingly,
these {Born-Again Networks (BANs), outperform their teachers significantly,
both on computer vision and language modeling tasks. Our experiments with BANs
based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10
(3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional
experiments explore two distillation objectives: (i) Confidence-Weighted by
Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP).
Both methods elucidate the essential components of KD, demonstrating a role of
the teacher outputs on both predicted and non-predicted classes. We present
experiments with students of various capacities, focusing on the under-explored
case where students overpower teachers. Our experiments show significant
advantages from transferring knowledge between DenseNets and ResNets in either
direction. | [
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] |
Recurrent Neural Networks (RNNs) play a major role in the field of sequential
learning, and have outperformed traditional algorithms on many benchmarks.
Training deep RNNs still remains a challenge, and most of the state-of-the-art
models are structured with a transition depth of 2-4 layers. Recurrent Highway
Networks (RHNs) were introduced in order to tackle this issue. These have
achieved state-of-the-art performance on a few benchmarks using a depth of 10
layers. However, the performance of this architecture suffers from a
bottleneck, and ceases to improve when an attempt is made to add more layers.
In this work, we analyze the causes for this, and postulate that the main
source is the way that the information flows through time. We introduce a novel
and simple variation for the RHN cell, called Highway State Gating (HSG), which
allows adding more layers, while continuing to improve performance. By using a
gating mechanism for the state, we allow the net to "choose" whether to pass
information directly through time, or to gate it. This mechanism also allows
the gradient to back-propagate directly through time and, therefore, results in
a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a
platform for empirical proof of concept. Empirical results show that the
improvement due to Highway State Gating is for all depths, and as the depth
increases, the improvement also increases. | [
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] |
Machine learning (ML) has become a core component of many real-world
applications and training data is a key factor that drives current progress.
This huge success has led Internet companies to deploy machine learning as a
service (MLaaS). Recently, the first membership inference attack has shown that
extraction of information on the training set is possible in such MLaaS
settings, which has severe security and privacy implications.
However, the early demonstrations of the feasibility of such attacks have
many assumptions on the adversary, such as using multiple so-called shadow
models, knowledge of the target model structure, and having a dataset from the
same distribution as the target model's training data. We relax all these key
assumptions, thereby showing that such attacks are very broadly applicable at
low cost and thereby pose a more severe risk than previously thought. We
present the most comprehensive study so far on this emerging and developing
threat using eight diverse datasets which show the viability of the proposed
attacks across domains.
In addition, we propose the first effective defense mechanisms against such
broader class of membership inference attacks that maintain a high level of
utility of the ML model. | [
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] |
Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative
to standard parametric deep learning models. A DGP is formed by stacking
multiple GPs resulting in a well-regularized composition of functions. The
Bayesian framework that equips the model with attractive properties, such as
implicit capacity control and predictive uncertainty, makes it at the same time
challenging to combine with a convolutional structure. This has hindered the
application of DGPs in computer vision tasks, an area where deep parametric
models (i.e. CNNs) have made breakthroughs. Standard kernels used in DGPs such
as radial basis functions (RBFs) are insufficient for handling pixel
variability in raw images. In this paper, we build on the recent convolutional
GP to develop Convolutional DGP (CDGP) models which effectively capture image
level features through the use of convolution kernels, therefore opening up the
way for applying DGPs to computer vision tasks. Our model learns local spatial
influence and outperforms strong GP based baselines on multi-class image
classification. We also consider various constructions of convolution kernel
over the image patches, analyze the computational trade-offs and provide an
efficient framework for convolutional DGP models. The experimental results on
image data such as MNIST, rectangles-image, CIFAR10 and Caltech101 demonstrate
the effectiveness of the proposed approaches. | [
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] |
Designing a good reward function is essential to robot planning and
reinforcement learning, but it can also be challenging and frustrating. The
reward needs to work across multiple different environments, and that often
requires many iterations of tuning. We introduce a novel divide-and-conquer
approach that enables the designer to specify a reward separately for each
environment. By treating these separate reward functions as observations about
the underlying true reward, we derive an approach to infer a common reward
across all environments. We conduct user studies in an abstract grid world
domain and in a motion planning domain for a 7-DOF manipulator that measure
user effort and solution quality. We show that our method is faster, easier to
use, and produces a higher quality solution than the typical method of
designing a reward jointly across all environments. We additionally conduct a
series of experiments that measure the sensitivity of these results to
different properties of the reward design task, such as the number of
environments, the number of feasible solutions per environment, and the
fraction of the total features that vary within each environment. We find that
independent reward design outperforms the standard, joint, reward design
process but works best when the design problem can be divided into simpler
subproblems. | [
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] |
Machine learning (ML) models are increasingly trained in clusters with
non-dedicated workers possessing heterogeneous resources. In such scenarios,
model training efficiency can be negatively affected by stragglers -- workers
that run much slower than others. Efficient model training requires eliminating
such stragglers, yet for modern ML workloads, existing load balancing
strategies are inefficient and even infeasible. In this paper, we propose a
novel strategy called semi-dynamic load balancing to eliminate stragglers of
distributed ML workloads. The key insight is that ML workers shall be
load-balanced at iteration boundaries, being non-intrusive to intra-iteration
execution. We develop LB-BSP based on such an insight, which is an integrated
worker coordination mechanism that adapts workers' load to their instantaneous
processing capabilities by right-sizing the sample batches at the
synchronization barriers. We have custom-designed the batch sizing algorithm
respectively for CPU and GPU clusters based on their own characteristics.
LB-BSP has been implemented as a Python module for ML frameworks like
TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical,
effective and light-weight, and is able to accelerating distributed training by
up to $54\%$. | [
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] |
In line with the sensorimotor contingency theory, we investigate the problem
of the perception of space from a fundamental sensorimotor perspective. Despite
its pervasive nature in our perception of the world, the origin of the concept
of space remains largely mysterious. For example in the context of artificial
perception, this issue is usually circumvented by having engineers pre-define
the spatial structure of the problem the agent has to face. We here show that
the structure of space can be autonomously discovered by a naive agent in the
form of sensorimotor regularities, that correspond to so called compensable
sensory experiences: these are experiences that can be generated either by the
agent or its environment. By detecting such compensable experiences the agent
can infer the topological and metric structure of the external space in which
its body is moving. We propose a theoretical description of the nature of these
regularities and illustrate the approach on a simulated robotic arm equipped
with an eye-like sensor, and which interacts with an object. Finally we show
how these regularities can be used to build an internal representation of the
sensor's external spatial configuration. | [
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] |
The 2017 ASSISTments Data Mining competition aims to use data from a
longitudinal study for predicting a brand-new outcome of students which had
never been studied before by the educational data mining research community.
Specifically, it facilitates research in developing predictive models that
predict whether the first job of a student out of college belongs to a STEM
(the acronym for science, technology, engineering, and mathematics) field. This
is based on the student's learning history on the ASSISTments blended learning
platform in the form of extensive clickstream data gathered during the middle
school years. To tackle this challenge, we first estimate the expected
knowledge state of students with respect to different mathematical skills using
a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then
combine the features corresponding to the DKT/DKT+ expected knowledge state
with other features extracted directly from the student profile in the dataset
to train several machine learning models for the STEM/non-STEM job prediction.
