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Model predictive control (MPC) is a popular control method that has proved
effective for robotics, among other fields. MPC performs re-planning at every
time step. Re-planning is done with a limited horizon per computational and
real-time constraints and often also for robustness to potential model errors.
However, the limited horizon leads to suboptimal performance. In this work, we
consider the iterative learning setting, where the same task can be repeated
several times, and propose a policy improvement scheme for MPC. The main idea
is that between executions we can, offline, run MPC with a longer horizon,
resulting in a hindsight plan. To bring the next real-world execution closer to
the hindsight plan, our approach learns to re-shape the original cost function
with the goal of satisfying the following property: short horizon planning (as
realistic during real executions) with respect to the shaped cost should result
in mimicking the hindsight plan. This effectively consolidates long-term
reasoning into the short-horizon planning. We empirically evaluate our approach
in contact-rich manipulation tasks both in simulated and real environments,
such as peg insertion by a real PR2 robot. | [
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] |
Autonomous systems can be used to search for sparse signals in a large space;
e.g., aerial robots can be deployed to localize threats, detect gas leaks, or
respond to distress calls. Intuitively, search algorithms may increase
efficiency by collecting aggregate measurements summarizing large contiguous
regions. However, most existing search methods either ignore the possibility of
such region observations (e.g., Bayesian optimization and multi-armed bandits)
or make strong assumptions about the sensing mechanism that allow each
measurement to arbitrarily encode all signals in the entire environment (e.g.,
compressive sensing). We propose an algorithm that actively collects data to
search for sparse signals using only noisy measurements of the average values
on rectangular regions (including single points), based on the greedy
maximization of information gain. We analyze our algorithm in 1d and show that
it requires $\tilde{O}(\frac{n}{\mu^2}+k^2)$ measurements to recover all of $k$
signal locations with small Bayes error, where $\mu$ and $n$ are the signal
strength and the size of the search space, respectively. We also show that
active designs can be fundamentally more efficient than passive designs with
region sensing, contrasting with the results of Arias-Castro, Candes, and
Davenport (2013). We demonstrate the empirical performance of our algorithm on
a search problem using satellite image data and in high dimensions. | [
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] |
Prediction in a small-sized sample with a large number of covariates, the
"small n, large p" problem, is challenging. This setting is encountered in
multiple applications, such as precision medicine, where obtaining additional
samples can be extremely costly or even impossible, and extensive research
effort has recently been dedicated to finding principled solutions for accurate
prediction. However, a valuable source of additional information, domain
experts, has not yet been efficiently exploited. We formulate knowledge
elicitation generally as a probabilistic inference process, where expert
knowledge is sequentially queried to improve predictions. In the specific case
of sparse linear regression, where we assume the expert has knowledge about the
values of the regression coefficients or about the relevance of the features,
we propose an algorithm and computational approximation for fast and efficient
interaction, which sequentially identifies the most informative features on
which to query expert knowledge. Evaluations of our method in experiments with
simulated and real users show improved prediction accuracy already with a small
effort from the expert. | [
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] |
Given a knowledge base or KB containing (noisy) facts about common nouns or
generics, such as "all trees produce oxygen" or "some animals live in forests",
we consider the problem of inferring additional such facts at a precision
similar to that of the starting KB. Such KBs capture general knowledge about
the world, and are crucial for various applications such as question answering.
Different from commonly studied named entity KBs such as Freebase, generics KBs
involve quantification, have more complex underlying regularities, tend to be
more incomplete, and violate the commonly used locally closed world assumption
(LCWA). We show that existing KB completion methods struggle with this new
task, and present the first approach that is successful. Our results
demonstrate that external information, such as relation schemas and entity
taxonomies, if used appropriately, can be a surprisingly powerful tool in this
setting. First, our simple yet effective knowledge guided tensor factorization
approach achieves state-of-the-art results on two generics KBs (80% precise)
for science, doubling their size at 74%-86% precision. Second, our novel
taxonomy guided, submodular, active learning method for collecting annotations
about rare entities (e.g., oriole, a bird) is 6x more effective at inferring
further new facts about them than multiple active learning baselines. | [
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] |
A key drawback of the current generation of artificial decision-makers is
that they do not adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, will optimize behavior with respect to an
objective function that must be evaluated and learned through simulations. Once
this objective function has been modeled, the agent can then choose its desired
behavior in different situations. Bayesian optimization with a Gaussian Process
surrogate is used as the method for investigating the objective function. One
key benefit is that during optimization the Gaussian Process learns a global
estimate of the true objective function, with predicted outcomes and a
statistical measure of confidence in areas that haven't been investigated yet.
However, standard Bayesian optimization does not perform consistently or
provide an accurate Gaussian Process surrogate function for highly volatile
objective functions. We treat these problems by introducing a novel sampling
technique called Hybrid Repeat/Multi-point Sampling. This technique gives the
AI ability to learn optimum behaviors in a highly uncertain environment. More
importantly, it not only improves the reliability of the optimization, but also
creates a better model of the entire objective surface. With this improved
model the agent is equipped to better adapt behaviors. | [
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] |
A key requirement for the current generation of artificial decision-makers is
that they should adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, must optimize behavior with respect to an
objective function that is evaluated and learned through simulations. Bayesian
optimization with a Gaussian Process surrogate is used as the method for
investigating the objective function. One key benefit is that during
optimization, the Gaussian Process learns a global estimate of the true
objective function, with predicted outcomes and a statistical measure of
confidence in areas that haven't been investigated yet. Having a model of the
objective function is important for being able to understand possible outcomes
in the decision space; for example this is crucial for training and providing
feedback to human pilots. However, standard Bayesian optimization does not
perform consistently or provide an accurate Gaussian Process surrogate function
for highly volatile objective functions. We treat these problems by introducing
a novel sampling technique called Hybrid Repeat/Multi-point Sampling. This
technique gives the AI ability to learn optimum behaviors in a highly uncertain
environment. More importantly, it not only improves the reliability of the
optimization, but also creates a better model of the entire objective surface.
With this improved model the agent is equipped to more accurately/efficiently
predict performance in unexplored scenarios. | [
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] |
We demonstrate the possibility of classifying causal systems into kinds that
share a common structure without first constructing an explicit dynamical model
or using prior knowledge of the system dynamics. The algorithmic ability to
determine whether arbitrary systems are governed by causal relations of the
same form offers significant practical applications in the development and
validation of dynamical models. It is also of theoretical interest as an
essential stage in the scientific inference of laws from empirical data. The
algorithm presented is based on the dynamical symmetry approach to dynamical
kinds. A dynamical symmetry with respect to time is an intervention on one or
more variables of a system that commutes with the time evolution of the system.
A dynamical kind is a class of systems sharing a set of dynamical symmetries.
The algorithm presented classifies deterministic, time-dependent causal systems
by directly comparing their exhibited symmetries. Using simulated, noisy data
from a variety of nonlinear systems, we show that this algorithm correctly
sorts systems into dynamical kinds. It is robust under significant sampling
error, is immune to violations of normality in sampling error, and fails
gracefully with increasing dynamical similarity. The algorithm we demonstrate
is the first to address this aspect of automated scientific discovery. | [
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] |
Deep learning techniques have been widely applied, achieving state-of-the-art
results in various fields of study. This survey focuses on deep learning
solutions that target learning control policies for robotics applications. We
carry out our discussions on the two main paradigms for learning control with
deep networks: deep reinforcement learning and imitation learning. For deep
reinforcement learning (DRL), we begin from traditional reinforcement learning
algorithms, showing how they are extended to the deep context and effective
mechanisms that could be added on top of the DRL algorithms. We then introduce
representative works that utilize DRL to solve navigation and manipulation
tasks in robotics. We continue our discussion on methods addressing the
challenge of the reality gap for transferring DRL policies trained in
simulation to real-world scenarios, and summarize robotics simulation platforms
for conducting DRL research. For imitation leaning, we go through its three
main categories, behavior cloning, inverse reinforcement learning and
generative adversarial imitation learning, by introducing their formulations
and their corresponding robotics applications. Finally, we discuss the open
challenges and research frontiers. | [
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] |
To ease the development of robot learning in industry, two conditions need to
be fulfilled. Manipulators must be able to learn high accuracy and precision
tasks while being safe for workers in the factory. In this paper, we extend
previously submitted work which consists in rapid learning of local high
accuracy behaviors. By exploration and regression, linear and quadratic models
are learnt for respectively the dynamics and cost function. Iterative Linear
Quadratic Gaussian Regulator combined with cost quadratic regression can
converge rapidly in the final stages towards high accuracy behavior as the cost
function is modelled quite precisely. In this paper, both a different cost
function and a second order improvement method are implemented within this
framework. We also propose an analysis of the algorithm parameters through
simulation for a positioning task. Finally, an experimental validation on a
KUKA LBR iiwa robot is carried out. This collaborative robot manipulator can be
easily programmed into safety mode, which makes it qualified for the second
industry constraint stated above. | [
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] |
Deep learning techniques lie at the heart of several significant AI advances
in recent years including object recognition and detection, image captioning,
machine translation, speech recognition and synthesis, and playing the game of
Go. Automated first-order theorem provers can aid in the formalization and
verification of mathematical theorems and play a crucial role in program
analysis, theory reasoning, security, interpolation, and system verification.
Here we suggest deep learning based guidance in the proof search of the theorem
prover E. We train and compare several deep neural network models on the traces
of existing ATP proofs of Mizar statements and use them to select processed
clauses during proof search. We give experimental evidence that with a hybrid,
two-phase approach, deep learning based guidance can significantly reduce the
average number of proof search steps while increasing the number of theorems
proved. Using a few proof guidance strategies that leverage deep neural
networks, we have found first-order proofs of 7.36% of the first-order logic
translations of the Mizar Mathematical Library theorems that did not previously
have ATP generated proofs. This increases the ratio of statements in the corpus
with ATP generated proofs from 56% to 59%. | [
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] |
We consider a scheduling problem where a cloud service provider has multiple
units of a resource available over time. Selfish clients submit jobs, each with
an arrival time, deadline, length, and value. The service provider's goal is to
implement a truthful online mechanism for scheduling jobs so as to maximize the
social welfare of the schedule. Recent work shows that under a stochastic
assumption on job arrivals, there is a single-parameter family of mechanisms
that achieves near-optimal social welfare. We show that given any such family
of near-optimal online mechanisms, there exists an online mechanism that in the
worst case performs nearly as well as the best of the given mechanisms. Our
mechanism is truthful whenever the mechanisms in the given family are truthful
and prompt, and achieves optimal (within constant factors) regret.
We model the problem of competing against a family of online scheduling
mechanisms as one of learning from expert advice. A primary challenge is that
any scheduling decisions we make affect not only the payoff at the current
step, but also the resource availability and payoffs in future steps.
Furthermore, switching from one algorithm (a.k.a. expert) to another in an
online fashion is challenging both because it requires synchronization with the
state of the latter algorithm as well as because it affects the incentive
structure of the algorithms. We further show how to adapt our algorithm to a
non-clairvoyant setting where job lengths are unknown until jobs are run to
completion. Once again, in this setting, we obtain truthfulness along with
asymptotically optimal regret (within poly-logarithmic factors). | [
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A matching in a two-sided market often incurs an externality: a matched
resource may become unavailable to the other side of the market, at least for a
while. This is especially an issue in online platforms involving human experts
as the expert resources are often scarce. The efficient utilization of experts
in these platforms is made challenging by the fact that the information
available about the parties involved is usually limited.
To address this challenge, we develop a model of a task-expert matching
system where a task is matched to an expert using not only the prior
information about the task but also the feedback obtained from the past
matches. In our model the tasks arrive online while the experts are fixed and
constrained by a finite service capacity. For this model, we characterize the
maximum task resolution throughput a platform can achieve. We show that the
natural greedy approaches where each expert is assigned a task most suitable to
her skill is suboptimal, as it does not internalize the above externality. We
develop a throughput optimal backpressure algorithm which does so by accounting
for the `congestion' among different task types. Finally, we validate our model
and confirm our theoretical findings with data-driven simulations via logs of
Math.StackExchange, a StackOverflow forum dedicated to mathematics. | [
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] |
Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects. | [
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] |
Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal control problem. Once trained, the policy can compute the
optimal next contacting body part (e.g. left foot, right foot, or hands),
contact location and timing, and the required joint actuation. We represent the
policy as a mixture of actor-critic neural network, which consists of n control
policies and the corresponding value functions. Each pair of actor-critic is
associated with one of the n possible contacting body parts. During execution,
the policy corresponding to the highest value function will be executed while
the associated body part will be the next contact with the ground. With this
mixture of actor-critic architecture, the discrete contact sequence planning is
solved through the selection of the best critics while the continuous control
problem is solved by the optimization of actors. We show that our policy can
achieve comparable, sometimes even higher, rewards than a recursive search of
the action space using dynamic programming, while enjoying 50 to 400 times of
speed gain during online execution. | [
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] |
Bayesian optimization has been successful at global optimization of
expensive-to-evaluate multimodal objective functions. However, unlike most
optimization methods, Bayesian optimization typically does not use derivative
information. In this paper we show how Bayesian optimization can exploit
derivative information to decrease the number of objective function evaluations
required for good performance. In particular, we develop a novel Bayesian
optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for
which we show one-step Bayes-optimality, asymptotic consistency, and greater
one-step value of information than is possible in the derivative-free setting.
