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9,901 | Modeling State-Conditional Observation Distribution using Weighted
Stereo Samples for Factorial Speech Processing Models | cs.LG | This paper investigates the effectiveness of factorial speech processing
models in noise-robust automatic speech recognition tasks. For this purpose,
the paper proposes an idealistic approach for modeling state-conditional
observation distribution of factorial models based on weighted stereo samples.
This approach is a... | computer science |
9,902 | ProtVec: A Continuous Distributed Representation of Biological Sequences | cs.AI | We introduce a new representation and feature extraction method for
biological sequences. Named bio-vectors (BioVec) to refer to biological
sequences in general with protein-vectors (ProtVec) for proteins (amino-acid
sequences) and gene-vectors (GeneVec) for gene sequences, this representation
can be widely used in app... | computer science |
9,903 | A Machine Learning Approach to Predicting the Smoothed Complexity of
Sorting Algorithms | cs.LG | Smoothed analysis is a framework for analyzing the complexity of an
algorithm, acting as a bridge between average and worst-case behaviour. For
example, Quicksort and the Simplex algorithm are widely used in practical
applications, despite their heavy worst-case complexity. Smoothed complexity
aims to better characteri... | computer science |
9,904 | When to Reset Your Keys: Optimal Timing of Security Updates via Learning | cs.LG | Cybersecurity is increasingly threatened by advanced and persistent attacks.
As these attacks are often designed to disable a system (or a critical
resource, e.g., a user account) repeatedly, it is crucial for the defender to
keep updating its security measures to strike a balance between the risk of
being compromised ... | computer science |
9,905 | Generalizing Skills with Semi-Supervised Reinforcement Learning | cs.LG | Deep reinforcement learning (RL) can acquire complex behaviors from low-level
inputs, such as images. However, real-world applications of such methods
require generalizing to the vast variability of the real world. Deep networks
are known to achieve remarkable generalization when provided with massive
amounts of labele... | computer science |
9,906 | Algorithmic Songwriting with ALYSIA | cs.AI | This paper introduces ALYSIA: Automated LYrical SongwrIting Application.
ALYSIA is based on a machine learning model using Random Forests, and we
discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA
was used to create original pop songs that were subsequently recorded and
produced. Finally, we d... | computer science |
9,907 | Enhancing Use Case Points Estimation Method Using Soft Computing
Techniques | cs.SE | Software estimation is a crucial task in software engineering. Software
estimation encompasses cost, effort, schedule, and size. The importance of
software estimation becomes critical in the early stages of the software life
cycle when the details of software have not been revealed yet. Several
commercial and non-comme... | computer science |
9,908 | Deep Learning of Robotic Tasks without a Simulator using Strong and Weak
Human Supervision | cs.AI | We propose a scheme for training a computerized agent to perform complex
human tasks such as highway steering. The scheme is designed to follow a
natural learning process whereby a human instructor teaches a computerized
trainee. The learning process consists of five elements: (i) unsupervised
feature learning; (ii) su... | computer science |
9,909 | Controlling Robot Morphology from Incomplete Measurements | cs.RO | Mobile robots with complex morphology are essential for traversing rough
terrains in Urban Search & Rescue missions (USAR). Since teleoperation of the
complex morphology causes high cognitive load of the operator, the morphology
is controlled autonomously. The autonomous control measures the robot state and
surrounding... | computer science |
9,910 | DeepCancer: Detecting Cancer through Gene Expressions via Deep
Generative Learning | cs.AI | Transcriptional profiling on microarrays to obtain gene expressions has been
used to facilitate cancer diagnosis. We propose a deep generative machine
learning architecture (called DeepCancer) that learn features from unlabeled
microarray data. These models have been used in conjunction with conventional
classifiers th... | computer science |
9,911 | A Unit Selection Methodology for Music Generation Using Deep Neural
Networks | cs.SD | Several methods exist for a computer to generate music based on data
including Markov chains, recurrent neural networks, recombinancy, and grammars.