Our experiments show that models trained with the combined features generally
perform better than the models trained with the student profile alone. Detailed
analysis of the student's knowledge state reveals that, when compared with
non-STEM students, STEM students generally show a higher mastery level and a
higher learning gain in mathematics. | [
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] |
DBSCAN may not be sufficient when the input data type is heterogeneous in
terms of textual description. When we aim to discover clusters of geo-tagged
records relevant to a particular point-of-interest (POI) on social media,
examining only one type of input data (e.g., the tweets relevant to a POI) may
draw an incomplete picture of clusters due to noisy regions. To overcome this
problem, we introduce DBSTexC, a newly defined density-based clustering
algorithm using spatio--textual information. We first characterize POI-relevant
and POI-irrelevant tweets as the texts that include and do not include a POI
name or its semantically coherent variations, respectively. By leveraging the
proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm
demonstrates much higher clustering performance than the DBSCAN case in terms
of $\mathcal{F}_1$ score and its variants. While DBSTexC performs exactly as
DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with
the textually heterogeneous inputs. Furthermore, to further improve the
clustering quality by fully capturing the geographic distribution of tweets, we
present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates
the notion of fuzzy clustering into the DBSTexC. We then demonstrate the
robustness of F-DBSTexC via intensive experiments. The computational complexity
of our algorithms is also analytically and numerically shown. | [
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] |
We consider peer review in a conference setting where there is typically an
overlap between the set of reviewers and the set of authors. This overlap can
incentivize strategic reviews to influence the final ranking of one's own
papers. In this work, we address this problem through the lens of social
choice, and present a theoretical framework for strategyproof and efficient
peer review. We first present and analyze an algorithm for reviewer-assignment
and aggregation that guarantees strategyproofness and a natural efficiency
property called unanimity, when the authorship graph satisfies a simple
property. Our algorithm is based on the so-called partitioning method, and can
be thought as a generalization of this method to conference peer review
settings. We then empirically show that the requisite property on the
authorship graph is indeed satisfied in the submission data from the ICLR
conference, and further demonstrate a simple trick to make the partitioning
method more practically appealing for conference peer review. Finally, we
complement our positive results with negative theoretical results where we
prove that under various ways of strengthening the requirements, it is
impossible for any algorithm to be strategyproof and efficient. | [
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] |
We have seen much recent progress in rigid object manipulation, but
interaction with deformable objects has notably lagged behind. Due to the large
configuration space of deformable objects, solutions using traditional
modelling approaches require significant engineering work. Perhaps then,
bypassing the need for explicit modelling and instead learning the control in
an end-to-end manner serves as a better approach? Despite the growing interest
in the use of end-to-end robot learning approaches, only a small amount of work
has focused on their applicability to deformable object manipulation. Moreover,
due to the large amount of data needed to learn these end-to-end solutions, an
emerging trend is to learn control policies in simulation and then transfer
them over to the real world. To-date, no work has explored whether it is
possible to learn and transfer deformable object policies. We believe that if
sim-to-real methods are to be employed further, then it should be possible to
learn to interact with a wide variety of objects, and not only rigid objects.
In this work, we use a combination of state-of-the-art deep reinforcement
learning algorithms to solve the problem of manipulating deformable objects
(specifically cloth). We evaluate our approach on three tasks --- folding a
towel up to a mark, folding a face towel diagonally, and draping a piece of
cloth over a hanger. Our agents are fully trained in simulation with domain
randomisation, and then successfully deployed in the real world without having
seen any real deformable objects. | [
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] |
We examine the usefulness of applying neural networks as a variational state
ansatz for many-body quantum systems in the context of quantum
information-processing tasks. In the neural network state ansatz, the complex
amplitude function of a quantum state is computed by a neural network. The
resulting multipartite entanglement structure captured by this ansatz has
proven rich enough to describe the ground states and unitary dynamics of
various physical systems of interest. In the present paper, we initiate the
study of neural network states in quantum information-processing tasks. We
demonstrate that neural network states are capable of efficiently representing
quantum codes for quantum information transmission and quantum error
correction, supplying further evidence for the usefulness of neural network
states to describe multipartite entanglement. In particular, we show the
following main results: a) Neural network states yield quantum codes with a
high coherent information for two important quantum channels, the generalized
amplitude damping channel and the dephrasure channel. These codes outperform
all other known codes for these channels, and cannot be found using a direct
parametrization of the quantum state. b) For the depolarizing channel, the
neural network state ansatz reliably finds the best known codes given by
repetition codes. c) Neural network states can be used to represent absolutely
maximally entangled states, a special type of quantum error-correcting codes.
In all three cases, the neural network state ansatz provides an efficient and
versatile means as a variational parametrization of these highly entangled
states. | [
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] |
Predictive modeling is invaded by elastic, yet complex methods such as neural
networks or ensembles (model stacking, boosting or bagging). Such methods are
usually described by a large number of parameters or hyper parameters - a price
that one needs to pay for elasticity. The very number of parameters makes
models hard to understand. This paper describes a consistent collection of
explainers for predictive models, a.k.a. black boxes. Each explainer is a
technique for exploration of a black box model. Presented approaches are
model-agnostic, what means that they extract useful information from any
predictive method despite its internal structure. Each explainer is linked with
a specific aspect of a model. Some are useful in decomposing predictions, some
serve better in understanding performance, while others are useful in
understanding importance and conditional responses of a particular variable.
Every explainer presented in this paper works for a single model or for a
collection of models. In the latter case, models can be compared against each
other. Such comparison helps to find strengths and weaknesses of different
approaches and gives additional possibilities for model validation. Presented
explainers are implemented in the DALEX package for R. They are based on a
uniform standardized grammar of model exploration which may be easily extended.
The current implementation supports the most popular frameworks for
classification and regression. | [
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] |
Online symptom checkers have significant potential to improve patient care,
however their reliability and accuracy remain variable. We hypothesised that an
artificial intelligence (AI) powered triage and diagnostic system would compare
favourably with human doctors with respect to triage and diagnostic accuracy.
We performed a prospective validation study of the accuracy and safety of an AI
powered triage and diagnostic system. Identical cases were evaluated by both an
AI system and human doctors. Differential diagnoses and triage outcomes were
evaluated by an independent judge, who was blinded from knowing the source (AI
system or human doctor) of the outcomes. Independently of these cases,
vignettes from publicly available resources were also assessed to provide a
benchmark to previous studies and the diagnostic component of the MRCGP exam.
Overall we found that the Babylon AI powered Triage and Diagnostic System was
able to identify the condition modelled by a clinical vignette with accuracy
comparable to human doctors (in terms of precision and recall). In addition, we
found that the triage advice recommended by the AI System was, on average,
safer than that of human doctors, when compared to the ranges of acceptable
triage provided by independent expert judges, with only a minimal reduction in
appropriateness. | [
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] |
Malicious software, or malware, presents a continuously evolving challenge in
computer security. These embedded snippets of code in the form of malicious
files or hidden within legitimate files cause a major risk to systems with
their ability to run malicious command sequences. Malware authors even use
polymorphism to reorder these commands and create several malicious variations.
However, if executed in a secure environment, one can perform early malware
detection on emulated command sequences.
The models presented in this paper leverage this sequential data derived via
emulation in order to perform Neural Malware Detection. These models target the
core of the malicious operation by learning the presence and pattern of
co-occurrence of malicious event actions from within these sequences. Our
models can capture entire event sequences and be trained directly using the
known target labels. These end-to-end learning models are powered by two
commonly used structures - Long Short-Term Memory (LSTM) Networks and
Convolutional Neural Networks (CNNs). Previously proposed sequential malware
classification models process no more than 200 events. Attackers can evade
detection by delaying any malicious activity beyond the beginning of the file.
