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9,801 | Refining Recency Search Results with User Click Feedback | cs.IR | Traditional machine-learned ranking systems for web search are often trained
to capture stationary relevance of documents to queries, which has limited
ability to track non-stationary user intention in a timely manner. In recency
search, for instance, the relevance of documents to a query on breaking news
often changes... | computer science |
9,802 | Learning Equilibria with Partial Information in Decentralized Wireless
Networks | cs.LG | In this article, a survey of several important equilibrium concepts for
decentralized networks is presented. The term decentralized is used here to
refer to scenarios where decisions (e.g., choosing a power allocation policy)
are taken autonomously by devices interacting with each other (e.g., through
mutual interferen... | computer science |
9,803 | Learning RoboCup-Keepaway with Kernels | cs.AI | We apply kernel-based methods to solve the difficult reinforcement learning
problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in
keepaway are the high-dimensionality of the state space (rendering conventional
discretization-based function approximation like tilecoding infeasible), the
stochasticity d... | computer science |
9,804 | Learning, Social Intelligence and the Turing Test - why an
"out-of-the-box" Turing Machine will not pass the Turing Test | cs.AI | The Turing Test (TT) checks for human intelligence, rather than any putative
general intelligence. It involves repeated interaction requiring learning in
the form of adaption to the human conversation partner. It is a macro-level
post-hoc test in contrast to the definition of a Turing Machine (TM), which is
a prior mic... | computer science |
9,805 | Objective Function Designing Led by User Preferences Acquisition | cs.LG | Many real world problems can be defined as optimisation problems in which the
aim is to maximise an objective function. The quality of obtained solution is
directly linked to the pertinence of the used objective function. However,
designing such function, which has to translate the user needs, is usually
fastidious. In... | computer science |
9,806 | Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree
Policies and Direct Policy Search | cs.SY | Direct policy search (DPS) and look-ahead tree (LT) policies are two widely
used classes of techniques to produce high performance policies for sequential
decision-making problems. To make DPS approaches work well, one crucial issue
is to select an appropriate space of parameterized policies with respect to the
targete... | computer science |
9,807 | Learning implicitly in reasoning in PAC-Semantics | cs.AI | We consider the problem of answering queries about formulas of propositional
logic based on background knowledge partially represented explicitly as other
formulas, and partially represented as partially obscured examples
independently drawn from a fixed probability distribution, where the queries
are answered with res... | computer science |
9,808 | Submodularity in Batch Active Learning and Survey Problems on Gaussian
Random Fields | cs.LG | Many real-world datasets can be represented in the form of a graph whose edge
weights designate similarities between instances. A discrete Gaussian random
field (GRF) model is a finite-dimensional Gaussian process (GP) whose prior
covariance is the inverse of a graph Laplacian. Minimizing the trace of the
predictive co... | computer science |
9,809 | Decayed MCMC Filtering | cs.AI | Filtering---estimating the state of a partially observable Markov process
from a sequence of observations---is one of the most widely studied problems in
control theory, AI, and computational statistics. Exact computation of the
posterior distribution is generally intractable for large discrete systems and
for nonlinea... | computer science |
9,810 | An MDP-based Recommender System | cs.LG | Typical Recommender systems adopt a static view of the recommendation process
and treat it as a prediction problem. We argue that it is more appropriate to
view the problem of generating recommendations as a sequential decision problem
and, consequently, that Markov decision processes (MDP) provide a more
appropriate m... | computer science |
9,811 | Using Temporal Data for Making Recommendations | cs.IR | We treat collaborative filtering as a univariate time series estimation
problem: given a user's previous votes, predict the next vote. We describe two
families of methods for transforming data to encode time order in ways amenable
to off-the-shelf classification and density estimation tools, and examine the
results of ... | computer science |
9,812 | BliStr: The Blind Strategymaker | cs.AI | BliStr is a system that automatically develops strategies for E prover on a
large set of problems. The main idea is to interleave (i) iterated
low-timelimit local search for new strategies on small sets of similar easy
problems with (ii) higher-timelimit evaluation of the new strategies on all
problems. The accumulated... | computer science |