Our procedure accommodates noisy and incomplete derivative information, comes
in both sequential and batch forms, and can optionally reduce the computational
cost of inference through automatically selected retention of a single
directional derivative. We also compute the d-KG acquisition function and its
gradient using a novel fast discretization-free technique. We show d-KG
provides state-of-the-art performance compared to a wide range of optimization
procedures with and without gradients, on benchmarks including logistic
regression, deep learning, kernel learning, and k-nearest neighbors. | [
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] |
We consider the optimal value of information (VoI) problem, where the goal is
to sequentially select a set of tests with a minimal cost, so that one can
efficiently make the best decision based on the observed outcomes. Existing
algorithms are either heuristics with no guarantees, or scale poorly (with
exponential run time in terms of the number of available tests). Moreover,
these methods assume a known distribution over the test outcomes, which is
often not the case in practice. We propose an efficient sampling-based online
learning framework to address the above issues. First, assuming the
distribution over hypotheses is known, we propose a dynamic hypothesis
enumeration strategy, which allows efficient information gathering with strong
theoretical guarantees. We show that with sufficient amount of samples, one can
identify a near-optimal decision with high probability. Second, when the
parameters of the hypotheses distribution are unknown, we propose an algorithm
which learns the parameters progressively via posterior sampling in an online
fashion. We further establish a rigorous bound on the expected regret. We
demonstrate the effectiveness of our approach on a real-world interactive
troubleshooting application and show that one can efficiently make high-quality
decisions with low cost. | [
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] |
We provide new results for noise-tolerant and sample-efficient learning
algorithms under $s$-concave distributions. The new class of $s$-concave
distributions is a broad and natural generalization of log-concavity, and
includes many important additional distributions, e.g., the Pareto distribution
and $t$-distribution. This class has been studied in the context of efficient
sampling, integration, and optimization, but much remains unknown about the
geometry of this class of distributions and their applications in the context
of learning. The challenge is that unlike the commonly used distributions in
learning (uniform or more generally log-concave distributions), this broader
class is not closed under the marginalization operator and many such
distributions are fat-tailed. In this work, we introduce new convex geometry
tools to study the properties of $s$-concave distributions and use these
properties to provide bounds on quantities of interest to learning including
the probability of disagreement between two halfspaces, disagreement outside a
band, and the disagreement coefficient. We use these results to significantly
generalize prior results for margin-based active learning, disagreement-based
active learning, and passive learning of intersections of halfspaces. Our
analysis of geometric properties of $s$-concave distributions might be of
independent interest to optimization more broadly. | [
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] |
Policy iteration (PI) is a recursive process of policy evaluation and
improvement for solving an optimal decision-making/control problem, or in other
words, a reinforcement learning (RL) problem. PI has also served as the
fundamental for developing RL methods. In this paper, we propose two PI
methods, called differential PI (DPI) and integral PI (IPI), and their
variants, for a general RL framework in continuous time and space (CTS), where
the environment is modeled by a system of ordinary differential equations
(ODEs). The proposed methods inherit the current ideas of PI in classical RL
and optimal control and theoretically support the existing RL algorithms in
CTS: TD-learning and value-gradient-based (VGB) greedy policy update. We also
provide case studies including 1) discounted RL and 2) optimal control tasks.
Fundamental mathematical properties -- admissibility, uniqueness of the
solution to the Bellman equation (BE), monotone improvement, convergence, and
optimality of the solution to the Hamilton-Jacobi-Bellman equation (HJBE) --
are all investigated in-depth and improved from the existing theory, along with
the general and case studies. Finally, the proposed ones are simulated with an
inverted-pendulum model and their model-based and partially model-free
implementations to support the theory and further investigate them beyond. | [
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] |
We propose an algorithm to separate simultaneously speaking persons from each
other, the "cocktail party problem", using a single microphone. Our approach
involves a deep recurrent neural networks regression to a vector space that is
descriptive of independent speakers. Such a vector space can embed empirically
determined speaker characteristics and is optimized by distinguishing between
speaker masks. We call this technique source-contrastive estimation. The
methodology is inspired by negative sampling, which has seen success in natural
language processing, where an embedding is learned by correlating and
de-correlating a given input vector with output weights. Although the matrix
determined by the output weights is dependent on a set of known speakers, we
only use the input vectors during inference. Doing so will ensure that source
separation is explicitly speaker-independent. Our approach is similar to recent
deep neural network clustering and permutation-invariant training research; we
use weighted spectral features and masks to augment individual speaker
frequencies while filtering out other speakers. We avoid, however, the severe
computational burden of other approaches with our technique. Furthermore, by
training a vector space rather than combinations of different speakers or
differences thereof, we avoid the so-called permutation problem during
training. Our algorithm offers an intuitive, computationally efficient response
to the cocktail party problem, and most importantly boasts better empirical
performance than other current techniques. | [
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] |
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN)
have achieved state-of-the-art results in a variety of challenging,
high-dimensional domains. This success is mainly attributed to the power of
deep neural networks to learn rich domain representations for approximating the
value function or policy. Batch reinforcement learning methods with linear
representations, on the other hand, are more stable and require less hyper
parameter tuning. Yet, substantial feature engineering is necessary to achieve
good results. In this work we propose a hybrid approach -- the Least Squares
Deep Q-Network (LS-DQN), which combines rich feature representations learned by
a DRL algorithm with the stability of a linear least squares method. We do this
by periodically re-training the last hidden layer of a DRL network with a batch
least squares update. Key to our approach is a Bayesian regularization term for
the least squares update, which prevents over-fitting to the more recent data.
We tested LS-DQN on five Atari games and demonstrate significant improvement
over vanilla DQN and Double-DQN. We also investigated the reasons for the
superior performance of our method. Interestingly, we found that the
performance improvement can be attributed to the large batch size used by the
LS method when optimizing the last layer. | [
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In the field of reinforcement learning there has been recent progress towards
safety and high-confidence bounds on policy performance. However, to our
knowledge, no practical methods exist for determining high-confidence policy
performance bounds in the inverse reinforcement learning setting---where the
true reward function is unknown and only samples of expert behavior are given.
We propose a sampling method based on Bayesian inverse reinforcement learning
that uses demonstrations to determine practical high-confidence upper bounds on
the $\alpha$-worst-case difference in expected return between any evaluation
policy and the optimal policy under the expert's unknown reward function. We
evaluate our proposed bound on both a standard grid navigation task and a
simulated driving task and achieve tighter and more accurate bounds than a
feature count-based baseline. We also give examples of how our proposed bound
can be utilized to perform risk-aware policy selection and risk-aware policy
improvement. Because our proposed bound requires several orders of magnitude
fewer demonstrations than existing high-confidence bounds, it is the first
practical method that allows agents that learn from demonstration to express
confidence in the quality of their learned policy. | [
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] |
We describe the Inspire system which participated in the first competition on
Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming
(ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis
space generation: given a set of facts representing the mode bias, and a set of
cost configuration parameters, each answer set of this encoding represents a
single rule that is considered for finding a hypothesis that entails the given
examples. Compared with state-of-the-art methods that use the length of the
rule body as a metric for rule complexity, our approach permits a much more
fine-grained specification of the shape of hypothesis candidate rules. The
Inspire system iteratively increases the rule cost limit and thereby increases
the search space until it finds a suitable hypothesis. The system searches for
a hypothesis that entails a single example at a time, utilizing an ASP encoding
derived from the encoding used in XHAIL. We perform experiments with the
development and test set of the ILP competition. For comparison we also adapted
the ILASP system to process competition instances. Experimental results show
that the cost parameters for the hypothesis search space are an important
factor for finding hypotheses to competition instances within tight resource
bounds. | [
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] |
Robotic motion planning problems are typically solved by constructing a
search tree of valid maneuvers from a start to a goal configuration. Limited
onboard computation and real-time planning constraints impose a limit on how
large this search tree can grow. Heuristics play a crucial role in such
situations by guiding the search towards potentially good directions and
consequently minimizing search effort. Moreover, it must infer such directions
in an efficient manner using only the information uncovered by the search up
until that time. However, state of the art methods do not address the problem
of computing a heuristic that explicitly minimizes search effort. In this
paper, we do so by training a heuristic policy that maps the partial
information from the search to decide which node of the search tree to expand.
Unfortunately, naively training such policies leads to slow convergence and
poor local minima. We present SaIL, an efficient algorithm that trains
heuristic policies by imitating "clairvoyant oracles" - oracles that have full
information about the world and demonstrate decisions that minimize search
effort. We leverage the fact that such oracles can be efficiently computed
using dynamic programming and derive performance guarantees for the learnt
heuristic. We validate the approach on a spectrum of environments which show
that SaIL consistently outperforms state of the art algorithms. Our approach
paves the way forward for learning heuristics that demonstrate an anytime
nature - finding feasible solutions quickly and incrementally refining it over
time. | [
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We study revenue optimization learning algorithms for repeated posted-price
auctions where a seller interacts with a single strategic buyer that holds a
fixed private valuation for a good and seeks to maximize his cumulative
discounted surplus. For this setting, first, we propose a novel algorithm that
never decreases offered prices and has a tight strategic regret bound in
$\Theta(\log\log T)$ under some mild assumptions on the buyer surplus
discounting. This result closes the open research question on the existence of
a no-regret horizon-independent weakly consistent pricing. The proposed
algorithm is inspired by our observation that a double decrease of offered
prices in a weakly consistent algorithm is enough to cause a linear regret.
This motivates us to construct a novel transformation that maps a
right-consistent algorithm to a weakly consistent one that never decreases
offered prices.
Second, we outperform the previously known strategic regret upper bound of
the algorithm PRRFES, where the improvement is achieved by means of a finer
constant factor $C$ of the principal term $C\log\log T$ in this upper bound.