We present specialized models that can handle extremely long sequences while
successfully performing malware detection in an efficient way. We present an
implementation of the Convoluted Partitioning of Long Sequences approach in
order to tackle this vulnerability and operate on long sequences. We present
our results on a large dataset consisting of 634,249 file sequences, with
extremely long file sequences. | [
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] |
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}. | [
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] |
Recent advances in representation learning on graphs, mainly leveraging graph
convolutional networks, have brought a substantial improvement on many
graph-based benchmark tasks. While novel approaches to learning node embeddings
are highly suitable for node classification and link prediction, their
application to graph classification (predicting a single label for the entire
graph) remains mostly rudimentary, typically using a single global pooling step
to aggregate node features or a hand-designed, fixed heuristic for hierarchical
coarsening of the graph structure. An important step towards ameliorating this
is differentiable graph coarsening---the ability to reduce the size of the
graph in an adaptive, data-dependent manner within a graph neural network
pipeline, analogous to image downsampling within CNNs. However, the previous
prominent approach to pooling has quadratic memory requirements during training
and is therefore not scalable to large graphs. Here we combine several recent
advances in graph neural network design to demonstrate that competitive
hierarchical graph classification results are possible without sacrificing
sparsity. Our results are verified on several established graph classification
benchmarks, and highlight an important direction for future research in
graph-based neural networks. | [
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] |
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies. | [
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] |
Imitation Learning has empowered recent advances in learning robotic
manipulation tasks by addressing shortcomings of Reinforcement Learning such as
exploration and reward specification. However, research in this area has been
limited to modest-sized datasets due to the difficulty of collecting large
quantities of task demonstrations through existing mechanisms. This work
introduces RoboTurk to address this challenge. RoboTurk is a crowdsourcing
platform for high quality 6-DoF trajectory based teleoperation through the use
of widely available mobile devices (e.g. iPhone). We evaluate RoboTurk on three
manipulation tasks of varying timescales (15-120s) and observe that our user
interface is statistically similar to special purpose hardware such as virtual
reality controllers in terms of task completion times. Furthermore, we observe
that poor network conditions, such as low bandwidth and high delay links, do
not substantially affect the remote users' ability to perform task
demonstrations successfully on RoboTurk. Lastly, we demonstrate the efficacy of
RoboTurk through the collection of a pilot dataset; using RoboTurk, we
collected 137.5 hours of manipulation data from remote workers, amounting to
over 2200 successful task demonstrations in 22 hours of total system usage. We
show that the data obtained through RoboTurk enables policy learning on
multi-step manipulation tasks with sparse rewards and that using larger
quantities of demonstrations during policy learning provides benefits in terms
of both learning consistency and final performance. For additional results,
videos, and to download our pilot dataset, visit
$\href{http://roboturk.stanford.edu/}{\texttt{roboturk.stanford.edu}}$ | [
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] |
We study the problem of learning conditional generators from noisy labeled
samples, where the labels are corrupted by random noise. A standard training of
conditional GANs will not only produce samples with wrong labels, but also
generate poor quality samples. We consider two scenarios, depending on whether
the noise model is known or not. When the distribution of the noise is known,
we introduce a novel architecture which we call Robust Conditional GAN (RCGAN).
The main idea is to corrupt the label of the generated sample before feeding to
the adversarial discriminator, forcing the generator to produce samples with
clean labels. This approach of passing through a matching noisy channel is
justified by corresponding multiplicative approximation bounds between the loss
of the RCGAN and the distance between the clean real distribution and the
generator distribution. This shows that the proposed approach is robust, when
used with a carefully chosen discriminator architecture, known as projection
discriminator. When the distribution of the noise is not known, we provide an
extension of our architecture, which we call RCGAN-U, that learns the noise
model simultaneously while training the generator. We show experimentally on
MNIST and CIFAR-10 datasets that both the approaches consistently improve upon
baseline approaches, and RCGAN-U closely matches the performance of RCGAN. | [
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] |
It is said that beauty is in the eye of the beholder. But how exactly can we
characterize such discrepancies in interpretation? For example, are there any
specific features of an image that makes person A regard an image as beautiful
while person B finds the same image displeasing? Such questions ultimately aim
at explaining our individual ways of interpretation, an intention that has been
of fundamental importance to the social sciences from the beginning. More
recently, advances in computer science brought up two related questions: First,
can computational tools be adopted for analyzing ways of interpretation?
Second, what if the "beholder" is a computer model, i.e., how can we explain a
computer model's point of view? Numerous efforts have been made regarding both
of these points, while many existing approaches focus on particular aspects and
are still rather separate. With this paper, in order to connect these
approaches we introduce a theoretical framework for analyzing interpretation,
which is applicable to interpretation of both human beings and computer models.
We give an overview of relevant computational approaches from various fields,
and discuss the most common and promising application areas. The focus of this
paper lies on interpretation of text and image data, while many of the
presented approaches are applicable to other types of data as well. | [
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] |
Probabilistic graphical models are powerful tools which allow us to formalise
our knowledge about the world and reason about its inherent uncertainty. There
exist a considerable number of methods for performing inference in
probabilistic graphical models; however, they can be computationally costly due
to significant time burden and/or storage requirements; or they lack
theoretical guarantees of convergence and accuracy when applied to large scale
graphical models. To this end, we propose the Universal Marginaliser Importance
Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a
deep neural network trained on samples from the model and inherits the
asymptotic guarantees of importance sampling. We show how combining samples
drawn from the graphical model with an appropriate masking function allows us
to train a single neural network to approximate any of the corresponding
conditional marginal distributions, and thus amortise the cost of inference. We
also show that the graph embeddings can be applied for tasks such as:
clustering, classification and interpretation of relationships between the
nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing
that UM-IS outperforms sampling-based methods by a large margin while being
computationally efficient. | [
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] |
Matter evolved under influence of gravity from minuscule density
fluctuations. Non-perturbative structure formed hierarchically over all scales,
and developed non-Gaussian features in the Universe, known as the Cosmic Web.
To fully understand the structure formation of the Universe is one of the holy
grails of modern astrophysics. Astrophysicists survey large volumes of the
Universe and employ a large ensemble of computer simulations to compare with
the observed data in order to extract the full information of our own Universe.
However, to evolve trillions of galaxies over billions of years even with the
simplest physics is a daunting task. We build a deep neural network, the Deep
Density Displacement Model (hereafter D$^3$M), to predict the non-linear
structure formation of the Universe from simple linear perturbation theory. Our
extensive analysis, demonstrates that D$^3$M outperforms the second order
perturbation theory (hereafter 2LPT), the commonly used fast approximate
simulation method, in point-wise comparison, 2-point correlation, and 3-point
correlation. We also show that D$^3$M is able to accurately extrapolate far
beyond its training data, and predict structure formation for significantly
different cosmological parameters. Our study proves, for the first time, that
deep learning is a practical and accurate alternative to approximate
simulations of the gravitational structure formation of the Universe. | [
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] |
We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration. | [
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] |
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more. | [
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Task-motion planning (TMP) addresses the problem of efficiently generating
executable and low-cost task plans in a discrete space such that the (initially
unknown) action costs are determined by motion plans in a corresponding
continuous space. However, a task-motion plan can be sensitive to unexpected
domain uncertainty and changes, leading to suboptimal behaviors or execution
failures. In this paper, we propose a novel framework, TMP-RL, which is an
integration of TMP and reinforcement learning (RL) from the execution
experience, to solve the problem of robust task-motion planning in dynamic and
uncertain domains. TMP-RL features two nested planning-learning loops. In the
inner TMP loop, the robot generates a low-cost, feasible task-motion plan by
iteratively planning in the discrete space and updating relevant action costs
evaluated by the motion planner in continuous space. In the outer loop, the
plan is executed, and the robot learns from the execution experience via
model-free RL, to further improve its task-motion plans. RL in the outer loop
is more accurate to the current domain but also more expensive, and using less
costly task and motion planning leads to a jump-start for learning in the real
world. Our approach is evaluated on a mobile service robot conducting
navigation tasks in an office area. Results show that TMP-RL approach
significantly improves adaptability and robustness (in comparison to TMP
methods) and leads to rapid convergence (in comparison to task planning (TP)-RL
methods). We also show that TMP-RL can reuse learned values to smoothly adapt
to new scenarios during long-term deployments. | [
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] |
Recommender systems in academia are not widely available. This may be in part
due to the difficulty and cost of developing and maintaining recommender
systems. Many operators of academic products such as digital libraries and
reference managers avoid this effort, although a recommender system could
provide significant benefits to their users. In this paper, we introduce Mr.
DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of
academic products to easily integrate a scientific recommender system into
their products. Mr. DLib generates recommendations for research articles but in
the future, recommendations may include call for papers, grants, etc. Operators
of academic products can request recommendations from Mr. DLib and display
these recommendations to their users. Mr. DLib can be integrated in just a few
hours or days; creating an equivalent recommender system from scratch would
require several months for an academic operator. Mr. DLib has been used by
GESIS Sowiport and by the reference manager JabRef. Mr. DLib is open source and
its goal is to facilitate the application of, and research on, scientific
recommender systems. In this paper, we present the motivation for Mr. DLib, the
architecture and details about the effectiveness. Mr. DLib has delivered 94m
recommendations over a span of two years with an average click-through rate of
0.12%. | [
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] |
Variational inference (VI) has become the method of choice for fitting many
modern probabilistic models. However, practitioners are faced with a fragmented
literature that offers a bewildering array of algorithmic options. First, the
variational family. Second, the granularity of the updates e.g. whether the
updates are local to each data point and employ message passing or global.
Third, the method of optimization (bespoke or blackbox, closed-form or
stochastic updates, etc.). This paper presents a new framework, termed
Partitioned Variational Inference (PVI), that explicitly acknowledges these
algorithmic dimensions of VI, unifies disparate literature, and provides
guidance on usage. Crucially, the proposed PVI framework allows us to identify
new ways of performing VI that are ideally suited to challenging learning
scenarios including federated learning (where distributed computing is
leveraged to process non-centralized data) and continual learning (where new
data and tasks arrive over time and must be accommodated quickly). We showcase
these new capabilities by developing communication-efficient federated training
of Bayesian neural networks and continual learning for Gaussian process models
with private pseudo-points. The new methods significantly outperform the
state-of-the-art, whilst being almost as straightforward to implement as
standard VI. | [
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] |
Dementia and especially Alzheimer's disease (AD) are the most common causes
of cognitive decline in elderly people. A spread of the above mentioned mental
health problems in aging societies is causing a significant medical and
economic burden in many countries around the world. According to a recent World
Health Organization (WHO) report, it is approximated that currently, worldwide,
about 47 million people live with a dementia spectrum of neurocognitive
disorders. This number is expected to triple by 2050, which calls for possible
application of AI-based technologies to support an early screening for
preventive interventions and a subsequent mental wellbeing monitoring as well
as maintenance with so-called digital-pharma or beyond a pill therapeutical
approaches. This paper discusses our attempt and preliminary results of
brainwave (EEG) techniques to develop digital biomarkers for dementia progress
detection and monitoring. We present an information geometry-based
classification approach for automatic EEG-derived event related responses
(ERPs) discrimination of low versus high task-load auditory or tactile stimuli
recognition, of which amplitude and latency variabilities are similar to those
in dementia. The discussed approach is a step forward to develop AI, and
especially machine learning (ML) approaches, for the subsequent application to
mild-cognitive impairment (MCI) and AD diagnostics. | [
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] |
Machine-learning models have demonstrated great success in learning complex
patterns that enable them to make predictions about unobserved data. In
addition to using models for prediction, the ability to interpret what a model
has learned is receiving an increasing amount of attention. However, this
increased focus has led to considerable confusion about the notion of
interpretability. In particular, it is unclear how the wide array of proposed
interpretation methods are related, and what common concepts can be used to
evaluate them.
We aim to address these concerns by defining interpretability in the context
of machine learning and introducing the Predictive, Descriptive, Relevant (PDR)
framework for discussing interpretations. The PDR framework provides three
overarching desiderata for evaluation: predictive accuracy, descriptive
accuracy and relevancy, with relevancy judged relative to a human audience.
Moreover, to help manage the deluge of interpretation methods, we introduce a
categorization of existing techniques into model-based and post-hoc categories,
with sub-groups including sparsity, modularity and simulatability. To
demonstrate how practitioners can use the PDR framework to evaluate and
understand interpretations, we provide numerous real-world examples. These
examples highlight the often under-appreciated role played by human audiences
in discussions of interpretability. Finally, based on our framework, we discuss
limitations of existing methods and directions for future work. We hope that
this work will provide a common vocabulary that will make it easier for both
practitioners and researchers to discuss and choose from the full range of
interpretation methods. | [
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] |
In this work, we investigate the feasibility and effectiveness of employing
deep learning algorithms for automatic recognition of the modulation type of
received wireless communication signals from subsampled data. Recent work
considered a GNU radio-based data set that mimics the imperfections in a real
wireless channel and uses 10 different modulation types. A Convolutional Neural
Network (CNN) architecture was then developed and shown to achieve performance
that exceeds that of expert-based approaches. Here, we continue this line of
work and investigate deep neural network architectures that deliver high
classification accuracy. We identify three architectures - namely, a
Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term
Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead
to typical classification accuracy values around 90% at high SNR. We then study
algorithms to reduce the training time by minimizing the size of the training
data set, while incurring a minimal loss in classification accuracy. To this
end, we demonstrate the performance of Principal Component Analysis in
significantly reducing the training time, while maintaining good performance at
low SNR. We also investigate subsampling techniques that further reduce the
training time, and pave the way for online classification at high SNR. Finally,
we identify representative SNR values for training each of the candidate
architectures, and consequently, realize drastic reductions of the training
time, with negligible loss in classification accuracy. | [
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Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model. | [
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The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance. | [
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] |
Behavior and motion planning play an important role in automated driving.
Traditionally, behavior planners instruct local motion planners with predefined
behaviors. Due to the high scene complexity in urban environments,
unpredictable situations may occur in which behavior planners fail to match
predefined behavior templates. Recently, general-purpose planners have been
introduced, combining behavior and local motion planning. These general-purpose
planners allow behavior-aware motion planning given a single reward function.
However, two challenges arise: First, this function has to map a complex
feature space into rewards. Second, the reward function has to be manually
tuned by an expert. Manually tuning this reward function becomes a tedious
task. In this paper, we propose an approach that relies on human driving
demonstrations to automatically tune reward functions. This study offers
important insights into the driving style optimization of general-purpose
planners with maximum entropy inverse reinforcement learning. We evaluate our
approach based on the expected value difference between learned and
demonstrated policies. Furthermore, we compare the similarity of human driven
trajectories with optimal policies of our planner under learned and
expert-tuned reward functions. Our experiments show that we are able to learn
reward functions exceeding the level of manual expert tuning without prior
domain knowledge. | [
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Coordination recognition and subtle pattern prediction of future trajectories
play a significant role when modeling interactive behaviors of multiple agents.