9,813 | Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks | cs.LG | Particle filters (PFs) are powerful sampling-based inference/learning
algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a
principled way, any type of probability distribution, nonlinearity and
non-stationarity. They have appeared in several fields under such names as
"condensation", "sequentia... | computer science |
9,814 | Dependency Networks for Collaborative Filtering and Data Visualization | cs.AI | We describe a graphical model for probabilistic relationships---an
alternative to the Bayesian network---called a dependency network. The graph of
a dependency network, unlike a Bayesian network, is potentially cyclic. The
probability component of a dependency network, like a Bayesian network, is a
set of conditional d... | computer science |
9,815 | Pattern Matching for Self- Tuning of MapReduce Jobs | cs.DC | In this paper, we study CPU utilization time patterns of several MapReduce
applications. After extracting running patterns of several applications, they
are saved in a reference database to be later used to tweak system parameters
to efficiently execute unknown applications in future. To achieve this goal,
CPU utilizat... | computer science |
9,816 | Recycling Proof Patterns in Coq: Case Studies | cs.AI | Development of Interactive Theorem Provers has led to the creation of big
libraries and varied infrastructures for formal proofs. However, despite (or
perhaps due to) their sophistication, the re-use of libraries by non-experts or
across domains is a challenge. In this paper, we provide detailed case studies
and evalua... | computer science |
9,817 | Parallel Computation Is ESS | cs.LG | There are enormous amount of examples of Computation in nature, exemplified
across multiple species in biology. One crucial aim for these computations
across all life forms their ability to learn and thereby increase the chance of
their survival. In the current paper a formal definition of autonomous learning
is propos... | computer science |
9,818 | Inference and learning in probabilistic logic programs using weighted
Boolean formulas | cs.AI | Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. This paper investigates how classical
inference and learning tasks known from the graphical model community can be
tackled for probabilistic logic programs. Several such tasks such as computing
the marginals giv... | computer science |
9,819 | Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee | cs.LG | Local Policy Search is a popular reinforcement learning approach for handling
large state spaces. Formally, it searches locally in a paramet erized policy
space in order to maximize the associated value function averaged over some
predefined distribution. It is probably commonly b elieved that the best one
can hope in ... | computer science |
9,820 | Galerkin Methods for Complementarity Problems and Variational
Inequalities | cs.LG | Complementarity problems and variational inequalities arise in a wide variety
of areas, including machine learning, planning, game theory, and physical
simulation. In all of these areas, to handle large-scale problem instances, we
need fast approximate solution methods. One promising idea is Galerkin
approximation, in ... | computer science |
9,821 | Synthesizing Manipulation Sequences for Under-Specified Tasks using
Unrolled Markov Random Fields | cs.RO | Many tasks in human environments require performing a sequence of navigation
and manipulation steps involving objects. In unstructured human environments,
the location and configuration of the objects involved often change in
unpredictable ways. This requires a high-level planning strategy that is robust
and flexible i... | computer science |
9,822 | Design of an Agent for Answering Back in Smart Phones | cs.AI | The objective of the paper is to design an agent which provides efficient
response to the caller when a call goes unanswered in smartphones. The agent
provides responses through text messages, email etc stating the most likely
reason as to why the callee is unable to answer a call. Responses are composed
taking into co... | computer science |
9,823 | The many faces of optimism - Extended version | cs.AI | The exploration-exploitation dilemma has been an intriguing and unsolved
problem within the framework of reinforcement learning. "Optimism in the face
of uncertainty" and model building play central roles in advanced exploration
methods. Here, we integrate several concepts and obtain a fast and simple
algorithm. We sho... | computer science |
9,824 | The use of entropy to measure structural diversity | cs.LG | In this paper entropy based methods are compared and used to measure
structural diversity of an ensemble of 21 classifiers. This measure is mostly
applied in ecology, whereby species counts are used as a measure of diversity.
The measures used were Shannon entropy, Simpsons and the Berger Parker
diversity indexes. As t... | computer science |
9,825 | Quantum reinforcement learning | cs.AI | The key approaches for machine learning, especially learning in unknown
probabilistic environments are new representations and computation mechanisms.