Finally, we generalize results on strategic regret previously known for
geometric discounting of the buyer's surplus to discounting of other types,
namely: the optimality of the pricing PRRFES to the case of geometrically
concave decreasing discounting; and linear lower bound on the strategic regret
of a wide range of horizon-independent weakly consistent algorithms to the case
of arbitrary discounts. | [
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] |
As intelligent systems gain autonomy and capability, it becomes vital to
ensure that their objectives match those of their human users; this is known as
the value-alignment problem. In robotics, value alignment is key to the design
of collaborative robots that can integrate into human workflows, successfully
inferring and adapting to their users' objectives as they go. We argue that a
meaningful solution to value alignment must combine multi-agent decision theory
with rich mathematical models of human cognition, enabling robots to tap into
people's natural collaborative capabilities. We present a solution to the
cooperative inverse reinforcement learning (CIRL) dynamic game based on
well-established cognitive models of decision making and theory of mind. The
solution captures a key reciprocity relation: the human will not plan her
actions in isolation, but rather reason pedagogically about how the robot might
learn from them; the robot, in turn, can anticipate this and interpret the
human's actions pragmatically. To our knowledge, this work constitutes the
first formal analysis of value alignment grounded in empirically validated
cognitive models. | [
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] |
In this paper, we propose a new approach to cover song identification using a
CNN (convolutional neural network). Most previous studies extract the feature
vectors that characterize the cover song relation from a pair of songs and used
it to compute the (dis)similarity between the two songs. Based on the
observation that there is a meaningful pattern between cover songs and that
this can be learned, we have reformulated the cover song identification problem
in a machine learning framework. To do this, we first build the CNN using as an
input a cross-similarity matrix generated from a pair of songs. We then
construct the data set composed of cover song pairs and non-cover song pairs,
which are used as positive and negative training samples, respectively. The
trained CNN outputs the probability of being in the cover song relation given a
cross-similarity matrix generated from any two pieces of music and identifies
the cover song by ranking on the probability. Experimental results show that
the proposed algorithm achieves performance better than or comparable to the
state-of-the-art. | [
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Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation). | [
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] |
The automatic detection of software vulnerabilities is an important research
problem. However, existing solutions to this problem rely on human experts to
define features and often miss many vulnerabilities (i.e., incurring high false
negative rate). In this paper, we initiate the study of using deep
learning-based vulnerability detection to relieve human experts from the
tedious and subjective task of manually defining features. Since deep learning
is motivated to deal with problems that are very different from the problem of
vulnerability detection, we need some guiding principles for applying deep
learning to vulnerability detection. In particular, we need to find
representations of software programs that are suitable for deep learning. For
this purpose, we propose using code gadgets to represent programs and then
transform them into vectors, where a code gadget is a number of (not
necessarily consecutive) lines of code that are semantically related to each
other. This leads to the design and implementation of a deep learning-based
vulnerability detection system, called Vulnerability Deep Pecker
(VulDeePecker). In order to evaluate VulDeePecker, we present the first
vulnerability dataset for deep learning approaches. Experimental results show
that VulDeePecker can achieve much fewer false negatives (with reasonable false
positives) than other approaches. We further apply VulDeePecker to 3 software
products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which
are not reported in the National Vulnerability Database but were "silently"
patched by the vendors when releasing later versions of these products; in
contrast, these vulnerabilities are almost entirely missed by the other
vulnerability detection systems we experimented with. | [
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] |
Deep learning has recently seen rapid development and received significant
attention due to its state-of-the-art performance on previously-thought hard
problems. However, because of the internal complexity and nonlinear structure
of deep neural networks, the underlying decision making processes for why these
models are achieving such performance are challenging and sometimes mystifying
to interpret. As deep learning spreads across domains, it is of paramount
importance that we equip users of deep learning with tools for understanding
when a model works correctly, when it fails, and ultimately how to improve its
performance. Standardized toolkits for building neural networks have helped
democratize deep learning; visual analytics systems have now been developed to
support model explanation, interpretation, debugging, and improvement. We
present a survey of the role of visual analytics in deep learning research,
which highlights its short yet impactful history and thoroughly summarizes the
state-of-the-art using a human-centered interrogative framework, focusing on
the Five W's and How (Why, Who, What, How, When, and Where). We conclude by
highlighting research directions and open research problems. This survey helps
researchers and practitioners in both visual analytics and deep learning to
quickly learn key aspects of this young and rapidly growing body of research,
whose impact spans a diverse range of domains. | [
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Neural text generation models are often autoregressive language models or
seq2seq models. These models generate text by sampling words sequentially, with
each word conditioned on the previous word, and are state-of-the-art for
several machine translation and summarization benchmarks. These benchmarks are
often defined by validation perplexity even though this is not a direct measure
of the quality of the generated text. Additionally, these models are typically
trained via maxi- mum likelihood and teacher forcing. These methods are
well-suited to optimizing perplexity but can result in poor sample quality
since generating text requires conditioning on sequences of words that may have
never been observed at training time. We propose to improve sample quality
using Generative Adversarial Networks (GANs), which explicitly train the
generator to produce high quality samples and have shown a lot of success in
image generation. GANs were originally designed to output differentiable
values, so discrete language generation is challenging for them. We claim that
validation perplexity alone is not indicative of the quality of text generated
by a model. We introduce an actor-critic conditional GAN that fills in missing
text conditioned on the surrounding context. We show qualitatively and
quantitatively, evidence that this produces more realistic conditional and
unconditional text samples compared to a maximum likelihood trained model. | [
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] |
A long line of work in social psychology has studied variations in people's
susceptibility to persuasion -- the extent to which they are willing to modify
their opinions on a topic. This body of literature suggests an interesting
perspective on theoretical models of opinion formation by interacting parties
in a network: in addition to considering interventions that directly modify
people's intrinsic opinions, it is also natural to consider interventions that
modify people's susceptibility to persuasion. In this work, we adopt a popular
model for social opinion dynamics, and we formalize the opinion maximization
and minimization problems where interventions happen at the level of
susceptibility.
We show that modeling interventions at the level of susceptibility lead to an
interesting family of new questions in network opinion dynamics. We find that
the questions are quite different depending on whether there is an overall
budget constraining the number of agents we can target or not. We give a
polynomial-time algorithm for finding the optimal target-set to optimize the
sum of opinions when there are no budget constraints on the size of the
target-set. We show that this problem is NP-hard when there is a budget, and
that the objective function is neither submodular nor supermodular. Finally, we
propose a heuristic for the budgeted opinion optimization and show its efficacy
at finding target-sets that optimize the sum of opinions compared on real world
networks, including a Twitter network with real opinion estimates. | [
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] |
The curse of dimensionality is a phenomenon frequently observed in machine
learning (ML) and knowledge discovery (KD). There is a large body of literature
investigating its origin and impact, using methods from mathematics as well as
from computer science. Among the mathematical insights into data
dimensionality, there is an intimate link between the dimension curse and the
phenomenon of measure concentration, which makes the former accessible to
methods of geometric analysis. The present work provides a comprehensive study
of the intrinsic geometry of a data set, based on Gromov's metric measure
geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we
define a concept of geometric data set and introduce a metric as well as a
partial order on the set of isomorphism classes of such data sets. Based on
these objects, we propose and investigate an axiomatic approach to the
intrinsic dimension of geometric data sets and establish a concrete dimension
function with the desired properties. Our model for data sets and their
intrinsic dimension is computationally feasible and, moreover, adaptable to
specific ML/KD-algorithms, as illustrated by various experiments. | [
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] |
Deep learning models are being deployed in many mobile intelligent
applications. End-side services, such as intelligent personal assistants,
autonomous cars, and smart home services often employ either simple local
models on the mobile or complex remote models on the cloud. However, recent
studies have shown that partitioning the DNN computations between the mobile
and cloud can increase the latency and energy efficiencies. In this paper, we
propose an efficient, adaptive, and practical engine, JointDNN, for
collaborative computation between a mobile device and cloud for DNNs in both
inference and training phase. JointDNN not only provides an energy and
performance efficient method of querying DNNs for the mobile side but also
benefits the cloud server by reducing the amount of its workload and
communications compared to the cloud-only approach. Given the DNN architecture,
we investigate the efficiency of processing some layers on the mobile device
and some layers on the cloud server. We provide optimization formulations at
layer granularity for forward- and backward-propagations in DNNs, which can
adapt to mobile battery limitations and cloud server load constraints and
quality of service. JointDNN achieves up to 18 and 32 times reductions on the
latency and mobile energy consumption of querying DNNs compared to the
status-quo approaches, respectively. | [
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] |
The design of gaits for robot locomotion can be a daunting process which
requires significant expert knowledge and engineering. This process is even
more challenging for robots that do not have an accurate physical model, such
as compliant or micro-scale robots. Data-driven gait optimization provides an
automated alternative to analytical gait design. In this paper, we propose a
novel approach to efficiently learn a wide range of locomotion tasks with
walking robots. This approach formalizes locomotion as a contextual policy
search task to collect data, and subsequently uses that data to learn
multi-objective locomotion primitives that can be used for planning. As a
proof-of-concept we consider a simulated hexapod modeled after a recently
developed microrobot, and we thoroughly evaluate the performance of this
microrobot on different tasks and gaits. Our results validate the proposed
controller and learning scheme on single and multi-objective locomotion tasks.
Moreover, the experimental simulations show that without any prior knowledge
about the robot used (e.g., dynamics model), our approach is capable of
learning locomotion primitives within 250 trials and subsequently using them to
successfully navigate through a maze. | [
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] |
We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent
architecture that brings insights from predictive state representations to
reinforcement learning in partially observable environments. Predictive state
policy networks consist of a recursive filter, which keeps track of a belief
about the state of the environment, and a reactive policy that directly maps
beliefs to actions, to maximize the cumulative reward. The recursive filter
leverages predictive state representations (PSRs) (Rosencrantz and Gordon,
2004; Sun et al., 2016) by modeling predictive state-- a prediction of the
distribution of future observations conditioned on history and future actions.
This representation gives rise to a rich class of statistically consistent
algorithms (Hefny et al., 2018) to initialize the recursive filter. Predictive
state serves as an equivalent representation of a belief state. Therefore, the
policy component of the RPSP-network can be purely reactive, simplifying
training while still allowing optimal behaviour. Moreover, we use the PSR
interpretation during training as well, by incorporating prediction error in
the loss function. The entire network (recursive filter and reactive policy) is
still differentiable and can be trained using gradient based methods. We
optimize our policy using a combination of policy gradient based on rewards
(Williams, 1992) and gradient descent based on prediction error. We show the
efficacy of RPSP-networks under partial observability on a set of robotic
control tasks from OpenAI Gym. We empirically show that RPSP-networks perform
well compared with memory-preserving networks such as GRUs, as well as finite
memory models, being the overall best performing method. | [
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] |
Recently popularized graph neural networks achieve the state-of-the-art
accuracy on a number of standard benchmark datasets for graph-based
semi-supervised learning, improving significantly over existing approaches.
These architectures alternate between a propagation layer that aggregates the
hidden states of the local neighborhood and a fully-connected layer. Perhaps
surprisingly, we show that a linear model, that removes all the intermediate
fully-connected layers, is still able to achieve a performance comparable to
the state-of-the-art models. This significantly reduces the number of
parameters, which is critical for semi-supervised learning where number of
labeled examples are small. This in turn allows a room for designing more
innovative propagation layers. Based on this insight, we propose a novel graph
neural network that removes all the intermediate fully-connected layers, and
replaces the propagation layers with attention mechanisms that respect the
structure of the graph. The attention mechanism allows us to learn a dynamic
and adaptive local summary of the neighborhood to achieve more accurate
predictions. In a number of experiments on benchmark citation networks
datasets, we demonstrate that our approach outperforms competing methods. By
examining the attention weights among neighbors, we show that our model
provides some interesting insights on how neighbors influence each other. | [
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] |
The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines. | [
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] |
This article presents an extensive literature review of technology based
intervention methodologies for individuals facing Autism Spectrum Disorder
(ASD). Reviewed methodologies include: contemporary Computer Aided Systems
(CAS), Computer Vision Assisted Technologies (CVAT) and Virtual Reality (VR) or
Artificial Intelligence (AI)-Assisted interventions. The research over the past
decade has provided enough demonstrations that individuals with ASD have a
strong interest in technology based interventions, which are useful in both,
clinical settings as well as at home and classrooms. Despite showing great
promise, research in developing an advanced technology based intervention that
is clinically quantitative for ASD is minimal. Moreover, the clinicians are
generally not convinced about the potential of the technology based
interventions due to non-empirical nature of published results. A major reason
behind this lack of acceptability is that a vast majority of studies on
distinct intervention methodologies do not follow any specific standard or
research design. We conclude from our findings that there remains a gap between
the research community of computer science, psychology and neuroscience to
develop an AI assisted intervention technology for individuals suffering from
ASD. Following the development of a standardized AI based intervention
technology, a database needs to be developed, to devise effective AI
algorithms. | [
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] |
Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
dissolved on the tongue within 3min or less especially for geriatric and
pediatric patients. Current ODT formulation studies usually rely on the
personal experience of pharmaceutical experts and trial-and-error in the
laboratory, which is inefficient and time-consuming. The aim of current
research was to establish the prediction model of ODT formulations with direct
compression process by Artificial Neural Network (ANN) and Deep Neural Network
(DNN) techniques. 145 formulation data were extracted from Web of Science. All
data sets were divided into three parts: training set (105 data), validation
set (20) and testing set (20). ANN and DNN were compared for the prediction of
the disintegrating time. The accuracy of the ANN model has reached 85.60%,
80.00% and 75.00% on the training set, validation set and testing set
respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%,
respectively. Compared with the ANN, DNN showed the better prediction for ODT
formulations. It is the first time that deep neural network with the improved
dataset selection algorithm is applied to formulation prediction on small data.
The proposed predictive approach could evaluate the critical parameters about
quality control of formulation, and guide research and process development. The
implementation of this prediction model could effectively reduce drug product
development timeline and material usage, and proactively facilitate the
development of a robust drug product. | [
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] |
Sponsored search is an indispensable business model and a major revenue
contributor of almost all the search engines. From the advertisers' side,
participating in ranking the search results by paying for the sponsored search
advertisement to attract more awareness and purchase facilitates their
commercial goal. From the users' side, presenting personalized advertisement
reflecting their propensity would make their online search experience more
satisfactory. Sponsored search platforms rank the advertisements by a ranking
function to determine the list of advertisements to show and the charging price
for the advertisers. Hence, it is crucial to find a good ranking function which
can simultaneously satisfy the platform, the users and the advertisers.