Due to the essential property of uncertainty in the future evolution,
deterministic predictors are not sufficiently safe and robust. In order to
tackle the task of probabilistic prediction for multiple, interactive entities,
we propose a coordination and trajectory prediction system (CTPS), which has a
hierarchical structure including a macro-level coordination recognition module
and a micro-level subtle pattern prediction module which solves a probabilistic
generation task. We illustrate two types of representation of the coordination
variable: categorized and real-valued, and compare their effects and advantages
based on empirical studies. We also bring the ideas of Bayesian deep learning
into deep generative models to generate diversified prediction hypotheses. The
proposed system is tested on multiple driving datasets in various traffic
scenarios, which achieves better performance than baseline approaches in terms
of a set of evaluation metrics. The results also show that using categorized
coordination can better capture multi-modality and generate more diversified
samples than the real-valued coordination, while the latter can generate
prediction hypotheses with smaller errors with a sacrifice of sample diversity.
Moreover, employing neural networks with weight uncertainty is able to generate
samples with larger variance and diversity. | [
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] |
Robot design is often a slow and difficult process requiring the iterative
construction and testing of prototypes, with the goal of sequentially
optimizing the design. For most robots, this process is further complicated by
the need, when validating the capabilities of the hardware to solve the desired
task, to already have an appropriate controller, which is in turn designed and
tuned for the specific hardware. In this paper, we propose a novel approach,
HPC-BBO, to efficiently and automatically design hardware configurations, and
evaluate them by also automatically tuning the corresponding controller.
HPC-BBO is based on a hierarchical Bayesian optimization process which
iteratively optimizes morphology configurations (based on the performance of
the previous designs during the controller learning process) and subsequently
learns the corresponding controllers (exploiting the knowledge collected from
optimizing for previous morphologies). Moreover, HPC-BBO can select a "batch"
of multiple morphology designs at once, thus parallelizing hardware validation
and reducing the number of time-consuming production cycles. We validate
HPC-BBO on the design of the morphology and controller for a simulated 6-legged
microrobot. Experimental results show that HPC-BBO outperforms multiple
competitive baselines, and yields a $360\%$ reduction in production cycles over
standard Bayesian optimization, thus reducing the hypothetical manufacturing
time of our microrobot from 21 to 4 months. | [
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] |
Gene interaction graphs aim to capture various relationships between genes
and can represent decades of biology research. When trying to make predictions
from genomic data, those graphs could be used to overcome the curse of
dimensionality by making machine learning models sparser and more consistent
with biological common knowledge. In this work, we focus on assessing how well
those graphs capture dependencies seen in gene expression data to evaluate the
adequacy of the prior knowledge provided by those graphs. We propose a
condition graphs should satisfy to provide good prior knowledge and test it
using `Single Gene Inference' tasks. We also compare with randomly generated
graphs, aiming to measure the true benefit of using biologically relevant
graphs in this context, and validate our findings with five clinical tasks. We
find some graphs capture relevant dependencies for most genes while being very
sparse. Our analysis with random graphs finds that dependencies can be captured
almost as well at random which suggests that, in terms of gene expression
levels, the relevant information about the state of the cell is spread across
many genes. | [
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Penetration testing (pentesting) involves performing a controlled attack on a
computer system in order to assess it's security. Although an effective method
for testing security, pentesting requires highly skilled practitioners and
currently there is a growing shortage of skilled cyber security professionals.
One avenue for alleviating this problem is automate the pentesting process
using artificial intelligence techniques. Current approaches to automated
pentesting have relied on model-based planning, however the cyber security
landscape is rapidly changing making maintaining up-to-date models of exploits
a challenge. This project investigated the application of model-free
Reinforcement Learning (RL) to automated pentesting. Model-free RL has the key
advantage over model-based planning of not requiring a model of the
environment, instead learning the best policy through interaction with the
environment. We first designed and built a fast, low compute simulator for
training and testing autonomous pentesting agents. We did this by framing
pentesting as a Markov Decision Process with the known configuration of the
network as states, the available scans and exploits as actions, the reward
determined by the value of machines on the network. We then used this simulator
to investigate the application of model-free RL to pentesting. We tested the
standard Q-learning algorithm using both tabular and neural network based
implementations. We found that within the simulated environment both tabular
and neural network implementations were able to find optimal attack paths for a
range of different network topologies and sizes without having a model of
action behaviour. However, the implemented algorithms were only practical for
smaller networks and numbers of actions. Further work is needed in developing
scalable RL algorithms and testing these algorithms in larger and higher
fidelity environments. | [
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] |
The debate about the ethical implications of Artificial Intelligence dates
from the 1960s. However, in recent years symbolic AI has been complemented and
sometimes replaced by Neural Networks and Machine Learning techniques. This has
vastly increased its potential utility and impact on society, with the
consequence that the ethical debate has gone mainstream. Such debate has
primarily focused on principles - the what of AI ethics - rather than on
practices, the how. Awareness of the potential issues is increasing at a fast
rate, but the AI community's ability to take action to mitigate the associated
risks is still at its infancy. Therefore, our intention in presenting this
research is to contribute to closing the gap between principles and practices
by constructing a typology that may help practically-minded developers apply
ethics at each stage of the pipeline, and to signal to researchers where
further work is needed. The focus is exclusively on Machine Learning, but it is
hoped that the results of this research may be easily applicable to other
branches of AI. The article outlines the research method for creating this
typology, the initial findings, and provides a summary of future research
needs. | [
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A common strategy to deal with the expensive reinforcement learning (RL) of
complex tasks is to decompose them into a collection of subtasks that are
usually simpler to learn as well as reusable for new problems. However, when a
robot learns the policies for these subtasks, common approaches treat every
policy learning process separately. Therefore, all these individual
(composable) policies need to be learned before tackling the learning process
of the complex task through policies composition. Moreover, such composition of
individual policies is usually performed sequentially, which is not suitable
for tasks that require to perform the subtasks concurrently. In this paper, we
propose to combine a set of composable Gaussian policies corresponding to these
subtasks using a set of activation vectors, resulting in a complex Gaussian
policy that is a function of the means and covariances matrices of the
composable policies. Moreover, we propose an algorithm for learning both
compound and composable policies within the same learning process by exploiting
the off-policy data generated from the compound policy. The algorithm is built
on a maximum entropy RL approach to favor exploration during the learning
process. The results of the experiments show that the experience collected with
the compound policy permits not only to solve the complex task but also to
obtain useful composable policies that successfully perform in their
corresponding subtasks. | [
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] |
We study the robustness of accelerated first-order algorithms to stochastic
uncertainties in gradient evaluation. Specifically, for unconstrained, smooth,
strongly convex optimization problems, we examine the mean-squared error in the
optimization variable when the iterates are perturbed by additive white noise.
This type of uncertainty may arise in situations where an approximation of the
gradient is sought through measurements of a real system or in a distributed
computation over a network. Even though the underlying dynamics of first-order
algorithms for this class of problems are nonlinear, we establish upper bounds
on the mean-squared deviation from the optimal solution that are tight up to
constant factors. Our analysis quantifies fundamental trade-offs between noise
amplification and convergence rates obtained via any acceleration scheme
similar to Nesterov's or heavy-ball methods. To gain additional analytical
insight, for strongly convex quadratic problems, we explicitly evaluate the
steady-state variance of the optimization variable in terms of the eigenvalues
of the Hessian of the objective function. We demonstrate that the entire
spectrum of the Hessian, rather than just the extreme eigenvalues, influence
robustness of noisy algorithms. We specialize this result to the problem of
distributed averaging over undirected networks and examine the role of network
size and topology on the robustness of noisy accelerated algorithms. | [
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] |
Recent interest in the external validity of prediction models (i.e., the
problem of different train and test distributions, known as dataset shift) has
produced many methods for finding predictive distributions that are invariant
to dataset shifts and can be used for prediction in new, unseen environments.