In this paper, a novel quantum reinforcement learning (QRL) method is proposed
by combining quantum theory and reinforcement learning (RL). Inspired by the
state superpo... | computer science |
9,826 | A Novel Clustering Algorithm Based on a Modified Model of Random Walk | cs.LG | We introduce a modified model of random walk, and then develop two novel
clustering algorithms based on it. In the algorithms, each data point in a
dataset is considered as a particle which can move at random in space according
to the preset rules in the modified model. Further, this data point may be also
viewed as a ... | computer science |
9,827 | On the Possibility of Learning in Reactive Environments with Arbitrary
Dependence | cs.LG | We address the problem of reinforcement learning in which observations may
exhibit an arbitrary form of stochastic dependence on past observations and
actions, i.e. environments more general than (PO)MDPs. The task for an agent is
to attain the best possible asymptotic reward where the true generating
environment is un... | computer science |
9,828 | A Monte Carlo AIXI Approximation | cs.AI | This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. Our approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the de... | computer science |
9,829 | Lower Bounds for BMRM and Faster Rates for Training SVMs | cs.LG | Regularized risk minimization with the binary hinge loss and its variants
lies at the heart of many machine learning problems. Bundle methods for
regularized risk minimization (BMRM) and the closely related SVMStruct are
considered the best general purpose solvers to tackle this problem. It was
recently shown that BMRM... | computer science |
9,830 | Abstraction Super-structuring Normal Forms: Towards a Theory of
Structural Induction | cs.AI | Induction is the process by which we obtain predictive laws or theories or
models of the world. We consider the structural aspect of induction. We answer
the question as to whether we can find a finite and minmalistic set of
operations on structural elements in terms of which any theory can be
expressed. We identify ab... | computer science |
9,831 | Text Classification: A Sequential Reading Approach | cs.AI | We propose to model the text classification process as a sequential decision
process. In this process, an agent learns to classify documents into topics
while reading the document sentences sequentially and learns to stop as soon as
enough information was read for deciding. The proposed algorithm is based on a
modelisa... | computer science |
9,832 | Normative design using inductive learning | cs.LO | In this paper we propose a use-case-driven iterative design methodology for
normative frameworks, also called virtual institutions, which are used to
govern open systems. Our computational model represents the normative framework
as a logic program under answer set semantics (ASP). By means of an inductive
logic progra... | computer science |
9,833 | Loop corrections for approximate inference | cs.AI | We propose a method for improving approximate inference methods that corrects
for the influence of loops in the graphical model. The method is applicable to
arbitrary factor graphs, provided that the size of the Markov blankets is not
too large. It is an alternative implementation of an idea introduced recently
by Mont... | computer science |
9,834 | Approximation of the Two-Part MDL Code | cs.LG | Approximation of the optimal two-part MDL code for given data, through
successive monotonically length-decreasing two-part MDL codes, has the
following properties: (i) computation of each step may take arbitrarily long;
(ii) we may not know when we reach the optimum, or whether we will reach the
optimum at all; (iii) t... | computer science |
9,835 | Feature Markov Decision Processes | cs.AI | General purpose intelligent learning agents cycle through (complex,non-MDP)
sequences of observations, actions, and rewards. On the other hand,
reinforcement learning is well-developed for small finite state Markov Decision
Processes (MDPs). So far it is an art performed by human designers to extract
the right state re... | computer science |
9,836 | Feature Dynamic Bayesian Networks | cs.AI | Feature Markov Decision Processes (PhiMDPs) are well-suited for learning
agents in general environments. Nevertheless, unstructured (Phi)MDPs are
limited to relatively simple environments. Structured MDPs like Dynamic
Bayesian Networks (DBNs) are used for large-scale real-world problems. In this
article I extend PhiMDP... | computer science |
9,837 | Efficiently Learning a Detection Cascade with Sparse Eigenvectors | cs.MM | In this work, we first show that feature selection methods other than
boosting can also be used for training an efficient object detector. In
particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA)
\cite{Moghaddam2007Fast} for its conceptual simplicity and computational
efficiency; and slightly bett... | computer science |
9,838 | Time manipulation technique for speeding up reinforcement learning in
simulations | cs.AI | A technique for speeding up reinforcement learning algorithms by using time
manipulation is proposed. It is applicable to failure-avoidance control
problems running in a computer simulation. Turning the time of the simulation
backwards on failure events is shown to speed up the learning by 260% and
improve the state sp... | computer science |
9,839 | Feature Reinforcement Learning: Part I: Unstructured MDPs | cs.LG | General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state ... | computer science |
9,840 | Open Problems in Universal Induction & Intelligence | cs.AI | Specialized intelligent systems can be found everywhere: finger print,
handwriting, speech, and face recognition, spam filtering, chess and other game
programs, robots, et al. This decade the first presumably complete mathematical
theory of artificial intelligence based on universal
induction-prediction-decision-action... | computer science |
9,841 | Near-Optimal Bayesian Active Learning with Noisy Observations | cs.LG | We tackle the fundamental problem of Bayesian active learning with noise,
where we need to adaptively select from a number of expensive tests in order to
identify an unknown hypothesis sampled from a known prior distribution. In the
case of noise-free observations, a greedy algorithm called generalized binary
search (G... | computer science |
9,842 | Analysing the behaviour of robot teams through relational sequential
pattern mining | cs.AI | This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the ... | computer science |
9,843 | Dyna-H: a heuristic planning reinforcement learning algorithm applied to
role-playing-game strategy decision systems | cs.AI | In a Role-Playing Game, finding optimal trajectories is one of the most
important tasks. In fact, the strategy decision system becomes a key component
of a game engine. Determining the way in which decisions are taken (online,
batch or simulated) and the consumed resources in decision making (e.g.