We explore the use of unit selection and concatenation as a means of generating
music using a procedure based on ranking, where, we consider a unit to be a
variable length... | computer science |
9,912 | Incorporating Human Domain Knowledge into Large Scale Cost Function
Learning | cs.RO | Recent advances have shown the capability of Fully Convolutional Neural
Networks (FCN) to model cost functions for motion planning in the context of
learning driving preferences purely based on demonstration data from human
drivers. While pure learning from demonstrations in the framework of Inverse
Reinforcement Learn... | computer science |
9,913 | Deep Reinforcement Learning with Successor Features for Navigation
across Similar Environments | cs.RO | In this paper we consider the problem of robot navigation in simple maze-like
environments where the robot has to rely on its onboard sensors to perform the
navigation task. In particular, we are interested in solutions to this problem
that do not require localization, mapping or planning. Additionally, we require
that... | computer science |
9,914 | Exploiting sparsity to build efficient kernel based collaborative
filtering for top-N item recommendation | cs.IR | The increasing availability of implicit feedback datasets has raised the
interest in developing effective collaborative filtering techniques able to
deal asymmetrically with unambiguous positive feedback and ambiguous negative
feedback. In this paper, we propose a principled kernel-based collaborative
filtering method ... | computer science |
9,915 | Computing Human-Understandable Strategies | cs.GT | Algorithms for equilibrium computation generally make no attempt to ensure
that the computed strategies are understandable by humans. For instance the
strategies for the strongest poker agents are represented as massive binary
files. In many situations, we would like to compute strategies that can
actually be implement... | computer science |
9,916 | Deep-learning in Mobile Robotics - from Perception to Control Systems: A
Survey on Why and Why not | cs.RO | Deep-learning has dramatically changed the world overnight. It greatly
boosted the development of visual perception, object detection, and speech
recognition, etc. That was attributed to the multiple convolutional processing
layers for abstraction of learning representations from massive data. The
advantages of deep co... | computer science |
9,917 | Efficient iterative policy optimization | cs.AI | We tackle the issue of finding a good policy when the number of policy
updates is limited. This is done by approximating the expected policy reward as
a sequence of concave lower bounds which can be efficiently maximized,
drastically reducing the number of policy updates required to achieve good
performance. We also ex... | computer science |
9,918 | A Contextual-Bandit Approach to Personalized News Article Recommendation | cs.LG | Personalized web services strive to adapt their services (advertisements,
news articles, etc) to individual users by making use of both content and user
information. Despite a few recent advances, this problem remains challenging
for at least two reasons. First, web service is featured with dynamically
changing pools o... | computer science |
9,919 | Inductive Logic Programming in Databases: from Datalog to DL+log | cs.LO | In this paper we address an issue that has been brought to the attention of
the database community with the advent of the Semantic Web, i.e. the issue of
how ontologies (and semantics conveyed by them) can help solving typical
database problems, through a better understanding of KR aspects related to
databases. In part... | computer science |
9,920 | A Formal Approach to Modeling the Memory of a Living Organism | cs.AI | We consider a living organism as an observer of the evolution of its
environment recording sensory information about the state space X of the
environment in real time. Sensory information is sampled and then processed on
two levels. On the biological level, the organism serves as an evaluation
mechanism of the subjecti... | computer science |
9,921 | Adaptive Submodularity: Theory and Applications in Active Learning and
Stochastic Optimization | cs.LG | Solving stochastic optimization problems under partial observability, where
one needs to adaptively make decisions with uncertain outcomes, is a
fundamental but notoriously difficult challenge. In this paper, we introduce
the concept of adaptive submodularity, generalizing submodular set functions to
adaptive policies.... | computer science |
9,922 | CHR(PRISM)-based Probabilistic Logic Learning | cs.PL | PRISM is an extension of Prolog with probabilistic predicates and built-in
support for expectation-maximization learning. Constraint Handling Rules (CHR)
is a high-level programming language based on multi-headed multiset rewrite
rules.