However, these methods consider different types of shifts and have been
developed under disparate frameworks, making it difficult to theoretically
analyze how solutions differ with respect to stability and accuracy. Taking a
causal graphical view, we use a flexible graphical representation to express
various types of dataset shifts. Given a known graph of the data generating
process, we show that all invariant distributions correspond to a causal
hierarchy of graphical operators which disable the edges in the graph that are
responsible for the shifts. The hierarchy provides a common theoretical
underpinning for understanding when and how stability to shifts can be
achieved, and in what ways stable distributions can differ. We use it to
establish conditions for minimax optimal performance across environments, and
derive new algorithms that find optimal stable distributions. Using this new
perspective, we empirically demonstrate that that there is a tradeoff between
minimax and average performance. | [
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] |
Knowledge bases (KBs) are the backbone of many ubiquitous applications and
are thus required to exhibit high precision. However, for KBs that store
subjective attributes of entities, e.g., whether a movie is "kid friendly",
simply estimating precision is complicated by the inherent ambiguity in
measuring subjective phenomena. In this work, we develop a method for
constructing KBs with tunable precision--i.e., KBs that can be made to operate
at a specific false positive rate, despite storing both difficult-to-evaluate
subjective attributes and more traditional factual attributes. The key to our
approach is probabilistically modeling user consensus with respect to each
entity-attribute pair, rather than modeling each pair as either True or False.
Uncertainty in the model is explicitly represented and used to control the KB's
precision. We propose three neural networks for fitting the consensus model and
evaluate each one on data from Google Maps--a large KB of locations and their
subjective and factual attributes. The results demonstrate that our learned
models are well-calibrated and thus can successfully be used to control the
KB's precision. Moreover, when constrained to maintain 95% precision, the best
consensus model matches the F-score of a baseline that models each
entity-attribute pair as a binary variable and does not support tunable
precision. When unconstrained, our model dominates the same baseline by 12%
F-score. Finally, we perform an empirical analysis of attribute-attribute
correlations and show that leveraging them effectively contributes to reduced
uncertainty and better performance in attribute prediction. | [
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] |
Sub-optimal control policies in transportation systems negatively impact
mobility, the environment and human health. Developing optimal transportation
control systems at the appropriate scale can be difficult as cities'
transportation systems can be large, complex and stochastic. Intersection
traffic signal controllers are an important element of modern transportation
infrastructure where sub-optimal control policies can incur high costs to many
users. Many adaptive traffic signal controllers have been proposed by the
community but research is lacking regarding their relative performance
difference - which adaptive traffic signal controller is best remains an open
question. This research contributes a framework for developing and evaluating
different adaptive traffic signal controller models in simulation - both
learning and non-learning - and demonstrates its capabilities. The framework is
used to first, investigate the performance variance of the modelled adaptive
traffic signal controllers with respect to their hyperparameters and second,
analyze the performance differences between controllers with optimal
hyperparameters. The proposed framework contains implementations of some of the
most popular adaptive traffic signal controllers from the literature;
Webster's, Max-pressure and Self-Organizing Traffic Lights, along with deep
Q-network and deep deterministic policy gradient reinforcement learning
controllers. This framework will aid researchers by accelerating their work
from a common starting point, allowing them to generate results faster with
less effort. All framework source code is available at
https://github.com/docwza/sumolights. | [
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] |
In critical decision-making scenarios, optimizing accuracy can lead to a
biased classifier, hence past work recommends enforcing group-based fairness
metrics in addition to maximizing accuracy. However, doing so exposes the
classifier to another kind of bias called infra-marginality. This refers to
individual-level bias where some individuals/subgroups can be worse off than
under simply optimizing for accuracy. For instance, a classifier implementing
race-based parity may significantly disadvantage women of the advantaged race.
To quantify this bias, we propose a general notion of $\eta$-infra-marginality
that can be used to evaluate the extent of this bias. We prove theoretically
that, unlike other fairness metrics, infra-marginality does not have a
trade-off with accuracy: high accuracy directly leads to low infra-marginality.
This observation is confirmed through empirical analysis on multiple simulated
and real-world datasets. Further, we find that maximizing group fairness often
increases infra-marginality, suggesting the consideration of both group-level
fairness and individual-level infra-marginality. However, measuring
infra-marginality requires knowledge of the true distribution of
individual-level outcomes correctly and explicitly. We propose a practical
method to measure infra-marginality, and a simple algorithm to maximize
group-wise accuracy and avoid infra-marginality. | [
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] |
The rapid advancement of artificial intelligence (AI) is changing our lives
in many ways. One application domain is data science. New techniques in
automating the creation of AI, known as AutoAI or AutoML, aim to automate the
work practices of data scientists. AutoAI systems are capable of autonomously
ingesting and pre-processing data, engineering new features, and creating and
scoring models based on a target objectives (e.g. accuracy or run-time
efficiency). Though not yet widely adopted, we are interested in understanding
how AutoAI will impact the practice of data science. We conducted interviews
with 20 data scientists who work at a large, multinational technology company
and practice data science in various business settings. Our goal is to
understand their current work practices and how these practices might change
with AutoAI. Reactions were mixed: while informants expressed concerns about
the trend of automating their jobs, they also strongly felt it was inevitable.
Despite these concerns, they remained optimistic about their future job
security due to a view that the future of data science work will be a
collaboration between humans and AI systems, in which both automation and human
expertise are indispensable. | [
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] |
Freezing of gait (FoG) is a common gait disability in Parkinson's disease,
that usually appears in its advanced stage. Freeze episodes are associated with
falls, injuries, and psychological consequences, negatively affecting the
patients' quality of life. For detecting FoG episodes automatically, a highly
accurate detection method is necessary. This paper presents an approach for
detecting FoG episodes utilizing a deep recurrent neural network (RNN) on
3D-accelerometer measurements. We investigate suitable features and feature
combinations extracted from the sensors' time series data. Specifically, for
detecting FoG episodes, we apply a deep RNN with Long Short-Term Memory cells.
In our experiments, we perform both user dependent and user independent
experiments, to detect freeze episodes. Our experimental results show that the
frequency domain features extracted from the trunk sensor are the most
informative feature group in the subject independent method, achieving an
average AUC score of 93%, Specificity of 90% and Sensitivity of 81%. Moreover,
frequency and statistical features of all the sensors are identified as the
best single input for the subject dependent method, achieving an average AUC
score of 97%, Specificity of 96% and Sensitivity of 87%. Overall, in a
comparison to state-of-the-art approaches from literature as baseline methods,
our proposed approach outperforms these significantly. | [
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] |
Sampling-based planning is the predominant paradigm for motion planning in
robotics. Most sampling-based planners use a global random sampling scheme to
guarantee probabilistic completeness. However, most schemes are often
inefficient as the samples drawn from the global proposal distribution, and do
not exploit relevant local structures. Local sampling-based motion planners, on
the other hand, take sequential decisions of random walks to samples valid
trajectories in configuration space. However, current approaches do not adapt
their strategies according to the success and failures of past samples.