execution time, memor... | computer science |
9,844 | Active Markov Information-Theoretic Path Planning for Robotic
Environmental Sensing | cs.LG | Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequentl... | computer science |
9,845 | Revisiting the Training of Logic Models of Protein Signaling Networks
with a Formal Approach based on Answer Set Programming | cs.AI | A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to hig... | computer science |
9,846 | ET-LDA: Joint Topic Modeling For Aligning, Analyzing and Sensemaking of
Public Events and Their Twitter Feeds | cs.LG | Social media channels such as Twitter have emerged as popular platforms for
crowds to respond to public events such as speeches, sports and debates. While
this promises tremendous opportunities to understand and make sense of the
reception of an event from the social media, the promises come entwined with
significant t... | computer science |
9,847 | Learning Onto-Relational Rules with Inductive Logic Programming | cs.AI | Rules complement and extend ontologies on the Semantic Web. We refer to these
rules as onto-relational since they combine DL-based ontology languages and
Knowledge Representation formalisms supporting the relational data model within
the tradition of Logic Programming and Deductive Databases. Rule authoring is a
very d... | computer science |
9,848 | Inferring Strategies from Limited Reconnaissance in Real-time Strategy
Games | cs.AI | In typical real-time strategy (RTS) games, enemy units are visible only when
they are within sight range of a friendly unit. Knowledge of an opponent's
disposition is limited to what can be observed through scouting. Information is
costly, since units dedicated to scouting are unavailable for other purposes,
and the en... | computer science |
9,849 | PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large
Scale Preference Feedback | cs.RO | We consider the problem of learning user preferences over robot trajectories
for environments rich in objects and humans. This is challenging because the
criterion defining a good trajectory varies with users, tasks and interactions
in the environment. We represent trajectory preferences using a cost function
that the ... | computer science |
9,850 | EigenEvent: An Algorithm for Event Detection from Complex Data Streams
in Syndromic Surveillance | cs.AI | Syndromic surveillance systems continuously monitor multiple pre-diagnostic
daily streams of indicators from different regions with the aim of early
detection of disease outbreaks. The main objective of these systems is to
detect outbreaks hours or days before the clinical and laboratory confirmation.