In this paper, we introduce a new probabilistic logic formalism, called
CHRiSM, b... | computer science |
9,923 | Approximate Judgement Aggregation | cs.GT | In this paper we analyze judgement aggregation problems in which a group of
agents independently votes on a set of complex propositions that has some
interdependency constraint between them(e.g., transitivity when describing
preferences). We consider the issue of judgement aggregation from the
perspective of approximat... | computer science |
9,924 | Prediction by Compression | cs.IT | It is well known that text compression can be achieved by predicting the next
symbol in the stream of text data based on the history seen up to the current
symbol. The better the prediction the more skewed the conditional probability
distribution of the next symbol and the shorter the codeword that needs to be
assigned... | computer science |
9,925 | Memristor Crossbar-based Hardware Implementation of IDS Method | cs.LG | Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is
the methodology of soft computing. IDS, as a pattern-based processing unit,
extracts useful information from a system subjected to modeling. In spite of
its excellent potential in solving problems such as classification and modeling
compared ... | computer science |
9,926 | Bootstrapping Intrinsically Motivated Learning with Human Demonstrations | cs.LG | This paper studies the coupling of internally guided learning and social
interaction, and more specifically the improvement owing to demonstrations of
the learning by intrinsic motivation. We present Socially Guided Intrinsic
Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous,
unbounded and n... | computer science |
9,927 | A Study on Using Uncertain Time Series Matching Algorithms in MapReduce
Applications | cs.DC | In this paper, we study CPU utilization time patterns of several Map-Reduce
applications. After extracting running patterns of several applications, the
patterns with their statistical information are saved in a reference database
to be later used to tweak system parameters to efficiently execute unknown
applications i... | computer science |
9,928 | Graph Laplacians on Singular Manifolds: Toward understanding complex
spaces: graph Laplacians on manifolds with singularities and boundaries | cs.AI | Recently, much of the existing work in manifold learning has been done under
the assumption that the data is sampled from a manifold without boundaries and
singularities or that the functions of interest are evaluated away from such
points. At the same time, it can be argued that singularities and boundaries
are an imp... | computer science |
9,929 | Learning-Assisted Automated Reasoning with Flyspeck | cs.AI | The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this... | computer science |
9,930 | Time-Series Classification Through Histograms of Symbolic Polynomials | cs.AI | Time-series classification has attracted considerable research attention due
to the various domains where time-series data are observed, ranging from
medicine to econometrics. Traditionally, the focus of time-series
classification has been on short time-series data composed of a unique pattern
with intraclass pattern d... | computer science |
9,931 | Optimistic Concurrency Control for Distributed Unsupervised Learning | cs.LG | Research on distributed machine learning algorithms has focused primarily on
one of two extremes - algorithms that obey strict concurrency constraints or
algorithms that obey few or no such constraints. We consider an intermediate
alternative in which algorithms optimistically assume that conflicts are
unlikely and if ... | computer science |
9,932 | Learning Lambek grammars from proof frames | cs.LG | In addition to their limpid interface with semantics, categorial grammars
enjoy another important property: learnability. This was first noticed by
Buskowsky and Penn and further studied by Kanazawa, for Bar-Hillel categorial
grammars.
What about Lambek categorial grammars? In a previous paper we showed that
product ... | computer science |
9,933 | Lemma Mining over HOL Light | cs.AI | Large formal mathematical libraries consist of millions of atomic inference
steps that give rise to a corresponding number of proved statements (lemmas).
Analogously to the informal mathematical practice, only a tiny fraction of such
statements is named and re-used in later proofs by formal mathematicians. In
this work... | computer science |
9,934 | Post-Proceedings of the First International Workshop on Learning and
Nonmonotonic Reasoning | cs.AI | Knowledge Representation and Reasoning and Machine Learning are two important
fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming
(ASP) provide formal languages for representing and reasoning with commonsense
knowledge and realize declarative problem solving in AI. On the other side,
Inductiv... | computer science |
9,935 | Volumetric Spanners: an Efficient Exploration Basis for Learning | cs.LG | Numerous machine learning problems require an exploration basis - a mechanism
to explore the action space. We define a novel geometric notion of exploration
basis with low variance, called volumetric spanners, and give efficient
algorithms to construct such a basis.
We show how efficient volumetric spanners give rise... | computer science |
9,936 | Distributed Policy Evaluation Under Multiple Behavior Strategies | cs.MA | We apply diffusion strategies to develop a fully-distributed cooperative
reinforcement learning algorithm in which agents in a network communicate only
with their immediate neighbors to improve predictions about their environment.