Moreover, advertisements showing positions under different queries from
different users may associate with advertisement candidates of different bid
price distributions and click probability distributions, which requires the
ranking functions to be optimized adaptively to the traffic characteristics. In
this work, we proposed a generic framework to optimize the ranking functions by
deep reinforcement learning methods. The framework is composed of two parts: an
offline learning part which initializes the ranking functions by learning from
a simulated advertising environment, allowing adequate exploration of the
ranking function parameter space without hurting the performance of the
commercial platform. An online learning part which further optimizes the
ranking functions by adapting to the online data distribution. Experimental
results on a large-scale sponsored search platform confirm the effectiveness of
the proposed method. | [
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] |
Issues regarding explainable AI involve four components: users, laws &
regulations, explanations and algorithms. Together these components provide a
context in which explanation methods can be evaluated regarding their adequacy.
The goal of this chapter is to bridge the gap between expert users and lay
users. Different kinds of users are identified and their concerns revealed,
relevant statements from the General Data Protection Regulation are analyzed in
the context of Deep Neural Networks (DNNs), a taxonomy for the classification
of existing explanation methods is introduced, and finally, the various classes
of explanation methods are analyzed to verify if user concerns are justified.
Overall, it is clear that (visual) explanations can be given about various
aspects of the influence of the input on the output. However, it is noted that
explanation methods or interfaces for lay users are missing and we speculate
which criteria these methods / interfaces should satisfy. Finally it is noted
that two important concerns are difficult to address with explanation methods:
the concern about bias in datasets that leads to biased DNNs, as well as the
suspicion about unfair outcomes. | [
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] |
In company with the data explosion over the past decade, deep neural network
(DNN) based software has experienced unprecedented leap and is becoming the key
driving force of many novel industrial applications, including many
safety-critical scenarios such as autonomous driving. Despite great success
achieved in various human intelligence tasks, similar to traditional software,
DNNs could also exhibit incorrect behaviors caused by hidden defects causing
severe accidents and losses. In this paper, we propose DeepHunter, an automated
fuzz testing framework for hunting potential defects of general-purpose DNNs.
DeepHunter performs metamorphic mutation to generate new semantically preserved
tests, and leverages multiple plugable coverage criteria as feedback to guide
the test generation from different perspectives. To be scalable towards
practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and
prioritizes the tests selection based on active feedback. The effectiveness of
DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10,
ImageNet) and 7 DNNs with diverse complexities, under a large set of 6 coverage
criteria as feedback. The large-scale experiments demonstrate that DeepHunter
can (1) significantly boost the coverage with guidance; (2) generate useful
tests to detect erroneous behaviors and facilitate the DNN model quality
evaluation; (3) accurately capture potential defects during DNN quantization
for platform migration. | [
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] |
Recent success in deep learning has generated immense interest among
practitioners and students, inspiring many to learn about this new technology.
While visual and interactive approaches have been successfully developed to
help people more easily learn deep learning, most existing tools focus on
simpler models. In this work, we present GAN Lab, the first interactive
visualization tool designed for non-experts to learn and experiment with
Generative Adversarial Networks (GANs), a popular class of complex deep
learning models. With GAN Lab, users can interactively train generative models
and visualize the dynamic training process's intermediate results. GAN Lab
tightly integrates an model overview graph that summarizes GAN's structure, and
a layered distributions view that helps users interpret the interplay between
submodels. GAN Lab introduces new interactive experimentation features for
learning complex deep learning models, such as step-by-step training at
multiple levels of abstraction for understanding intricate training dynamics.
Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web
browsers, without the need for installation or specialized hardware, overcoming
a major practical challenge in deploying interactive tools for deep learning. | [
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] |
Accurate and robust recognition and prediction of traffic situation plays an
important role in autonomous driving, which is a prerequisite for risk
assessment and effective decision making. Although there exist a lot of works
dealing with modeling driver behavior of a single object, it remains a
challenge to make predictions for multiple highly interactive agents that react
to each other simultaneously. In this work, we propose a generic probabilistic
hierarchical recognition and prediction framework which employs a two-layer
Hidden Markov Model (TLHMM) to obtain the distribution of potential situations
and a learning-based dynamic scene evolution model to sample a group of future
trajectories. Instead of predicting motions of a single entity, we propose to
get the joint distribution by modeling multiple interactive agents as a whole
system. Moreover, due to the decoupling property of the layered structure, our
model is suitable for knowledge transfer from simulation to real world
applications as well as among different traffic scenarios, which can reduce the
computational efforts of training and the demand for a large data amount. A
case study of highway ramp merging scenario is demonstrated to verify the
effectiveness and accuracy of the proposed framework. | [
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] |
Regret minimization is a powerful tool for solving large-scale extensive-form
games. State-of-the-art methods rely on minimizing regret locally at each
decision point. In this work we derive a new framework for regret minimization
on sequential decision problems and extensive-form games with general compact
convex sets at each decision point and general convex losses, as opposed to
prior work which has been for simplex decision points and linear losses. We
call our framework laminar regret decomposition. It generalizes the CFR
algorithm to this more general setting. Furthermore, our framework enables a
new proof of CFR even in the known setting, which is derived from a perspective
of decomposing polytope regret, thereby leading to an arguably simpler
interpretation of the algorithm. Our generalization to convex compact sets and
convex losses allows us to develop new algorithms for several problems:
regularized sequential decision making, regularized Nash equilibria in
extensive-form games, and computing approximate extensive-form perfect
equilibria. Our generalization also leads to the first regret-minimization
algorithm for computing reduced-normal-form quantal response equilibria based
on minimizing local regrets. Experiments show that our framework leads to
algorithms that scale at a rate comparable to the fastest variants of
counterfactual regret minimization for computing Nash equilibrium, and
therefore our approach leads to the first algorithm for computing quantal
response equilibria in extremely large games. Finally we show that our
framework enables a new kind of scalable opponent exploitation approach. | [
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] |
In online display advertising, guaranteed contracts and real-time bidding
(RTB) are two major ways to sell impressions for a publisher. Despite the
increasing popularity of RTB, there is still half of online display advertising
revenue generated from guaranteed contracts. Therefore, simultaneously selling
impressions through both guaranteed contracts and RTB is a straightforward
choice for a publisher to maximize its yield. However, deriving the optimal
strategy to allocate impressions is not a trivial task, especially when the
environment is unstable in real-world applications. In this paper, we formulate
the impression allocation problem as an auction problem where each contract can
submit virtual bids for individual impressions. With this formulation, we
derive the optimal impression allocation strategy by solving the optimal
bidding functions for contracts. Since the bids from contracts are decided by
the publisher, we propose a multi-agent reinforcement learning (MARL) approach
to derive cooperative policies for the publisher to maximize its yield in an
unstable environment. The proposed approach also resolves the common challenges
in MARL such as input dimension explosion, reward credit assignment, and
non-stationary environment. Experimental evaluations on large-scale real
datasets demonstrate the effectiveness of our approach. | [
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Moving a human body or a large and bulky object can require the strength of
whole arm manipulation (WAM). This type of manipulation places the load on the
robot's arms and relies on global properties of the interaction to
succeed---rather than local contacts such as grasping or non-prehensile
pushing. In this paper, we learn to generate motions that enable WAM for
holding and transporting of humans in certain rescue or patient care scenarios.
We model the task as a reinforcement learning problem in order to provide a
behavior that can directly respond to external perturbation and human motion.
For this, we represent global properties of the robot-human interaction with
topology-based coordinates that are computed from arm and torso positions.
These coordinates also allow transferring the learned policy to other body
shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue
scenario and show in quantitative experiments that the policy can solve unseen
scenarios with differently-shaped humans, floating humans, or with perception
noise. Our qualitative experiments show the subsequent transporting after
holding is achieved and we demonstrate that the policy can be directly
transferred to a real world setting. | [
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] |
AI systems that learn through reward feedback about the actions they take are
increasingly deployed in domains that have significant impact on our daily
life. However, in many cases the online rewards should not be the only guiding
criteria, as there are additional constraints and/or priorities imposed by
regulations, values, preferences, or ethical principles. We detail a novel
online agent that learns a set of behavioral constraints by observation and
uses these learned constraints as a guide when making decisions in an online
setting while still being reactive to reward feedback. To define this agent, we
propose to adopt a novel extension to the classical contextual multi-armed
bandit setting and we provide a new algorithm called Behavior Constrained
Thompson Sampling (BCTS) that allows for online learning while obeying
exogenous constraints. Our agent learns a constrained policy that implements
the observed behavioral constraints demonstrated by a teacher agent, and then
uses this constrained policy to guide the reward-based online exploration and
exploitation. We characterize the upper bound on the expected regret of the
contextual bandit algorithm that underlies our agent and provide a case study
with real world data in two application domains. Our experiments show that the
designed agent is able to act within the set of behavior constraints without
significantly degrading its overall reward performance. | [
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] |
Algorithms for clustering points in metric spaces is a long-studied area of
research. Clustering has seen a multitude of work both theoretically, in
understanding the approximation guarantees possible for many objective
functions such as k-median and k-means clustering, and experimentally, in
finding the fastest algorithms and seeding procedures for Lloyd's algorithm.
The performance of a given clustering algorithm depends on the specific
application at hand, and this may not be known up front. For example, a
"typical instance" may vary depending on the application, and different
clustering heuristics perform differently depending on the instance.
In this paper, we define an infinite family of algorithms generalizing
Lloyd's algorithm, with one parameter controlling the initialization procedure,
and another parameter controlling the local search procedure. This family of
algorithms includes the celebrated k-means++ algorithm, as well as the classic
farthest-first traversal algorithm. We design efficient learning algorithms
which receive samples from an application-specific distribution over clustering
instances and learn a near-optimal clustering algorithm from the class. We show
the best parameters vary significantly across datasets such as MNIST, CIFAR,
and mixtures of Gaussians. Our learned algorithms never perform worse than
k-means++, and on some datasets we see significant improvements. | [
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] |
We present a developmental framework based on a long-term memory and
reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This
architecture allows a robot to optimize autonomously hyper-parameters that need
to be tuned from any action and/or vision module, treated as a black-box. The
learning can take advantage of past experiences (stored in the episodic and
procedural memories) in order to warm-start the exploration using a set of
hyper-parameters previously optimized from objects similar to the new unknown
one (stored in a semantic memory). As example, the system has been used to
optimized 9 continuous hyper-parameters of a professional software (Kamido)
both in simulation and with a real robot (industrial robotic arm Fanuc) with a
total of 13 different objects. The robot is able to find a good object-specific
optimization in 68 (simulation) or 40 (real) trials. In simulation, we
demonstrate the benefit of the transfer learning based on visual similarity, as
opposed to an amnesic learning (i.e. learning from scratch all the time).
Moreover, with the real robot, we show that the method consistently outperforms
the manual optimization from an expert with less than 2 hours of training time
to achieve more than 88% of success. | [
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] |
Organized crime inflicts human suffering on a genocidal scale: the Mexican
drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
per year are "exported" in a human trafficking industry enslaving an estimated
40 million people. These nefarious industries rely on sophisticated money
laundering schemes to operate. Despite tremendous resources dedicated to
anti-money laundering (AML) only a tiny fraction of illicit activity is
prevented. The research community can help. In this brief paper, we map the
structural and behavioral dynamics driving the technical challenge. We review
AML methods, current and emergent. We provide a first look at scalable graph
convolutional neural networks for forensic analysis of financial data, which is
massive, dense, and dynamic. We report preliminary experimental results using a
large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
created called AMLSim. We consider opportunities for high performance
efficiency, in terms of computation and memory, and we share results from a
simple graph compression experiment. Our results support our working hypothesis
that graph deep learning for AML bears great promise in the fight against
criminal financial activity. | [
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] |
Technology is shaping our lives in a multitude of ways. This is fuelled by a
technology infrastructure, both legacy and state of the art, composed of a
heterogeneous group of hardware, software, services and organisations. Such
infrastructure faces a diverse range of challenges to its operations that
include security, privacy, resilience, and quality of services. Among these,
cybersecurity and privacy are taking the centre-stage, especially since the
General Data Protection Regulation (GDPR) came into effect. Traditional
security and privacy techniques are overstretched and adversarial actors have
evolved to design exploitation techniques that circumvent protection. With the
ever-increasing complexity of technology infrastructure, security and
privacy-preservation specialists have started to look for adaptable and
flexible protection methods that can evolve (potentially autonomously) as the
adversarial actor changes its techniques. For this, Artificial Intelligence
(AI), Machine Learning (ML) and Deep Learning (DL) were put forward as
saviours. In this paper, we look at the promises of AI, ML, and DL stated in
academic and industrial literature and evaluate how realistic they are. We also
put forward potential challenges a DL based security and privacy protection
technique has to overcome. Finally, we conclude the paper with a discussion on
what steps the DL and the security and privacy-preservation community have to
take to ensure that DL is not just going to be hype, but an opportunity to
build a secure, reliable, and trusted technology infrastructure on which we can
rely on for so much in our lives. | [
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] |
The more AI agents are deployed in scenarios with possibly unexpected
situations, the more they need to be flexible, adaptive, and creative in
achieving the goal we have given them. Thus, a certain level of freedom to
choose the best path to the goal is inherent in making AI robust and flexible
enough. At the same time, however, the pervasive deployment of AI in our life,
whether AI is autonomous or collaborating with humans, raises several ethical
challenges. AI agents should be aware and follow appropriate ethical principles
and should thus exhibit properties such as fairness or other virtues. These
ethical principles should define the boundaries of AI's freedom and creativity.