In this work, we introduce a local sampling-based motion planner with a
Bayesian learning scheme for modelling an adaptive sampling proposal
distribution. The proposal distribution is sequentially updated based on
previous samples, consequently shaping it according to local obstacles and
constraints in the configuration space. Thus, through learning from past
observed outcomes, we maximise the likelihood of sampling in regions that have
a higher probability to form trajectories within narrow passages. We provide
the formulation of a sample-efficient distribution, along with theoretical
foundation of sequentially updating this distribution. We demonstrate
experimentally that by using a Bayesian proposal distribution, a solution is
found faster, requiring fewer samples, and without any noticeable performance
overhead. | [
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] |
A key step in medical diagnosis is giving the patient a universally
recognized label (e.g. Appendicitis) which essentially assigns the patient to a
class(es) of patients with similar body failures. However, two patients having
the same disease label(s) with high probability may still have differences in
their feature manifestation patterns implying differences in the required
treatments. Additionally, in many cases, the labels of the primary diagnoses
leave some findings unexplained. Medical diagnosis is only partially about
probability calculations for label X or Y. Diagnosis is not complete until the
patient overall situation is clinically understood to the level that enables
the best therapeutic decisions. Most machine learning models are data centric
models, and evidence so far suggest they can reach expert level performance in
the disease labeling phase. Nonetheless, like any other mathematical technique,
they have their limitations and applicability scope. Primarily, data centric
algorithms are knowledge blind and lack anatomy and physiology knowledge that
physicians leverage to achieve complete diagnosis. This article advocates to
complement them with intelligence to overcome their inherent limitations as
knowledge blind algorithms. Machines can learn many things from data, but data
is not the only source that machines can learn from. Historic patient data only
tells us what the possible manifestations of a certain body failure are.
Anatomy and physiology knowledge tell us how the body works and fails. Both are
needed for complete diagnosis. The proposed Double Deep Learning approach,
along with the initiative for Medical Wikipedia for Smart Machines, leads to AI
diagnostic support solutions for complete diagnosis beyond the limited data
only labeling solutions we see today. AI for medicine will forever be limited
until their intelligence also integrates anatomy and physiology. | [
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] |
This paper studies a fundamental mechanism of how to detect a conflict
between arguments given sentiments regarding acceptability of the arguments. We
introduce a concept of the inverse problem of the abstract argumentation to
tackle the problem. Given noisy sets of acceptable arguments, it aims to find
attack relations explaining the sets well in terms of acceptability semantics.
It is the inverse of the direct problem corresponding to the traditional
problem of the abstract argumentation that focuses on finding sets of
acceptable arguments in terms of the semantics given an attack relation between
the arguments. We give a probabilistic model handling both of the problems in a
way that is faithful to the acceptability semantics. From a theoretical point
of view, we show that a solution to both the direct and inverse problems is a
special case of the probabilistic inference on the model. We discuss that the
model provides a natural extension of the semantics to cope with uncertain
attack relations distributed probabilistically. From en empirical point of
view, we argue that it reasonably predicts individuals sentiments regarding
acceptability of arguments. This paper contributes to lay the foundation for
making acceptability semantics data-driven and to provide a way to tackle the
knowledge acquisition bottleneck. | [
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] |
Vision-based grasping systems typically adopt an open-loop execution of a
planned grasp. This policy can fail due to many reasons, including ubiquitous
calibration error. Recovery from a failed grasp is further complicated by
visual occlusion, as the hand is usually occluding the vision sensor as it
attempts another open-loop regrasp. This work presents MAT, a tactile
closed-loop method capable of realizing grasps provided by a coarse initial
positioning of the hand above an object. Our algorithm is a deep reinforcement
learning (RL) policy optimized through the clipped surrogate objective within a
maximum entropy RL framework to balance exploitation and exploration. The
method utilizes tactile and proprioceptive information to act through both fine
finger motions and larger regrasp movements to execute stable grasps. A novel
curriculum of action motion magnitude makes learning more tractable and helps
turn common failure cases into successes. Careful selection of features that
exhibit small sim-to-real gaps enables this tactile grasping policy, trained
purely in simulation, to transfer well to real world environments without the
need for additional learning. Experimentally, this methodology improves over a
vision-only grasp success rate substantially on a multi-fingered robot hand.
When this methodology is used to realize grasps from coarse initial positions
provided by a vision-only planner, the system is made dramatically more robust
to calibration errors in the camera-robot transform. | [
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] |
Traditional planning and control methods could fail to find a feasible
trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
This is because the obstacle-free volume in spacetime is very small in these
scenarios for the vehicle to drive through. However, that does not mean the
task is infeasible since human drivers are known to be able to drive amongst
dense traffic by leveraging the cooperativeness of other drivers to open a gap.
The traditional methods fail to take into account the fact that the actions
taken by an agent affect the behaviour of other vehicles on the road. In this
work, we rely on the ability of deep reinforcement learning to implicitly model
such interactions and learn a continuous control policy over the action space
of an autonomous vehicle. The application we consider requires our agent to
negotiate and open a gap in the road in order to successfully merge or change
lanes. Our policy learns to repeatedly probe into the target road lane while
trying to find a safe spot to move in to. We compare against two
model-predictive control-based algorithms and show that our policy outperforms
them in simulation. | [
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] |
Traditional Reinforcement Learning (RL) problems depend on an exhaustive
simulation environment that models real-world physics of the problem and trains
the RL agent by observing this environment. In this paper, we present a novel
approach to creating an environment by modeling the reward function based on
empirical rules extracted from human domain knowledge of the system under
study. Using this empirical rewards function, we will build an environment and
train the agent. We will first create an environment that emulates the effect
of setting cabin temperature through thermostat. This is typically done in RL
problems by creating an exhaustive model of the system with detailed
thermodynamic study. Instead, we propose an empirical approach to model the
reward function based on human domain knowledge. We will document some rules of
thumb that we usually exercise as humans while setting thermostat temperature
and try and model these into our reward function. This modeling of empirical
human domain rules into a reward function for RL is the unique aspect of this
paper. This is a continuous action space problem and using deep deterministic
policy gradient (DDPG) method, we will solve for maximizing the reward
function. We will create a policy network that predicts optimal temperature
setpoint given external temperature and humidity. | [
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] |
Clinical depression or Major Depressive Disorder (MDD) is a common and
serious medical illness. In this paper, a deep recurrent neural network-based
framework is presented to detect depression and to predict its severity level
from speech. Low-level and high-level audio features are extracted from audio
recordings to predict the 24 scores of the Patient Health Questionnaire and the
binary class of depression diagnosis. To overcome the problem of the small size
of Speech Depression Recognition (SDR) datasets, expanding training labels and
transferred features are considered. The proposed approach outperforms the
state-of-art approaches on the DAIC-WOZ database with an overall accuracy of
76.27% and a root mean square error of 0.4 in assessing depression, while a
root mean square error of 0.168 is achieved in predicting the depression
severity levels. The proposed framework has several advantages (fastness,
non-invasiveness, and non-intrusion), which makes it convenient for real-time
applications. The performances of the proposed approach are evaluated under a
multi-modal and a multi-features experiments. MFCC based high-level features
hold relevant information related to depression. Yet, adding visual action
units and different other acoustic features further boosts the classification
results by 20% and 10% to reach an accuracy of 95.6% and 86%, respectively.