The algorithm can also be applied to off-policy learning, meaning that the
agents can pre... | computer science |
9,937 | CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit | cs.AI | Continuous time Bayesian network classifiers are designed for temporal
classification of multivariate streaming data when time duration of events
matters and the class does not change over time. This paper introduces the
CTBNCToolkit: an open source Java toolkit which provides a stand-alone
application for temporal cla... | computer science |
9,938 | XML Matchers: approaches and challenges | cs.DB | Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R sc... | computer science |
9,939 | Collaborative Filtering Ensemble for Personalized Name Recommendation | cs.IR | Out of thousands of names to choose from, picking the right one for your
child is a daunting task. In this work, our objective is to help parents making
an informed decision while choosing a name for their baby. We follow a
recommender system approach and combine, in an ensemble, the individual
rankings produced by sim... | computer science |
9,940 | Are There Good Mistakes? A Theoretical Analysis of CEGIS | cs.LO | Counterexample-guided inductive synthesis CEGIS is used to synthesize
programs from a candidate space of programs. The technique is guaranteed to
terminate and synthesize the correct program if the space of candidate programs
is finite. But the technique may or may not terminate with the correct program
if the candidat... | computer science |
9,941 | Non-myopic learning in repeated stochastic games | cs.GT | In repeated stochastic games (RSGs), an agent must quickly adapt to the
behavior of previously unknown associates, who may themselves be learning. This
machine-learning problem is particularly challenging due, in part, to the
presence of multiple (even infinite) equilibria and inherently large strategy
spaces. In this ... | computer science |
9,942 | Top Rank Optimization in Linear Time | cs.LG | Bipartite ranking aims to learn a real-valued ranking function that orders
positive instances before negative instances. Recent efforts of bipartite
ranking are focused on optimizing ranking accuracy at the top of the ranked
list. Most existing approaches are either to optimize task specific metrics or
to extend the ra... | computer science |
9,943 | Distributed Submodular Maximization | cs.LG | Many large-scale machine learning problems--clustering, non-parametric
learning, kernel machines, etc.--require selecting a small yet representative
subset from a large dataset. Such problems can often be reduced to maximizing a
submodular set function subject to various constraints. Classical approaches to
submodular ... | computer science |
9,944 | Learning Fuzzy Controllers in Mobile Robotics with Embedded
Preprocessing | cs.RO | The automatic design of controllers for mobile robots usually requires two
stages. In the first stage,sensorial data are preprocessed or transformed into
high level and meaningful values of variables whichare usually defined from
expert knowledge. In the second stage, a machine learning technique is applied
toobtain a ... | computer science |
9,945 | Highly comparative fetal heart rate analysis | cs.LG | A database of fetal heart rate (FHR) time series measured from 7221 patients
during labor is analyzed with the aim of learning the types of features of
these recordings that are informative of low cord pH. Our 'highly comparative'
analysis involves extracting over 9000 time-series analysis features from each
FHR time s... | computer science |
9,946 | A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic | cs.CY | In recent years, adaptive learning systems rely increasingly on learning
hierarchy to customize the educational logic developed in their courses. Most
approaches do not consider that the relationships of prerequisites between the
skills are fuzzy relationships. In this article, we describe a new approach of
a practical... | computer science |
9,947 | Efficient Decision-Making by Volume-Conserving Physical Object | cs.AI | We demonstrate that any physical object, as long as its volume is conserved
when coupled with suitable operations, provides a sophisticated decision-making
capability. We consider the problem of finding, as accurately and quickly as
possible, the most profitable option from a set of options that gives
stochastic reward... | computer science |
9,948 | Grounding Hierarchical Reinforcement Learning Models for Knowledge
Transfer | cs.LG | Methods of deep machine learning enable to to reuse low-level representations
efficiently for generating more abstract high-level representations.