However, it is still a challenge to understand how to specify and reason with
ethical boundaries in AI agents and how to combine them appropriately with
subjective preferences and goal specifications. Some initial attempts employ
either a data-driven example-based approach for both, or a symbolic rule-based
approach for both. We envision a modular approach where any AI technique can be
used for any of these essential ingredients in decision making or decision
support systems, paired with a contextual approach to define their combination
and relative weight. In a world where neither humans nor AI systems work in
isolation, but are tightly interconnected, e.g., the Internet of Things, we
also envision a compositional approach to building ethically bounded AI, where
the ethical properties of each component can be fruitfully exploited to derive
those of the overall system. In this paper we define and motivate the notion of
ethically-bounded AI, we describe two concrete examples, and we outline some
outstanding challenges. | [
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] |
Deep learning (DL) defines a data-driven programming paradigm that
automatically composes the system decision logic from the training data. In
company with the data explosion and hardware acceleration during the past
decade, DL achieves tremendous success in many cutting-edge applications.
However, even the state-of-the-art DL systems still suffer from quality and
reliability issues. It was only until recently that some preliminary progress
was made in testing feed-forward DL systems. In contrast to feed-forward DL
systems, recurrent neural networks (RNN) follow a very different architectural
design, implementing temporal behaviors and memory with loops and internal
states. Such stateful nature of RNN contributes to its success in handling
sequential inputs such as audio, natural languages and video processing, but
also poses new challenges for quality assurance.
In this paper, we initiate the very first step towards testing RNN-based
stateful DL systems. We model RNN as an abstract state transition system, based
on which we define a set of test coverage criteria specialized for stateful DL
systems. Moreover, we propose an automated testing framework, DeepCruiser,
which systematically generates tests in large scale to uncover defects of
stateful DL systems with coverage guidance. Our in-depth evaluation on a
state-of-the-art speech-to-text DL system demonstrates the effectiveness of our
technique in improving quality and reliability of stateful DL systems. | [
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] |
We propose a variant of iterated belief revision designed for settings with
limited computational resources, such as mobile autonomous robots. The proposed
memory architecture---called the {\em universal memory architecture}
(UMA)---maintains an epistemic state in the form of a system of default rules
similar to those studied by Pearl and by Goldszmidt and Pearl (systems $Z$ and
$Z^+$). A duality between the category of UMA representations and the category
of the corresponding model spaces, extending the Sageev-Roller duality between
discrete poc sets and discrete median algebras provides a two-way dictionary
from inference to geometry, leading to immense savings in computation, at a
cost in the quality of representation that can be quantified in terms of
topological invariants. Moreover, the same framework naturally enables
comparisons between different model spaces, making it possible to analyze the
deficiencies of one model space in comparison to others. This paper develops
the formalism underlying UMA, analyzes the complexity of maintenance and
inference operations in UMA, and presents some learning guarantees for
different UMA-based learners. Finally, we present simulation results to
illustrate the viability of the approach, and close with a discussion of the
strengths, weaknesses, and potential development of UMA-based learners. | [
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] |
Quantum error correction is widely thought to be the key to fault-tolerant
quantum computation. However, determining the most suited encoding for unknown
error channels or specific laboratory setups is highly challenging. Here, we
present a reinforcement learning framework for optimizing and fault-tolerantly
adapting quantum error correction codes. We consider a reinforcement learning
agent tasked with modifying a family of surface code quantum memories until a
desired logical error rate is reached. Using efficient simulations with about
70 data qubits with arbitrary connectivity, we demonstrate that such a
reinforcement learning agent can determine near-optimal solutions, in terms of
the number of data qubits, for various error models of interest. Moreover, we
show that agents trained on one setting are able to successfully transfer their
experience to different settings. This ability for transfer learning showcases
the inherent strengths of reinforcement learning and the applicability of our
approach for optimization from off-line simulations to on-line laboratory
settings. | [
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] |
Small base stations (SBs) of fifth-generation (5G) cellular networks are
envisioned to have storage devices to locally serve requests for reusable and
popular contents by \emph{caching} them at the edge of the network, close to
the end users. The ultimate goal is to shift part of the predictable load on
the back-haul links, from on-peak to off-peak periods, contributing to a better
overall network performance and service experience. To enable the SBs with
efficient \textit{fetch-cache} decision-making schemes operating in dynamic
settings, this paper introduces simple but flexible generic time-varying
fetching and caching costs, which are then used to formulate a constrained
minimization of the aggregate cost across files and time. Since caching
decisions per time slot influence the content availability in future slots, the
novel formulation for optimal fetch-cache decisions falls into the class of
dynamic programming. Under this generic formulation, first by considering
stationary distributions for the costs and file popularities, an efficient
reinforcement learning-based solver known as value iteration algorithm can be
used to solve the emerging optimization problem. Later, it is shown that
practical limitations on cache capacity can be handled using a particular
instance of the generic dynamic pricing formulation. Under this setting, to
provide a light-weight online solver for the corresponding optimization, the
well-known reinforcement learning algorithm, $Q$-learning, is employed to find
optimal fetch-cache decisions. Numerical tests corroborating the merits of the
proposed approach wrap up the paper. | [
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] |
We propose Differentiable Satisfiability and Differentiable Answer Set
Programming (Differentiable SAT/ASP) for multi-model optimization. Models
(answer sets or satisfying truth assignments) are sampled using a novel SAT/ASP
solving approach which uses a gradient descent-based branching mechanism.
Sampling proceeds until the value of a user-defined multi-model cost function
reaches a given threshold. As major use cases for our approach we propose
distribution-aware model sampling and expressive yet scalable probabilistic
logic programming. As our main algorithmic approach to Differentiable SAT/ASP,
we introduce an enhancement of the state-of-the-art CDNL/CDCL algorithm for
SAT/ASP solving. Additionally, we present alternative algorithms which use an
unmodified ASP solver (Clingo/clasp) and map the optimization task to
conventional answer set optimization or use so-called propagators. We also
report on the open source software DelSAT, a recent prototype implementation of
our main algorithm, and on initial experimental results which indicate that
DelSATs performance is, when applied to the use case of probabilistic logic
inference, on par with Markov Logic Network (MLN) inference performance,
despite having advantageous properties compared to MLNs, such as the ability to
express inductive definitions and to work with probabilities as weights
directly in all cases. Our experiments also indicate that our main algorithm is
strongly superior in terms of performance compared to the presented alternative
approaches which reduce a common instance of the general problem to regular
SAT/ASP. | [
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] |
Causal effect identification considers whether an interventional probability
distribution can be uniquely determined without parametric assumptions from
measured source distributions and structural knowledge on the generating
system. While complete graphical criteria and procedures exist for many
identification problems, there are still challenging but important extensions
that have not been considered in the literature. To tackle these new settings,
we present a search algorithm directly over the rules of do-calculus. Due to
generality of do-calculus, the search is capable of taking more advanced
data-generating mechanisms into account along with an arbitrary type of both
observational and experimental source distributions. The search is enhanced via
a heuristic and search space reduction techniques. The approach, called
do-search, is provably sound, and it is complete with respect to
identifiability problems that have been shown to be completely characterized by
do-calculus. When extended with additional rules, the search is capable of
handling missing data problems as well. With the versatile search, we are able
to approach new problems such as combined transportability and selection bias,
or multiple sources of selection bias. We perform a systematic analysis of
bivariate missing data problems and study causal inference under case-control
design. We also present the R package dosearch that provides an interface for a
C++ implementation of the search. | [
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] |
MapReduce and its variants have significantly simplified and accelerated the
process of developing parallel programs. However, most MapReduce
implementations focus on data-intensive tasks while many real-world tasks are
compute intensive and their data can fit distributedly into the memory. For
these tasks, the speed of MapReduce programs can be much slower than those
hand-optimized ones. We present Blaze, a C++ library that makes it easy to
develop high performance parallel programs for such compute intensive tasks. At
the core of Blaze is a highly-optimized in-memory MapReduce function, which has
three main improvements over conventional MapReduce implementations: eager
reduction, fast serialization, and special treatment for a small fixed key
range. We also offer additional conveniences that make developing parallel
programs similar to developing serial programs. These improvements make Blaze
an easy-to-use cluster computing library that approaches the speed of
hand-optimized parallel code. We apply Blaze to some common data mining tasks,
including word frequency count, PageRank, k-means, expectation maximization
(Gaussian mixture model), and k-nearest neighbors. Blaze outperforms Apache
Spark by more than 10 times on average for these tasks, and the speed of Blaze
scales almost linearly with the number of nodes. In addition, Blaze uses only
the MapReduce function and 3 utility functions in its implementation while
Spark uses almost 30 different parallel primitives in its official
implementation. | [
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] |
Understanding deep neural networks is a major research objective with notable
experimental and theoretical attention in recent years. The practical success
of excessively large networks underscores the need for better theoretical
analyses and justifications. In this paper we focus on layer-wise functional
structure and behavior in overparameterized deep models. To do so, we study
empirically the layers' robustness to post-training re-initialization and
re-randomization of the parameters. We provide experimental results which give
evidence for the heterogeneity of layers. Morally, layers of large deep neural
networks can be categorized as either "robust" or "critical". Resetting the
robust layers to their initial values does not result in adverse decline in
performance. In many cases, robust layers hardly change throughout training. In
contrast, re-initializing critical layers vastly degrades the performance of
the network with test error essentially dropping to random guesses. Our study
provides further evidence that mere parameter counting or norm calculations are
too coarse in studying generalization of deep models, and "flatness" and
robustness analysis of trained models need to be examined while taking into
account the respective network architectures. | [
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Evaluating the clinical similarities between pairwise patients is a
fundamental problem in healthcare informatics. A proper patient similarity
measure enables various downstream applications, such as cohort study and
treatment comparative effectiveness research. One major carrier for conducting
patient similarity research is Electronic Health Records(EHRs), which are
usually heterogeneous, longitudinal, and sparse. Though existing studies on
learning patient similarity from EHRs have shown being useful in solving real
clinical problems, their applicability is limited due to the lack of medical
interpretations. Moreover, most previous methods assume a vector-based
representation for patients, which typically requires aggregation of medical
events over a certain time period. As a consequence, temporal information will
be lost. In this paper, we propose a patient similarity evaluation framework
based on the temporal matching of longitudinal patient EHRs. Two efficient
methods are presented, unsupervised and supervised, both of which preserve the
temporal properties in EHRs. The supervised scheme takes a convolutional neural
network architecture and learns an optimal representation of patient clinical
records with medical concept embedding. The empirical results on real-world
clinical data demonstrate substantial improvement over the baselines. We make
our code and sample data available for further study. | [
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] |
We study the interplay between memorization and generalization of
overparameterized networks in the extreme case of a single training example and
an identity-mapping task. We examine fully-connected and convolutional networks
(FCN and CNN), both linear and nonlinear, initialized randomly and then trained
to minimize the reconstruction error. The trained networks stereotypically take
one of two forms: the constant function (memorization) and the identity
function (generalization). We formally characterize generalization in
single-layer FCNs and CNNs. We show empirically that different architectures
exhibit strikingly different inductive biases. For example, CNNs of up to 10
layers are able to generalize from a single example, whereas FCNs cannot learn
the identity function reliably from 60k examples. Deeper CNNs often fail, but
nonetheless do astonishing work to memorize the training output: because CNN
biases are location invariant, the model must progressively grow an output
pattern from the image boundaries via the coordination of many layers. Our work
helps to quantify and visualize the sensitivity of inductive biases to
architectural choices such as depth, kernel width, and number of channels. | [
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] |
Circuit obfuscation is a recently proposed defense mechanism to protect
digital integrated circuits (ICs) from reverse engineering by using camouflaged
gates i.e., logic gates whose functionality cannot be precisely determined by
the attacker. There have been effective schemes such as satisfiability-checking
(SAT)-based attacks that can potentially decrypt obfuscated circuits, called
deobfuscation. Deobfuscation runtime could have a large span ranging from few
milliseconds to thousands of years or more, depending on the number and layouts
of the ICs and camouflaged gates. And hence accurately pre-estimating the
deobfuscation runtime is highly crucial for the defenders to maximize it and
optimize their defense. However, estimating the deobfuscation runtime is a
challenging task due to 1) the complexity and heterogeneity of graph-structured
circuit, 2) the unknown and sophisticated mechanisms of the attackers for
deobfuscation. To address the above mentioned challenges, this work proposes
the first machine-learning framework that predicts the deobfuscation runtime
based on graph deep learning techniques. Specifically, we design a new model,
ICNet with new input and convolution layers to characterize and extract graph
frequencies from ICs, which are then integrated by heterogeneous deep
fully-connected layers to obtain final output. ICNet is an end-to-end framework
which can automatically extract the determinant features for deobfuscation
runtime. Extensive experiments demonstrate its effectiveness and efficiency. | [
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In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity. | [
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] |
Long-range indoor navigation requires guiding robots with noisy sensors and
controls through cluttered environments along paths that span a variety of
buildings. We achieve this with PRM-RL, a hierarchical robot navigation method
in which reinforcement learning agents that map noisy sensors to robot controls
learn to solve short-range obstacle avoidance tasks, and then sampling-based
planners map where these agents can reliably navigate in simulation; these
roadmaps and agents are then deployed on robots, guiding them along the
shortest path where the agents are likely to succeed. Here we use Probabilistic
Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement
learning method in the indoor navigation context. We evaluate the method in
simulation for kinematic differential drive and kinodynamic car-like robots in
several environments, and on differential-drive robots at three physical sites.