Considering visual-facial modality needs to be carefully studied as it sparks
patient privacy concerns while adding more acoustic features increases the
computation time. | [
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] |
A consequence of the fragmented and siloed healthcare landscape is that
patient care (and data) is split along multitude of different facilities and
computer systems and enabling interoperability between these systems is hard.
The lack interoperability not only hinders continuity of care and burdens
providers, but also hinders effective application of Machine Learning (ML)
algorithms. Thus, most current ML algorithms, designed to understand patient
care and facilitate clinical decision-support, are trained on limited datasets.
This approach is analogous to the Newtonian paradigm of Reductionism in which a
system is broken down into elementary components and a description of the whole
is formed by understanding those components individually. A key limitation of
the reductionist approach is that it ignores the component-component
interactions and dynamics within the system which are often of prime
significance in understanding the overall behaviour of complex adaptive systems
(CAS). Healthcare is a CAS.
Though the application of ML on health data have shown incremental
improvements for clinical decision support, ML has a much a broader potential
to restructure care delivery as a whole and maximize care value. However, this
ML potential remains largely untapped: primarily due to functional limitations
of Electronic Health Records (EHR) and the inability to see the healthcare
system as a whole. This viewpoint (i) articulates the healthcare as a complex
system which has a biological and an organizational perspective, (ii) motivates
with examples, the need of a system's approach when addressing healthcare
challenges via ML and, (iii) emphasizes to unleash EHR functionality - while
duly respecting all ethical and legal concerns - to reap full benefits of ML. | [
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] |
In autonomous navigation, a planning system reasons about other agents to
plan a safe and plausible trajectory. Before planning starts, agents are
typically processed with computationally intensive models for recognition,
tracking, motion estimation and prediction. With limited computational
resources and a large number of agents to process in real time, it becomes
important to efficiently rank agents according to their impact on the decision
making process. This allows spending more time processing the most important
agents. We propose a system to rank agents around an autonomous vehicle (AV) in
real time. We automatically generate a ranking data set by running the planner
in simulation on real-world logged data, where we can afford to run more
accurate and expensive models on all the agents. The causes of various planner
actions are logged and used for assigning ground truth importance scores. The
generated data set can be used to learn ranking models. In particular, we show
the utility of combining learned features, via a convolutional neural network,
with engineered features designed to capture domain knowledge. We show the
benefits of various design choices experimentally. When tested on real AVs, our
system demonstrates the capability of understanding complex driving situations. | [
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] |
Reasoning about graphs evolving over time is a challenging concept in many
domains, such as bioinformatics, physics, and social networks. We consider a
common case in which edges can be short term interactions (e.g., messaging) or
long term structural connections (e.g., friendship). In practice, long term
edges are often specified by humans. Human-specified edges can be both
expensive to produce and suboptimal for the downstream task. To alleviate these
issues, we propose a model based on temporal point processes and variational
autoencoders that learns to infer temporal attention between nodes by observing
node communication. As temporal attention drives between-node feature
propagation, using the dynamics of node interactions to learn this key
component provides more flexibility while simultaneously avoiding issues
associated with human-specified edges. We also propose a bilinear
transformation layer for pairs of node features instead of concatenation,
typically used in prior work, and demonstrate its superior performance in all
cases. In experiments on two datasets in the dynamic link prediction task, our
model often outperforms the baseline model that requires a human-specified
graph. Moreover, our learned attention is semantically interpretable and infers
connections similar to actual graphs. | [
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] |
The Optimal Power Flow (OPF) problem is a fundamental building block for the
optimization of electrical power systems. It is nonlinear and nonconvex and
computes the generator setpoints for power and voltage, given a set of load
demands. It is often needed to be solved repeatedly under various conditions,
either in real-time or in large-scale studies. This need is further exacerbated
by the increasing stochasticity of power systems due to renewable energy
sources in front and behind the meter. To address these challenges, this paper
presents a deep learning approach to the OPF. The learning model exploits the
information available in the prior states of the system (which is commonly
available in practical applications), as well as a dual Lagrangian method to
satisfy the physical and engineering constraints present in the OPF. The
proposed model is evaluated on a large collection of realistic power systems.
The experimental results show that its predictions are highly accurate with
average errors as low as 0.2%. Additionally, the proposed approach is shown to
improve the accuracy of widely adopted OPF linear DC approximation by at least
two orders of magnitude. | [
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] |
Deep Reinforcement Learning (RL) is a promising approach for adaptive robot
control, but its current application to robotics is currently hindered by high
sample requirements. To alleviate this issue, we propose to exploit the
symmetries present in robotic tasks. Intuitively, symmetries from observed
trajectories define transformations that leave the space of feasible RL
trajectories invariant and can be used to generate new feasible trajectories,
which could be used for training. Based on this data augmentation idea, we
formulate a general framework, called Invariant Transform Experience Replay
that we present with two techniques: (i) Kaleidoscope Experience Replay
exploits reflectional symmetries and (ii) Goal-augmented Experience Replay
which takes advantage of lax goal definitions. In the Fetch tasks from OpenAI
Gym, our experimental results show significant increases in learning rates and
success rates. Particularly, we attain a 13, 3, and 5 times speedup in the
pushing, sliding, and pick-and-place tasks respectively in the multi-goal
setting. Performance gains are also observed in similar tasks with obstacles
and we successfully deployed a trained policy on a real Baxter robot. Our work
demonstrates that invariant transformations on RL trajectories are a promising
methodology to speed up learning in deep RL. | [
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] |
Visual exploration and smart data collection via autonomous vehicles is an
attractive topic in various disciplines. Disturbances like wind significantly
influence both the power consumption of the flying robots and the performance
of the camera. We propose a reinforcement learning approach which combines the
effects of the power consumption and the object detection modules to develop a
policy for object detection in large areas with limited battery life. The
learning model enables dynamic learning of the negative rewards of each action
based on the drag forces that is resulted by the motion of the flying robot
with respect to the wind field. The algorithm is implemented in a near-real
world simulation environment both for the planar motion and flight in different
altitudes. The trained agent often performed a trade-off between detecting the
objects with high accuracy and increasing the area coverage within its battery
life. The developed exploration policy outperformed the complete coverage
algorithm by minimizing the traveled path while finding the target objects. The
performance of the algorithms under various wind fields was evaluated in planar
and 3D motion. During an exploration task with sparsely distributed goals and
within a UAV's battery life, the proposed architecture could detect more than
twice the amount of goal objects compared to the coverage path planning
algorithm in moderate wind field. In high wind intensities, the energy-aware
algorithm could detect 4 times the amount of goal objects when compared to its
complete coverage counterpart. | [
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] |
This paper describes a purely data-driven solution to a class of sequential
decision-making problems with a large number of concurrent online decisions,
with applications to computing systems and operations research. We assume that
while the micro-level behaviour of the system can be broadly captured by
analytical expressions or simulation, the macro-level or emergent behaviour is
complicated by non-linearity, constraints, and stochasticity. If we represent
the set of concurrent decisions to be computed as a vector, each element of the
vector is assumed to be a continuous variable, and the number of such elements
is arbitrarily large and variable from one problem instance to another. We
first formulate the decision-making problem as a canonical reinforcement
learning (RL) problem, which can be solved using purely data-driven techniques.
We modify a standard approach known as advantage actor critic (A2C) to ensure
its suitability to the problem at hand, and compare its performance to that of
baseline approaches on the specific instance of a multi-product inventory
management task. The key modifications include a parallelised formulation of
the decision-making task, and a training procedure that explicitly recognises
the quantitative relationship between different decisions. We also present
experimental results probing the learned policies, and their robustness to
variations in the data. | [
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