Originally, deep learning has been applied passively (e.g., for classification
purposes). Recently, it has been extended to estimate the value of actions for
autonomous agen... | computer science |
9,949 | Robobarista: Object Part based Transfer of Manipulation Trajectories
from Crowd-sourcing in 3D Pointclouds | cs.RO | There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation p... | computer science |
9,950 | Learning of Behavior Trees for Autonomous Agents | cs.RO | Definition of an accurate system model for Automated Planner (AP) is often
impractical, especially for real-world problems. Conversely, off-the-shelf
planners fail to scale up and are domain dependent. These drawbacks are
inherited from conventional transition systems such as Finite State Machines
(FSMs) that describes... | computer science |
9,951 | Strategic Teaching and Learning in Games | cs.GT | It is known that there are uncoupled learning heuristics leading to Nash
equilibrium in all finite games. Why should players use such learning
heuristics and where could they come from? We show that there is no uncoupled
learning heuristic leading to Nash equilibrium in all finite games that a
player has an incentive t... | computer science |
9,952 | Discovering Valuable Items from Massive Data | cs.LG | Suppose there is a large collection of items, each with an associated cost
and an inherent utility that is revealed only once we commit to selecting it.
Given a budget on the cumulative cost of the selected items, how can we pick a
subset of maximal value? This task generalizes several important problems such
as multi-... | computer science |
9,953 | A Framework for Constrained and Adaptive Behavior-Based Agents | cs.AI | Behavior Trees are commonly used to model agents for robotics and games,
where constrained behaviors must be designed by human experts in order to
guarantee that these agents will execute a specific chain of actions given a
specific set of perceptions. In such application areas, learning is a desirable
feature to provi... | computer science |
9,954 | Deep Knowledge Tracing | cs.AI | Knowledge tracing---where a machine models the knowledge of a student as they
interact with coursework---is a well established problem in computer supported
education. Though effectively modeling student knowledge would have high
educational impact, the task has many inherent challenges. In this paper we
explore the ut... | computer science |
9,955 | Fast Convergence of Regularized Learning in Games | cs.GT | We show that natural classes of regularized learning algorithms with a form
of recency bias achieve faster convergence rates to approximate efficiency and
to coarse correlated equilibria in multiplayer normal form games. When each
player in a game uses an algorithm from our class, their individual regret
decays at $O(T... | computer science |
9,956 | Semi-supervised Multi-sensor Classification via Consensus-based
Multi-View Maximum Entropy Discrimination | cs.IT | In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement ... | computer science |
9,957 | Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning
of Dynamic Visuo-Motor-Attentional Coordination | cs.AI | The current study examines how adequate coordination among different
cognitive processes including visual recognition, attention switching, action
preparation and generation can be developed via learning of robots by
introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
The proposed model is b... | computer science |
9,958 | A New Framework for Distributed Submodular Maximization | cs.DS | A wide variety of problems in machine learning, including exemplar
clustering, document summarization, and sensor placement, can be cast as
constrained submodular maximization problems. A lot of recent effort has been
devoted to developing distributed algorithms for these problems. However, these
results suffer from hi... | computer science |
9,959 | Solomonoff Induction Violates Nicod's Criterion | cs.LG | Nicod's criterion states that observing a black raven is evidence for the
hypothesis H that all ravens are black. We show that Solomonoff induction does
not satisfy Nicod's criterion: there are time steps in which observing black
ravens decreases the belief in H. Moreover, while observing any computable
infinite string... | computer science |
9,960 | Learning Action Models: Qualitative Approach | cs.LG | In dynamic epistemic logic, actions are described using action models. In
this paper we introduce a framework for studying learnability of action models
from observations. We present first results concerning propositional action
models. First we check two basic learnability criteria: finite identifiability
(conclusivel... | computer science |
9,961 | Framework for learning agents in quantum environments | cs.AI | In this paper we provide a broad framework for describing learning agents in
general quantum environments. We analyze the types of classically specified
environments which allow for quantum enhancements in learning, by contrasting
environments to quantum oracles. We show that whether or not quantum
improvements are at ... | computer science |
9,962 | Schema Independent Relational Learning | cs.DB | Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represente... | computer science |
9,963 | ERBlox: Combining Matching Dependencies with Machine Learning for Entity
Resolution | cs.DB | Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called matching dependencies (MDs) have been proposed for specifying
similarity co... | computer science |
9,964 | Research: Analysis of Transport Model that Approximates Decision Taker's
Preferences | cs.LG | Paper provides a method for solving the reverse Monge-Kantorovich transport
problem (TP). It allows to accumulate positive decision-taking experience made
by decision-taker in situations that can be presented in the form of TP. The
initial data for the solution of the inverse TP is the information on orders,
inventorie... | computer science |
9,965 | Recurrent Reinforcement Learning: A Hybrid Approach | cs.LG | Successful applications of reinforcement learning in real-world problems
often require dealing with partially observable states. It is in general very
challenging to construct and infer hidden states as they often depend on the
agent's entire interaction history and may require substantial domain
knowledge. In this wor... | computer science |
9,966 | Sports highlights generation based on acoustic events detection: A rugby
case study | cs.SD | We approach the challenging problem of generating highlights from sports
broadcasts utilizing audio information only. A language-independent,
multi-stage classification approach is employed for detection of key acoustic
events which then act as a platform for summarization of highlight scenes.