Our results show PRM-RL with AutoRL is more successful than several baselines,
is robust to noise, and can guide robots over hundreds of meters in the face of
noise and obstacles in both simulation and on robots, including over 5.8
kilometers of physical robot navigation. Video: https://youtu.be/xN-OWX5gKvQ | [
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] |
Insects and hummingbirds exhibit extraordinary flight capabilities and can
simultaneously master seemingly conflicting goals: stable hovering and
aggressive maneuvering, unmatched by small scale man-made vehicles. Flapping
Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this
performance gap. However, design and control of such systems remain challenging
due to various constraints. Here, we present an open source high fidelity
dynamic simulation for FWMAVs to serve as a testbed for the design,
optimization and flight control of FWMAVs. For simulation validation, we
recreated the hummingbird-scale robot developed in our lab in the simulation.
System identification was performed to obtain the model parameters. The force
generation, open-loop and closed-loop dynamic response between simulated and
experimental flights were compared and validated. The unsteady aerodynamics and
the highly nonlinear flight dynamics present challenging control problems for
conventional and learning control algorithms such as Reinforcement Learning.
The interface of the simulation is fully compatible with OpenAI Gym
environment. As a benchmark study, we present a linear controller for hovering
stabilization and a Deep Reinforcement Learning control policy for
goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real
transfer of both control policies onto the physical robot, further
demonstrating the fidelity of the simulation. | [
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] |
Packet classification is a fundamental problem in computer networking. This
problem exposes a hard tradeoff between the computation and state complexity,
which makes it particularly challenging. To navigate this tradeoff, existing
solutions rely on complex hand-tuned heuristics, which are brittle and hard to
optimize. In this paper, we propose a deep reinforcement learning (RL) approach
to solve the packet classification problem. There are several characteristics
that make this problem a good fit for Deep RL. First, many of the existing
solutions are iteratively building a decision tree by splitting nodes in the
tree. Second, the effects of these actions (e.g., splitting nodes) can only be
evaluated once we are done with building the tree. These two characteristics
are naturally captured by the ability of RL to take actions that have sparse
and delayed rewards. Third, it is computationally efficient to generate data
traces and evaluate decision trees, which alleviate the notoriously high sample
complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses
succinct representations to encode state and action space, and efficiently
explore candidate decision trees to optimize for a global objective. It
produces compact decision trees optimized for a specific set of rules and a
given performance metric, such as classification time, memory footprint, or a
combination of the two. Evaluation on ClassBench shows that NeuroCuts
outperforms existing hand-crafted algorithms in classification time by 18% at
the median, and reduces both time and memory footprint by up to 3x. | [
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] |
In order to enable high-quality decision making and motion planning of
intelligent systems such as robotics and autonomous vehicles, accurate
probabilistic predictions for surrounding interactive objects is a crucial
prerequisite. Although many research studies have been devoted to making
predictions on a single entity, it remains an open challenge to forecast future
behaviors for multiple interactive agents simultaneously. In this work, we take
advantage of the Generative Adversarial Network (GAN) due to its capability of
distribution learning and propose a generic multi-agent probabilistic
prediction and tracking framework which takes the interactions among multiple
entities into account, in which all the entities are treated as a whole.
However, since GAN is very hard to train, we make an empirical research and
present the relationship between training performance and hyperparameter values
with a numerical case study. The results imply that the proposed model can
capture both the mean, variance and multi-modalities of the groundtruth
distribution. Moreover, we apply the proposed approach to a real-world task of
vehicle behavior prediction to demonstrate its effectiveness and accuracy. The
results illustrate that the proposed model trained by adversarial learning can
achieve a better prediction performance than other state-of-the-art models
trained by traditional supervised learning which maximizes the data likelihood.
The well-trained model can also be utilized as an implicit proposal
distribution for particle filtered based Bayesian state estimation. | [
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Aerial filming is constantly gaining importance due to the recent advances in
drone technology. It invites many intriguing, unsolved problems at the
intersection of aesthetical and scientific challenges. In this work, we propose
a deep reinforcement learning agent which supervises motion planning of a
filming drone by making desirable shot mode selections based on aesthetical
values of video shots. Unlike most of the current state-of-the-art approaches
that require explicit guidance by a human expert, our drone learns how to make
favorable viewpoint selections by experience. We propose a learning scheme that
exploits aesthetical features of retrospective shots in order to extract a
desirable policy for better prospective shots. We train our agent in realistic
AirSim simulations using both a hand-crafted reward function as well as reward
from direct human input. We then deploy the same agent on a real DJI M210 drone
in order to test the generalization capability of our approach to real world
conditions. To evaluate the success of our approach in the end, we conduct a
comprehensive user study in which participants rate the shot quality of our
methods. Videos of the system in action can be seen at
https://youtu.be/qmVw6mfyEmw. | [
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] |
Transferring knowledge across tasks to improve data-efficiency is one of the
open key challenges in the field of global black-box optimization. Readily
available algorithms are typically designed to be universal optimizers and,
therefore, often suboptimal for specific tasks. We propose a novel transfer
learning method to obtain customized optimizers within the well-established
framework of Bayesian optimization, allowing our algorithm to utilize the
proven generalization capabilities of Gaussian processes. Using reinforcement
learning to meta-train an acquisition function (AF) on a set of related tasks,
the proposed method learns to extract implicit structural information and to
exploit it for improved data-efficiency. We present experiments on a
simulation-to-real transfer task as well as on several synthetic functions and
on two hyperparameter search problems. The results show that our algorithm (1)
automatically identifies structural properties of objective functions from
available source tasks or simulations, (2) performs favourably in settings with
both scarse and abundant source data, and (3) falls back to the performance
level of general AFs if no particular structure is present. | [
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] |
This work investigates the design of risk-perception-aware motion-planning
strategies that incorporate non-rational perception of risks associated with
uncertain spatial costs. Our proposed method employs the Cumulative Prospect
Theory (CPT) to generate a perceived risk map over a given environment.
CPT-like perceived risks and path-length metrics are then combined to define a
cost function that is compliant with the requirements of asymptotic optimality
of sampling-based motion planners (RRT*). The modeling power of CPT is
illustrated in theory and in simulation, along with a comparison to other risk
perception models like Conditional Value at Risk (CVaR). Theoretically, we
define a notion of expressiveness for a risk perception model and show that
CPT's is higher than that of CVaR and expected risk. We then show that this
expressiveness translates to our path planning setting, where we observe that a
planner equipped with CPT together with a simultaneous perturbation stochastic
approximation (SPSA) method can better approximate arbitrary paths in an
environment. Additionally, we show in simulation that our planner captures a
rich set of meaningful paths, representative of different risk perceptions in a
custom environment. We then compare the performance of our planner with T-RRT*
(a planner for continuous cost spaces) and Risk-RRT* (a risk-aware planner for
dynamic human obstacles) through simulations in cluttered and dynamic
environments respectively, showing the advantage of our proposed planner. | [
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] |
As data becomes the fuel driving technological and economic growth, a
fundamental challenge is how to quantify the value of data in algorithmic
predictions and decisions. For example, in healthcare and consumer markets, it
has been suggested that individuals should be compensated for the data that
they generate, but it is not clear what is an equitable valuation for
individual data. In this work, we develop a principled framework to address
data valuation in the context of supervised machine learning. Given a learning
algorithm trained on $n$ data points to produce a predictor, we propose data
Shapley as a metric to quantify the value of each training datum to the
predictor performance. Data Shapley value uniquely satisfies several natural
properties of equitable data valuation. We develop Monte Carlo and
gradient-based methods to efficiently estimate data Shapley values in practical
settings where complex learning algorithms, including neural networks, are
trained on large datasets. In addition to being equitable, extensive
experiments across biomedical, image and synthetic data demonstrate that data
Shapley has several other benefits: 1) it is more powerful than the popular
leave-one-out or leverage score in providing insight on what data is more
valuable for a given learning task; 2) low Shapley value data effectively
capture outliers and corruptions; 3) high Shapley value data inform what type
of new data to acquire to improve the predictor. | [
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] |
Machine learning techniques have enabled robots to learn narrow, yet complex
tasks and also perform broad, yet simple skills with a wide variety of objects.
However, learning a model that can both perform complex tasks and generalize to
previously unseen objects and goals remains a significant challenge. We study
this challenge in the context of "improvisational" tool use: a robot is
presented with novel objects and a user-specified goal (e.g., sweep some
clutter into the dustpan), and must figure out, using only raw image
observations, how to accomplish the goal using the available objects as tools.
We approach this problem by training a model with both a visual and physical
understanding of multi-object interactions, and develop a sampling-based
optimizer that can leverage these interactions to accomplish tasks. We do so by
combining diverse demonstration data with self-supervised interaction data,
aiming to leverage the interaction data to build generalizable models and the
demonstration data to guide the model-based RL planner to solve complex tasks.
Our experiments show that our approach can solve a variety of complex tool use
tasks from raw pixel inputs, outperforming both imitation learning and
self-supervised learning individually. Furthermore, we show that the robot can
perceive and use novel objects as tools, including objects that are not
conventional tools, while also choosing dynamically to use or not use tools
depending on whether or not they are required. | [
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] |
Quantum tomography is currently ubiquitous for testing any implementation of
a quantum information processing device. Various sophisticated procedures for
state and process reconstruction from measured data are well developed and
benefit from precise knowledge of the model describing state preparation and
the measurement apparatus. However, physical models suffer from intrinsic
limitations as actual measurement operators and trial states cannot be known
precisely. This scenario inevitably leads to state-preparation-and-measurement
(SPAM) errors degrading reconstruction performance. Here we develop and
experimentally implement a machine learning based protocol reducing SPAM
errors. We trained a supervised neural network to filter the experimental data
and hence uncovered salient patterns that characterize the measurement
probabilities for the original state and the ideal experimental apparatus free
from SPAM errors. We compared the neural network state reconstruction protocol
with a protocol treating SPAM errors by process tomography, as well as to a
SPAM-agnostic protocol with idealized measurements. The average reconstruction
fidelity is shown to be enhanced by 10\% and 27\%, respectively. The presented
methods apply to the vast range of quantum experiments which rely on
tomography. | [
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] |
In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge
computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as
flying MEC platform to provide computing resource to ground user equipments
(UEs). Compared to the traditional fixed location MEC, UAV enabled MEC (i.e.,
UAVE) is particular useful in case of temporary events, emergency situations
and on-demand services, due to its high flexibility, low cost and easy
deployment features. However, operation of UAVE faces several challenges, two
of which are how to achieve both 1) the association between multiple UEs and
UAVs and 2) the resource allocation from UAVs to UEs, while minimizing the
energy consumption for all the UEs. To address this, we formulate the above
problem into a mixed integer nonlinear programming (MINLP), which is difficult
to be solved in general, especially in the large-scale scenario. We then
propose a Reinforcement Learning (RL)-based user Association and resource
Allocation (RLAA) algorithm to tackle this problem efficiently and effectively.