The type of data ... | computer science |
9,851 | Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian
Hierarchical Models | stat.CO | Sampling from hierarchical Bayesian models is often difficult for MCMC
methods, because of the strong correlations between the model parameters and
the hyperparameters. Recent Riemannian manifold Hamiltonian Monte Carlo (RMHMC)
methods have significant potential advantages in this setting, but are
computationally expen... | computer science |
9,852 | Exact Decoding on Latent Variable Conditional Models is NP-Hard | cs.AI | Latent variable conditional models, including the latent conditional random
fields as a special case, are popular models for many natural language
processing and vision processing tasks. The computational complexity of the
exact decoding/inference in latent conditional random fields is unclear. In
this paper, we try to... | computer science |
9,853 | Latent Semantic Learning with Structured Sparse Representation for Human
Action Recognition | cs.MM | This paper proposes a novel latent semantic learning method for extracting
high-level features (i.e. latent semantics) from a large vocabulary of abundant
mid-level features (i.e. visual keywords) with structured sparse
representation, which can help to bridge the semantic gap in the challenging
task of human action re... | computer science |
9,854 | kLog: A Language for Logical and Relational Learning with Kernels | cs.AI | We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems d... | computer science |
9,855 | Online Structured Prediction via Coactive Learning | cs.LG | We propose Coactive Learning as a model of interaction between a learning
system and a human user, where both have the common goal of providing results
of maximum utility to the user. At each step, the system (e.g. search engine)
receives a context (e.g. query) and predicts an object (e.g. ranking). The user
responds b... | computer science |
9,856 | Diagnosing client faults using SVM-based intelligent inference from TCP
packet traces | cs.NI | We present the Intelligent Automated Client Diagnostic (IACD) system, which
only relies on inference from Transmission Control Protocol (TCP) packet traces
for rapid diagnosis of client device problems that cause network performance
issues. Using soft-margin Support Vector Machine (SVM) classifiers, the system
(i) dist... | computer science |
9,857 | On When and How to use SAT to Mine Frequent Itemsets | cs.AI | A new stream of research was born in the last decade with the goal of mining
itemsets of interest using Constraint Programming (CP). This has promoted a
natural way to combine complex constraints in a highly flexible manner.
Although CP state-of-the-art solutions formulate the task using Boolean
variables, the few atte... | computer science |
9,858 | GURLS: a Least Squares Library for Supervised Learning | cs.LG | We present GURLS, a least squares, modular, easy-to-extend software library
for efficient supervised learning. GURLS is targeted to machine learning
practitioners, as well as non-specialists. It offers a number state-of-the-art
training strategies for medium and large-scale learning, and routines for
efficient model se... | computer science |
9,859 | BayesOpt: A Library for Bayesian optimization with Robotics Applications | cs.RO | The purpose of this paper is twofold. On one side, we present a general
framework for Bayesian optimization and we compare it with some related fields
in active learning and Bayesian numerical analysis. On the other hand, Bayesian
optimization and related problems (bandits, sequential experimental design) are
highly de... | computer science |
9,860 | On the Robustness of Temporal Properties for Stochastic Models | cs.LO | Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic
Hybrid Automata (SHA) are powerful formalisms to model and to reason about the
dynamics of biological systems, due to their ability to capture the
stochasticity inherent in biological processes. A classical question in formal
modelling with c... | computer science |
9,861 | Decision Trees for Function Evaluation - Simultaneous Optimization of
Worst and Expected Cost | cs.DS | In several applications of automatic diagnosis and active learning a central
problem is the evaluation of a discrete function by adaptively querying the
values of its variables until the values read uniquely determine the value of
the function. In general, the process of reading the value of a variable might
involve so... | computer science |
9,862 | Temporal-Difference Learning to Assist Human Decision Making during the
Control of an Artificial Limb | cs.AI | In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important ... | computer science |
9,863 | Treedy: A Heuristic for Counting and Sampling Subsets | cs.DS | Consider a collection of weighted subsets of a ground set N. Given a query
subset Q of N, how fast can one (1) find the weighted sum over all subsets of
Q, and (2) sample a subset of Q proportionally to the weights? We present a
tree-based greedy heuristic, Treedy, that for a given positive tolerance d
answers such cou... | computer science |
9,864 | Margins, Kernels and Non-linear Smoothed Perceptrons | cs.LG | We focus on the problem of finding a non-linear classification function that
lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of
view (finding a perfect separator when one exists) and the dual point of view
(giving a certificate of non-existence), with special focus on generalizations
of two... | computer science |
9,865 | Graph Partitioning via Parallel Submodular Approximation to Accelerate
Distributed Machine Learning | cs.DC | Distributed computing excels at processing large scale data, but the
communication cost for synchronizing the shared parameters may slow down the
overall performance. Fortunately, the interactions between parameter and data
in many problems are sparse, which admits efficient partition in order to
reduce the communicati... | computer science |
9,866 | Accessing accurate documents by mining auxiliary document information | cs.IR | Earlier techniques of text mining included algorithms like k-means, Naive
Bayes, SVM which classify and cluster the text document for mining relevant
information about the documents. The need for improving the mining techniques
has us searching for techniques using the available algorithms. This paper
proposes one tech... | computer science |
9,867 | Asymptotic Convergence in Online Learning with Unbounded Delays | cs.LG | We study the problem of predicting the results of computations that are too
expensive to run, via the observation of the results of smaller computations.