Objective results and hum... | computer science |
9,967 | Learning Adversary Behavior in Security Games: A PAC Model Perspective | cs.AI | Recent applications of Stackelberg Security Games (SSG), from wildlife crime
to urban crime, have employed machine learning tools to learn and predict
adversary behavior using available data about defender-adversary interactions.
Given these recent developments, this paper commits to an approach of directly
learning th... | computer science |
9,968 | Toward an Efficient Multi-class Classification in an Open Universe | cs.LG | Classification is a fundamental task in machine learning and data mining.
Existing classification methods are designed to classify unknown instances
within a set of previously known training classes. Such a classification takes
the form of a prediction within a closed-set of classes. However, a more
realistic scenario ... | computer science |
9,969 | Learning in Auctions: Regret is Hard, Envy is Easy | cs.GT | A line of recent work provides welfare guarantees of simple combinatorial
auction formats, such as selling m items via simultaneous second price auctions
(SiSPAs) (Christodoulou et al. 2008, Bhawalkar and Roughgarden 2011, Feldman et
al. 2013). These guarantees hold even when the auctions are repeatedly executed
and pl... | computer science |
9,970 | Seeing the Unseen Network: Inferring Hidden Social Ties from
Respondent-Driven Sampling | cs.SI | Learning about the social structure of hidden and hard-to-reach populations
--- such as drug users and sex workers --- is a major goal of epidemiological
and public health research on risk behaviors and disease prevention.
Respondent-driven sampling (RDS) is a peer-referral process widely used by many
health organizati... | computer science |
9,971 | Multiagent Cooperation and Competition with Deep Reinforcement Learning | cs.AI | Multiagent systems appear in most social, economical, and political
situations. In the present work we extend the Deep Q-Learning Network
architecture proposed by Google DeepMind to multiagent environments and
investigate how two agents controlled by independent Deep Q-Networks interact
in the classic videogame Pong. B... | computer science |
9,972 | Robotic Search & Rescue via Online Multi-task Reinforcement Learning | cs.AI | Reinforcement learning (RL) is a general and well-known method that a robot
can use to learn an optimal control policy to solve a particular task. We would
like to build a versatile robot that can learn multiple tasks, but using RL for
each of them would be prohibitively expensive in terms of both time and
wear-and-tea... | computer science |
9,973 | Object-based World Modeling in Semi-Static Environments with Dependent
Dirichlet-Process Mixtures | cs.AI | To accomplish tasks in human-centric indoor environments, robots need to
represent and understand the world in terms of objects and their attributes. We
refer to this attribute-based representation as a world model, and consider how
to acquire it via noisy perception and maintain it over time, as objects are
added, cha... | computer science |
9,974 | Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization | cs.NA | Bayesian matrix completion has been studied based on a low-rank matrix
factorization formulation with promising results. However, little work has been
done on Bayesian matrix completion based on the more direct spectral
regularization formulation. We fill this gap by presenting a novel Bayesian
matrix completion method... | computer science |
9,975 | Deep Reinforcement Learning with Attention for Slate Markov Decision
Processes with High-Dimensional States and Actions | cs.AI | Many real-world problems come with action spaces represented as feature
vectors. Although high-dimensional control is a largely unsolved problem, there
has recently been progress for modest dimensionalities. Here we report on a
successful attempt at addressing problems of dimensionality as high as $2000$,
of a particul... | computer science |
9,976 | Risk-Constrained Reinforcement Learning with Percentile Risk Criteria | cs.AI | In many sequential decision-making problems one is interested in minimizing
an expected cumulative cost while taking into account \emph{risk}, i.e.,
increased awareness of events of small probability and high consequences.