Numerical results show that the proposed RLAA can achieve the optimal
performance with comparison to the exhaustive search in small scale, and have
considerable performance gain over other typical algorithms in large-scale
cases. | [
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] |
The widespread adoption of smartphones dramatically increases the risk of
attacks and the spread of mobile malware, especially on the Android platform.
Machine learning-based solutions have been already used as a tool to supersede
signature-based anti-malware systems. However, malware authors leverage
features from malicious and legitimate samples to estimate statistical
difference in-order to create adversarial examples. Hence, to evaluate the
vulnerability of machine learning algorithms in malware detection, we propose
five different attack scenarios to perturb malicious applications (apps). By
doing this, the classification algorithm inappropriately fits the discriminant
function on the set of data points, eventually yielding a higher
misclassification rate. Further, to distinguish the adversarial examples from
benign samples, we propose two defense mechanisms to counter attacks. To
validate our attacks and solutions, we test our model on three different
benchmark datasets. We also test our methods using various classifier
algorithms and compare them with the state-of-the-art data poisoning method
using the Jacobian matrix. Promising results show that generated adversarial
samples can evade detection with a very high probability. Additionally, evasive
variants generated by our attack models when used to harden the developed
anti-malware system improves the detection rate up to 50% when using the
Generative Adversarial Network (GAN) method. | [
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] |
Knowledge graphs (KGs), i.e. representation of information as a semantic
graph, provide a significant test bed for many tasks including question
answering, recommendation, and link prediction. Various amount of scholarly
metadata have been made vailable as knowledge graphs from the diversity of data
providers and agents. However, these high-quantities of data remain far from
quality criteria in terms of completeness while growing at a rapid pace. Most
of the attempts in completing such KGs are following traditional data
digitization, harvesting and collaborative curation approaches. Whereas,
advanced AI-related approaches such as embedding models - specifically designed
for such tasks - are usually evaluated for standard benchmarks such as Freebase
and Wordnet. The tailored nature of such datasets prevents those approaches to
shed the lights on more accurate discoveries. Application of such models on
domain-specific KGs takes advantage of enriched meta-data and provides accurate
results where the underlying domain can enormously benefit. In this work, the
TransE embedding model is reconciled for a specific link prediction task on
scholarly metadata. The results show a significant shift in the accuracy and
performance evaluation of the model on a dataset with scholarly metadata. The
newly proposed version of TransE obtains 99.9% for link prediction task while
original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms
other embedding models such as ComplEx, TransH and TransR experimented over
scholarly knowledge graphs | [
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] |
In various business settings, there is an interest in using more complex
machine learning techniques for sales forecasting. It is difficult to convince
analysts, along with their superiors, to adopt these techniques since the
models are considered to be "black boxes," even if they perform better than
current models in use. We examine the impact of contrastive explanations about
large errors on users' attitudes towards a "black-box'" model. We propose an
algorithm, Monte Carlo Bounds for Reasonable Predictions. Given a large error,
MC-BRP determines (1) feature values that would result in a reasonable
prediction, and (2) general trends between each feature and the target, both
based on Monte Carlo simulations. We evaluate on a real dataset with real users
by conducting a user study with 75 participants to determine if explanations
generated by MC-BRP help users understand why a prediction results in a large
error, and if this promotes trust in an automatically-learned model. Our study
shows that users are able to answer objective questions about the model's
predictions with overall 81.1% accuracy when provided with these contrastive
explanations. We show that users who saw MC-BRP explanations understand why the
model makes large errors in predictions significantly more than users in the
control group. We also conduct an in-depth analysis on the difference in
attitudes between Practitioners and Researchers, and confirm that our results
hold when conditioning on the users' background. | [
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] |
We present a novel method for training a social robot to generate
backchannels during human-robot interaction. We address the problem within an
off-policy reinforcement learning framework, and show how a robot may learn to
produce non-verbal backchannels like laughs, when trained to maximize the
engagement and attention of the user. A major contribution of this work is the
formulation of the problem as a Markov decision process (MDP) with states
defined by the speech activity of the user and rewards generated by quantified
engagement levels. The problem that we address falls into the class of
applications where unlimited interaction with the environment is not possible
(our environment being a human) because it may be time-consuming, costly,
impracticable or even dangerous in case a bad policy is executed. Therefore, we
introduce deep Q-network (DQN) in a batch reinforcement learning framework,
where an optimal policy is learned from a batch data collected using a more
controlled policy. We suggest the use of human-to-human dyadic interaction
datasets as a batch of trajectories to train an agent for engaging
interactions. Our experiments demonstrate the potential of our method to train
a robot for engaging behaviors in an offline manner. | [
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] |
In recent years, with the trend of applying deep learning (DL) in high
performance scientific computing, the unique characteristics of emerging DL
workloads in HPC raise great challenges in designing, implementing HPC AI
systems. The community needs a new yard stick for evaluating the future HPC
systems. In this paper, we propose HPC AI500 --- a benchmark suite for
evaluating HPC systems that running scientific DL workloads. Covering the most
representative scientific fields, each workload from HPC AI500 is based on
real-world scientific DL applications. Currently, we choose 14 scientific DL
benchmarks from perspectives of application scenarios, data sets, and software
stack. We propose a set of metrics for comprehensively evaluating the HPC AI
systems, considering both accuracy, performance as well as power and cost. We
provide a scalable reference implementation of HPC AI500. HPC AI500 is a part
of the open-source AIBench project, the specification and source code are
publicly available from \url{http://www.benchcouncil.org/AIBench/index.html}. | [
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] |
Deep reinforcement learning (RL) has been successfully applied to a variety
of game-like environments. However, the application of deep RL to visual
navigation with realistic environments is a challenging task. We propose a
novel learning architecture capable of navigating an agent, e.g. a mobile
robot, to a target given by an image. To achieve this, we have extended the
batched A2C algorithm with auxiliary tasks designed to improve visual
navigation performance. We propose three additional auxiliary tasks: predicting
the segmentation of the observation image and of the target image and
predicting the depth-map. These tasks enable the use of supervised learning to
pre-train a large part of the network and to reduce the number of training
steps substantially. The training performance has been further improved by
increasing the environment complexity gradually over time. An efficient neural
network structure is proposed, which is capable of learning for multiple
targets in multiple environments. Our method navigates in continuous state
spaces and on the AI2-THOR environment simulator outperforms state-of-the-art
goal-oriented visual navigation methods from the literature. | [
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A goal shared by artificial intelligence and information retrieval is to
create an oracle, that is, a machine that can answer our questions, no matter
how difficult they are. A more limited, but still instrumental, version of this
oracle is a question-answering system, in which an open-ended question is given
to the machine, and an answer is produced based on the knowledge it has access
to. Such systems already exist and are increasingly capable of answering
complicated questions. This progress can be partially attributed to the recent
success of machine learning and to the efficient methods for storing and
retrieving information, most notably through web search engines. One can
imagine that this general-purpose question-answering system can be built as a
billion-parameters neural network trained end-to-end with a large number of
pairs of questions and answers. We argue, however, that although this approach
has been very successful for tasks such as machine translation, storing the
world's knowledge as parameters of a learning machine can be very hard. A more
efficient way is to train an artificial agent on how to use an external
retrieval system to collect relevant information. This agent can leverage the
effort that has been put into designing and running efficient storage and
retrieval systems by learning how to best utilize them to accomplish a task.
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] |
Most work on musical score models (a.k.a. musical language models) for music
transcription has focused on describing the local sequential dependence of
notes in musical scores and failed to capture their global repetitive
structure, which can be a useful guide for transcribing music. Focusing on
rhythm, we formulate several classes of Bayesian Markov models of musical
scores that describe repetitions indirectly using the sparse transition
probabilities of notes or note patterns. This enables us to construct
piece-specific models for unseen scores with an unfixed repetitive structure
and to derive tractable inference algorithms. Moreover, to describe approximate
repetitions, we explicitly incorporate a process for modifying the repeated
notes/note patterns. We apply these models as prior musical score models for
rhythm transcription, where piece-specific score models are inferred from
performed MIDI data by Bayesian learning, in contrast to the conventional
supervised construction of score models. Evaluations using the vocal melodies
of popular music showed that the Bayesian models improved the transcription
accuracy for most of the tested model types, indicating the universal efficacy
of the proposed approach. Moreover, we found an effective data representation
for modelling rhythms that maximizes the transcription accuracy and
computational efficiency. | [
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] |
Hierarchical clustering has been shown to be valuable in many scenarios.
Despite its usefulness to many situations, there is no agreed methodology on
how to properly evaluate the hierarchies produced from different techniques,
particularly in the case where ground-truth labels are unavailable. This
motivates us to propose a framework for assessing the quality of hierarchical
clustering allocations which covers the case of no ground-truth information.
This measurement is useful, e.g., to assess the hierarchical structures used by
online retailer websites to display their product catalogues. Our framework is
one of the few attempts for the hierarchy evaluation from a decision-theoretic
perspective. We model the process as a bot searching stochastically for items
in the hierarchy and establish a measure representing the degree to which the
hierarchy supports this search. We employ Partially Observable Markov Decision
Processes (POMDP) to model the uncertainty, the decision making, and the
cognitive return for searchers in such a scenario. | [
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] |
A successful robot-assisted feeding system requires bite acquisition of a
wide variety of food items. It must adapt to changing user food preferences
under uncertain visual and physical environments. Different food items in
different environmental conditions require different manipulation strategies
for successful bite acquisition. Therefore, a key challenge is how to handle
previously unseen food items with very different success rate distributions
over strategy. Combining low-level controllers and planners into discrete
action trajectories, we show that the problem can be represented using a linear
contextual bandit setting. We construct a simulated environment using a doubly
robust loss estimate from previously seen food items, which we use to tune the
parameters of off-the-shelf contextual bandit algorithms. Finally, we
demonstrate empirically on a robot-assisted feeding system that, even starting
with a model trained on thousands of skewering attempts on dissimilar
previously seen food items, $\epsilon$-greedy and LinUCB algorithms can quickly
converge to the most successful manipulation strategy. | [
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] |
Last-mile delivery systems commonly propose the use of autonomous robotic
vehicles to increase scalability and efficiency. The economic inefficiency of
collecting accurate prior maps for navigation motivates the use of planning
algorithms that operate in unmapped environments. However, these algorithms
typically waste time exploring regions that are unlikely to contain the
delivery destination. Context is key information about structured environments
that could guide exploration toward the unknown goal location, but the abstract
idea is difficult to quantify for use in a planning algorithm. Some approaches
specifically consider contextual relationships between objects, but would
perform poorly in object-sparse environments like outdoors. Recent deep
learning-based approaches consider context too generally, making
training/transferability difficult. Therefore, this work proposes a novel
formulation of utilizing context for planning as an image-to-image translation
problem, which is shown to extract terrain context from semantic gridmaps, into
a metric that an exploration-based planner can use. The proposed framework has
the benefit of training on a static dataset instead of requiring a
time-consuming simulator. Across 42 test houses with layouts from satellite
images, the trained algorithm enables a robot to reach its goal 189\% faster
than with a context-unaware planner, and within 63\% of the optimal path
computed with a prior map. The proposed algorithm is also implemented on a
vehicle with a forward-facing camera in a high-fidelity, Unreal simulation of
neighborhood houses. | [
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] |
While feedback loops are known to play important roles in many complex
systems, their existence is ignored in a large part of the causal discovery
literature, as systems are typically assumed to be acyclic from the outset.
When applying causal discovery algorithms designed for the acyclic setting on
data generated by a system that involves feedback, one would not expect to
obtain correct results. In this work, we show that -- surprisingly -- the
output of the Fast Causal Inference (FCI) algorithm is correct if it is applied
to observational data generated by a system that involves feedback. More
specifically, we prove that for observational data generated by a simple and
$\sigma$-faithful Structural Causal Model (SCM), FCI is sound and complete, and
can be used to consistently estimate (i) the presence and absence of causal
relations, (ii) the presence and absence of direct causal relations, (iii) the
absence of confounders, and (iv) the absence of specific cycles in the causal
graph of the SCM. We extend these results to constraint-based causal discovery
algorithms that exploit certain forms of background knowledge, including the
causally sufficient setting (e.g., the PC algorithm) and the Joint Causal
Inference setting (e.g., the FCI-JCI algorithm). | [
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] |
A central concern in an interactive intelligent system is optimization of its
actions, to be maximally helpful to its human user. In recommender systems for
instance, the action is to choose what to recommend, and the optimization task
is to recommend items the user prefers. The optimization is done based on
earlier user's feedback (e.g. "likes" and "dislikes"), and the algorithms
assume the feedback to be faithful. That is, when the user clicks "like," they
actually prefer the item. We argue that this fundamental assumption can be
extensively violated by human users, who are not passive feedback sources.