Accordingly, the objective of this paper is to present efficient reinforcement
learning algorithm... | computer science |
9,977 | Scalable Models for Computing Hierarchies in Information Networks | cs.AI | Information hierarchies are organizational structures that often used to
organize and present large and complex information as well as provide a
mechanism for effective human navigation. Fortunately, many statistical and
computational models exist that automatically generate hierarchies; however,
the existing approache... | computer science |
9,978 | Learning Preferences for Manipulation Tasks from Online Coactive
Feedback | cs.RO | We consider the problem of learning preferences over trajectories for mobile
manipulators such as personal robots and assembly line robots. The preferences
we learn are more intricate than simple geometric constraints on trajectories;
they are rather governed by the surrounding context of various objects and
human inte... | computer science |
9,979 | Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal
Embedding | cs.RO | There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation p... | computer science |
9,980 | Trust from the past: Bayesian Personalized Ranking based Link Prediction
in Knowledge Graphs | cs.LG | Link prediction, or predicting the likelihood of a link in a knowledge graph
based on its existing state is a key research task. It differs from a
traditional link prediction task in that the links in a knowledge graph are
categorized into different predicates and the link prediction performance of
different predicates... | computer science |
9,981 | Quantum machine learning with glow for episodic tasks and decision games | cs.AI | We consider a general class of models, where a reinforcement learning (RL)
agent learns from cyclic interactions with an external environment via
classical signals. Perceptual inputs are encoded as quantum states, which are
subsequently transformed by a quantum channel representing the agent's memory,
while the outcome... | computer science |
9,982 | ERBlox: Combining Matching Dependencies with Machine Learning for Entity
Resolution | cs.DB | Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called "matching dependencies" (MDs) have been proposed for specifying
similarity ... | computer science |
9,983 | Network of Bandits insure Privacy of end-users | cs.AI | In order to distribute the best arm identification task as close as possible
to the user's devices, on the edge of the Radio Access Network, we propose a
new problem setting, where distributed players collaborate to find the best
arm. This architecture guarantees privacy to end-users since no events are
stored. The onl... | computer science |
9,984 | Random Forest Based Approach for Concept Drift Handling | cs.AI | Concept drift has potential in smart grid analysis because the socio-economic
behaviour of consumers is not governed by the laws of physics. Likewise there
are also applications in wind power forecasting. In this paper we present
decision tree ensemble classification method based on the Random Forest
algorithm for conc... | computer science |
9,985 | Recommendations as Treatments: Debiasing Learning and Evaluation | cs.LG | Most data for evaluating and training recommender systems is subject to
selection biases, either through self-selection by the users or through the
actions of the recommendation system itself. In this paper, we provide a
principled approach to handling selection biases, adapting models and
estimation techniques from ca... | computer science |
9,986 | Machine learning meets network science: dimensionality reduction for
fast and efficient embedding of networks in the hyperbolic space | cs.AI | Complex network topologies and hyperbolic geometry seem specularly connected,
and one of the most fascinating and challenging problems of recent complex
network theory is to map a given network to its hyperbolic space. The
Popularity Similarity Optimization (PSO) model represents - at the moment - the
climax of this th... | computer science |
9,987 | Latent Skill Embedding for Personalized Lesson Sequence Recommendation | cs.LG | Students in online courses generate large amounts of data that can be used to
personalize the learning process and improve quality of education. In this
paper, we present the Latent Skill Embedding (LSE), a probabilistic model of
students and educational content that can be used to recommend personalized
sequences of l... | computer science |
9,988 | How effective can simple ordinal peer grading be? | cs.AI | Ordinal peer grading has been proposed as a simple and scalable solution for
computing reliable information about student performance in massive open online
courses. The idea is to outsource the grading task to the students themselves
as follows. After the end of an exam, each student is asked to rank ---in terms
of qu... | computer science |
9,989 | Guided Cost Learning: Deep Inverse Optimal Control via Policy
Optimization | cs.LG | Reinforcement learning can acquire complex behaviors from high-level
specifications. However, defining a cost function that can be optimized
effectively and encodes the correct task is challenging in practice. We explore
how inverse optimal control (IOC) can be used to learn behaviors from
demonstrations, with applicat... | computer science |
9,990 | Continuous Deep Q-Learning with Model-based Acceleration | cs.LG | Model-free reinforcement learning has been successfully applied to a range of
challenging problems, and has recently been extended to handle large neural
network policies and value functions. However, the sample complexity of
model-free algorithms, particularly when using high-dimensional function
approximators, tends ... | computer science |
9,991 | Deep Reinforcement Learning from Self-Play in Imperfect-Information
Games | cs.LG | Many real-world applications can be described as large-scale games of
imperfect information. To deal with these challenging domains, prior work has
focused on computing Nash equilibria in a handcrafted abstraction of the
domain. In this paper we introduce the first scalable end-to-end approach to
learning approximate N... | computer science |
9,992 | Hierarchical Decision Making In Electricity Grid Management | cs.AI | The power grid is a complex and vital system that necessitates careful
reliability management. Managing the grid is a difficult problem with multiple
time scales of decision making and stochastic behavior due to renewable energy
generations, variable demand and unplanned outages. Solving this problem in the
face of unc... | computer science |
9,993 | Unscented Bayesian Optimization for Safe Robot Grasping | cs.RO | We address the robot grasp optimization problem of unknown objects
considering uncertainty in the input space. Grasping unknown objects can be
achieved by using a trial and error exploration strategy. Bayesian optimization
is a sample efficient optimization algorithm that is especially suitable for
this setups as it ac... | computer science |
9,994 | UTA-poly and UTA-splines: additive value functions with polynomial
marginals | math.OC | Additive utility function models are widely used in multiple criteria
decision analysis. In such models, a numerical value is associated to each
alternative involved in the decision problem. It is computed by aggregating the
scores of the alternative on the different criteria of the decision problem.
The score of an al... | computer science |
9,995 | Near-Optimal Active Learning of Halfspaces via Query Synthesis in the
Noisy Setting | cs.AI | In this paper, we consider the problem of actively learning a linear
classifier through query synthesis where the learner can construct artificial
queries in order to estimate the true decision boundaries. This problem has
recently gained a lot of interest in automated science and adversarial reverse
engineering for wh... | computer science |
9,996 | From virtual demonstration to real-world manipulation using LSTM and MDN | cs.RO | Robots assisting the disabled or elderly must perform complex manipulation
tasks and must adapt to the home environment and preferences of their user.
Learning from demonstration is a promising choice, that would allow the
non-technical user to teach the robot different tasks. However, collecting
demonstrations in the ... | computer science |
9,997 | Item2Vec: Neural Item Embedding for Collaborative Filtering | cs.LG | Many Collaborative Filtering (CF) algorithms are item-based in the sense that
they analyze item-item relations in order to produce item similarities.
Recently, several works in the field of Natural Language Processing (NLP)
suggested to learn a latent representation of words using neural embedding
algorithms. Among the... | computer science |
9,998 | Automated Correction for Syntax Errors in Programming Assignments using
Recurrent Neural Networks | cs.PL | We present a method for automatically generating repair feedback for syntax
errors for introductory programming problems. Syntax errors constitute one of
the largest classes of errors (34%) in our dataset of student submissions
obtained from a MOOC course on edX. The previous techniques for generating
automated feed- b... | computer science |
9,999 | Low-Complexity Stochastic Generalized Belief Propagation | cs.LG | The generalized belief propagation (GBP), introduced by Yedidia et al., is an
extension of the belief propagation (BP) algorithm, which is widely used in
different problems involved in calculating exact or approximate marginals of
probability distributions. In many problems, it has been observed that the
accuracy of GB... | computer science |
10,000 | Query-Efficient Imitation Learning for End-to-End Autonomous Driving | cs.LG | One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minim... | computer science |
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