Instead, they are in control, actively steering the system towards their goal.
To verify this hypothesis, that humans steer and are able to improve
performance by steering, we designed a function optimization task where a human
and an optimization algorithm collaborate to find the maximum of a
1-dimensional function. At each iteration, the optimization algorithm queries
the user for the value of a hidden function $f$ at a point $x$, and the user,
who sees the hidden function, provides an answer about $f(x)$. Our study on 21
participants shows that users who understand how the optimization works,
strategically provide biased answers (answers not equal to $f(x)$), which
results in the algorithm finding the optimum significantly faster. Our work
highlights that next-generation intelligent systems will need user models
capable of helping users who steer systems to pursue their goals. | [
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Explainable machine learning and artificial intelligence models have been
used to justify a model's decision-making process. This added transparency aims
to help improve user performance and understanding of the underlying model.
However, in practice, explainable systems face many open questions and
challenges. Specifically, designers might reduce the complexity of deep
learning models in order to provide interpretability. The explanations
generated by these simplified models, however, might not accurately justify and
be truthful to the model. This can further add confusion to the users as they
might not find the explanations meaningful with respect to the model
predictions. Understanding how these explanations affect user behavior is an
ongoing challenge. In this paper, we explore how explanation veracity affects
user performance and agreement in intelligent systems. Through a controlled
user study with an explainable activity recognition system, we compare
variations in explanation veracity for a video review and querying task. The
results suggest that low veracity explanations significantly decrease user
performance and agreement compared to both accurate explanations and a system
without explanations. These findings demonstrate the importance of accurate and
understandable explanations and caution that poor explanations can sometimes be
worse than no explanations with respect to their effect on user performance and
reliance on an AI system. | [
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] |
Designing reward functions is a challenging problem in AI and robotics.
Humans usually have a difficult time directly specifying all the desirable
behaviors that a robot needs to optimize. One common approach is to learn
reward functions from collected expert demonstrations. However, learning reward
functions from demonstrations introduces many challenges: some methods require
highly structured models, e.g. reward functions that are linear in some
predefined set of features, while others adopt less structured reward functions
that on the other hand require tremendous amount of data. In addition, humans
tend to have a difficult time providing demonstrations on robots with high
degrees of freedom, or even quantifying reward values for given demonstrations.
To address these challenges, we present a preference-based learning approach,
where as an alternative, the human feedback is only in the form of comparisons
between trajectories. Furthermore, we do not assume highly constrained
structures on the reward function. Instead, we model the reward function using
a Gaussian Process (GP) and propose a mathematical formulation to actively find
a GP using only human preferences. Our approach enables us to tackle both
inflexibility and data-inefficiency problems within a preference-based learning
framework. Our results in simulations and a user study suggest that our
approach can efficiently learn expressive reward functions for robotics tasks. | [
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] |
Artificial behavioral agents are often evaluated based on their consistent
behaviors and performance to take sequential actions in an environment to
maximize some notion of cumulative reward. However, human decision making in
real life usually involves different strategies and behavioral trajectories
that lead to the same empirical outcome. Motivated by clinical literature of a
wide range of neurological and psychiatric disorders, we propose here a more
general and flexible parametric framework for sequential decision making that
involves a two-stream reward processing mechanism. We demonstrated that this
framework is flexible and unified enough to incorporate a family of problems
spanning multi-armed bandits (MAB), contextual bandits (CB) and reinforcement
learning (RL), which decompose the sequential decision making process in
different levels. Inspired by the known reward processing abnormalities of many
mental disorders, our clinically-inspired agents demonstrated interesting
behavioral trajectories and comparable performance on simulated tasks with
particular reward distributions, a real-world dataset capturing human
decision-making in gambling tasks, and the PacMan game across different reward
stationarities in a lifelong learning setting. | [
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] |
In the age of big data, companies and governments are increasingly using
algorithms to inform hiring decisions, employee management, policing, credit
scoring, insurance pricing, and many more aspects of our lives. AI systems can
help us make evidence-driven, efficient decisions, but can also confront us
with unjustified, discriminatory decisions wrongly assumed to be accurate
because they are made automatically and quantitatively. It is becoming evident
that these technological developments are consequential to people's fundamental
human rights. Despite increasing attention to these urgent challenges in recent
years, technical solutions to these complex socio-ethical problems are often
developed without empirical study of societal context and the critical input of
societal stakeholders who are impacted by the technology. On the other hand,
calls for more ethically- and socially-aware AI often fail to provide answers
for how to proceed beyond stressing the importance of transparency,
explainability, and fairness. Bridging these socio-technical gaps and the deep
divide between abstract value language and design requirements is essential to
facilitate nuanced, context-dependent design choices that will support moral
and social values. In this paper, we bridge this divide through the framework
of Design for Values, drawing on methodologies of Value Sensitive Design and
Participatory Design to present a roadmap for proactively engaging societal
stakeholders to translate fundamental human rights into context-dependent
design requirements through a structured, inclusive, and transparent process. | [
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Fashion is an inherently visual concept and computer vision and artificial
intelligence (AI) are playing an increasingly important role in shaping the
future of this domain. Many research has been done on recommending fashion
products based on the learned user preferences. However, in addition to
recommending single items, AI can also help users create stylish outfits from
items they already have, or purchase additional items that go well with their
current wardrobe. Compatibility is the key factor in creating stylish outfits
from single items. Previous studies have mostly focused on modeling pair-wise
compatibility. There are a few approaches that consider an entire outfit, but
these approaches have limitations such as requiring rich semantic information,
category labels, and fixed order of items. Thus, they fail to effectively
determine compatibility when such information is not available. In this work,
we adopt a Relation Network (RN) to develop new compatibility learning models,
Fashion RN and FashionRN-VSE, that addresses the limitations of existing
approaches. FashionRN learns the compatibility of an entire outfit, with an
arbitrary number of items, in an arbitrary order. We evaluated our model using
a large dataset of 49,740 outfits that we collected from Polyvore website.
Quantitatively, our experimental results demonstrate state of the art
performance compared with alternative methods in the literature in both
compatibility prediction and fill-in-the-blank test. Qualitatively, we also
show that the item embedding learned by FashionRN indicate the compatibility
among fashion items. | [
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] |
Boolean functional synthesis is a fundamental problem in computer science
with wide-ranging applications and has witnessed a surge of interest resulting
in progressively improved techniques over the past decade. Despite intense
algorithmic development, a large number of problems remain beyond the reach of
the state of the art techniques. Motivated by the progress in machine learning,
we propose Manthan, a novel data-driven approach to Boolean functional
synthesis. Manthan views functional synthesis as a classification problem,
relying on advances in constrained sampling for data generation, and advances
in automated reasoning for a novel proof-guided refinement and provable
verification. On an extensive and rigorous evaluation over 609 benchmarks, we
demonstrate that Manthan significantly improves upon the current state of the
art, solving 356 benchmarks in comparison to 280, which is the most solved by a
state of the art technique; thereby, we demonstrate an increase of 76
benchmarks over the current state of the art. Furthermore, Manthan solves 60
benchmarks that none of the current state of the art techniques could solve.
The significant performance improvements, along with our detailed analysis,
highlights several interesting avenues of future work at the intersection of
machine learning, constrained sampling, and automated reasoning. | [
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] |
Adversarial attacks against conventional Deep Learning (DL) systems and
algorithms have been widely studied, and various defenses were proposed.
However, the possibility and feasibility of such attacks against Deep
Reinforcement Learning (DRL) are less explored. As DRL has achieved great
success in various complex tasks, designing effective adversarial attacks is an
indispensable prerequisite towards building robust DRL algorithms. In this
paper, we introduce two novel adversarial attack techniques to
\emph{stealthily} and \emph{efficiently} attack the DRL agents. These two
techniques enable an adversary to inject adversarial samples in a minimal set
of critical moments while causing the most severe damage to the agent. The
first technique is the \emph{critical point attack}: the adversary builds a
model to predict the future environmental states and agent's actions, assesses
the damage of each possible attack strategy, and selects the optimal one. The
second technique is the \emph{antagonist attack}: the adversary automatically
learns a domain-agnostic model to discover the critical moments of attacking
the agent in an episode. Experimental results demonstrate the effectiveness of
our techniques. Specifically, to successfully attack the DRL agent, our
critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout)
steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks),
which are significant improvements over state-of-the-art methods. | [
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Multiple governmental agencies and private organisations have made
commitments for the colonisation of Mars. Such colonisation requires complex
systems and infrastructure that could be very costly to repair or replace in
cases of cyber attacks. This paper surveys deep learning algorithms, IoT cyber
security and risk models, and established mathematical formulas to identify the
best approach for developing a dynamic and self adapting system for predictive
cyber risk analytics supported with Artificial Intelligence and Machine
Learning and real time intelligence in edge computing. The paper presents a new
mathematical approach for integrating concepts for cognition engine design,
edge computing and Artificial Intelligence and Machine Learning to automate
anomaly detection. This engine instigates a step change by applying Artificial
Intelligence and Machine Learning embedded at the edge of IoT networks, to
deliver safe and functional real time intelligence for predictive cyber risk
analytics. This will enhance capacities for risk analytics and assists in the
creation of a comprehensive and systematic understanding of the opportunities
and threats that arise when edge computing nodes are deployed, and when
Artificial Intelligence and Machine Learning technologies are migrated to the
periphery of the internet and into local IoT networks. | [
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] |
Cloud computing has become a powerful and indispensable technology for
complex, high performance and scalable computation. The exponential expansion
in the deployment of cloud technology has produced a massive amount of data
from a variety of applications, resources and platforms. In turn, the rapid
rate and volume of data creation has begun to pose significant challenges for
data management and security. The design and deployment of intrusion detection
systems (IDS) in the big data setting has, therefore, become a topic of
importance. In this paper, we conduct a systematic literature review (SLR) of
data mining techniques (DMT) used in IDS-based solutions through the period
2013-2018. We employed criterion-based, purposive sampling identifying 32
articles, which constitute the primary source of the present survey. After a
careful investigation of these articles, we identified 17 separate DMTs
deployed in an IDS context. This paper also presents the merits and
disadvantages of the various works of current research that implemented DMTs
and distributed streaming frameworks (DSF) to detect and/or prevent malicious
attacks in a big data environment. | [
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] |
Penetration testing is a security exercise aimed at assessing the security of
a system by simulating attacks against it. So far, penetration testing has been
carried out mainly by trained human attackers and its success critically
depended on the available expertise. Automating this practice constitutes a
non-trivial problem, as the range of actions that a human expert may attempts
against a system and the range of knowledge she relies on to take her decisions
are hard to capture. In this paper, we focus our attention on simplified
penetration testing problems expressed in the form of capture the flag hacking
challenges, and we analyze how model-free reinforcement learning algorithms may
help to solve them. In modeling these capture the flag competitions as
reinforcement learning problems we highlight that a specific challenge that
characterize penetration testing is the problem of discovering the structure of
the problem at hand. We then show how this challenge may be eased by relying on
different forms of prior knowledge that may be provided to the agent. In this
way we demonstrate how the feasibility of tackling penetration testing using
reinforcement learning may rest on a careful trade-off between model-free and
model-based algorithms. By using techniques to inject a priori knowledge, we
show it is possible to better direct the agent and restrict the space of its
exploration problem, thus achieving solutions more efficiently. | [
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] |
Deep Reinforcement Learning has been successfully applied in various computer
games [8]. However, it is still rarely used in real-world applications,
especially for the navigation and continuous control of real mobile robots
[13]. Previous approaches lack safety and robustness and/or need a structured
environment. In this paper we present our proof of concept for autonomous
self-learning robot navigation in an unknown environment for a real robot
without a map or planner. The input for the robot is only the fused data from a
2D laser scanner and a RGB-D camera as well as the orientation to the goal. The
map of the environment is unknown. The output actions of an Asynchronous
Advantage Actor-Critic network (GA3C) are the linear and angular velocities for
the robot. The navigator/controller network is pretrained in a high-speed,
parallel, and self-implemented simulation environment to speed up the learning
process and then deployed to the real robot. To avoid overfitting, we train
relatively small networks, and we add random Gaussian noise to the input laser
data. The sensor data fusion with the RGB-D camera allows the robot to navigate
in real environments with real 3D obstacle avoidance and without the need to
fit the environment to the sensory capabilities of the robot. To further
increase the robustness, we train on environments of varying difficulties and
run 32 training instances simultaneously. Video: supplementary File / YouTube,
Code: GitHub | [
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