We model this as an online learning problem with delayed feedback, where the
length of the delay is unbounded, which we study mainly in a stochastic
setting. We show ... | computer science |
9,868 | Inductive Coherence | cs.AI | While probability theory is normally applied to external environments, there
has been some recent interest in probabilistic modeling of the outputs of
computations that are too expensive to run. Since mathematical logic is a
powerful tool for reasoning about computer programs, we consider this problem
from the perspect... | computer science |
9,869 | Benchmarking Deep Reinforcement Learning for Continuous Control | cs.LG | Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. H... | computer science |
9,870 | Predicting Contextual Sequences via Submodular Function Maximization | cs.AI | Sequence optimization, where the items in a list are ordered to maximize some
reward has many applications such as web advertisement placement, search, and
control libraries in robotics. Previous work in sequence optimization produces
a static ordering that does not take any features of the item or context of the
probl... | computer science |
9,871 | An efficient high-quality hierarchical clustering algorithm for
automatic inference of software architecture from the source code of a
software system | cs.AI | It is a high-quality algorithm for hierarchical clustering of large software
source code. This effectively allows to break the complexity of tens of
millions lines of source code, so that a human software engineer can comprehend
a software system at high level by means of looking at its architectural
diagram that is re... | computer science |
9,872 | Distributed Power Allocation with SINR Constraints Using Trial and Error
Learning | cs.GT | In this paper, we address the problem of global transmit power minimization
in a self-congiguring network where radio devices are subject to operate at a
minimum signal to interference plus noise ratio (SINR) level. We model the
network as a parallel Gaussian interference channel and we introduce a fully
decentralized ... | computer science |
9,873 | Dyna-Style Planning with Linear Function Approximation and Prioritized
Sweeping | cs.AI | We consider the problem of efficiently learning optimal control policies and
value functions over large state spaces in an online setting in which estimates
must be available after each interaction with the world. This paper develops an
explicitly model-based approach extending the Dyna architecture to linear
function ... | computer science |
9,874 | Active Learning for Matching Problems | cs.LG | Effective learning of user preferences is critical to easing user burden in
various types of matching problems. Equally important is active query selection
to further reduce the amount of preference information users must provide. We
address the problem of active learning of user preferences for matching
problems, intr... | computer science |
9,875 | Unachievable Region in Precision-Recall Space and Its Effect on
Empirical Evaluation | cs.LG | Precision-recall (PR) curves and the areas under them are widely used to
summarize machine learning results, especially for data sets exhibiting class
skew. They are often used analogously to ROC curves and the area under ROC
curves. It is known that PR curves vary as class skew changes. What was not
recognized before ... | computer science |
9,876 | A new parameter Learning Method for Bayesian Networks with Qualitative
Influences | cs.AI | We propose a new method for parameter learning in Bayesian networks with
qualitative influences. This method extends our previous work from networks of
binary variables to networks of discrete variables with ordered values. The
specified qualitative influences correspond to certain order restrictions on
the parameters ... | computer science |
9,877 | Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian
Networks and Maximal Ancestral Graphs | cs.AI | We consider the incorporation of causal knowledge about the presence or
absence of (possibly indirect) causal relations into a causal model. Such
causal relations correspond to directed paths in a causal model. This type of
knowledge naturally arises from experimental data, among others. Specifically,
we consider the f... | computer science |
9,878 | The AI&M Procedure for Learning from Incomplete Data | stat.ME | We investigate methods for parameter learning from incomplete data that is
not missing at random. Likelihood-based methods then require the optimization
of a profile likelihood that takes all possible missingness mechanisms into
account. Optimzing this profile likelihood poses two main difficulties:
multiple (local) ma... | computer science |
9,879 | Infinite Hidden Relational Models | cs.AI | In many cases it makes sense to model a relationship symmetrically, not
implying any particular directionality. Consider the classical example of a
recommendation system where the rating of an item by a user should
symmetrically be dependent on the attributes of both the user and the item. The
attributes of the (known)... | computer science |
9,880 | Monte Carlo Matrix Inversion Policy Evaluation | cs.LG | In 1950, Forsythe and Leibler (1950) introduced a statistical technique for
finding the inverse of a matrix by characterizing the elements of the matrix
inverse as expected values of a sequence of random walks. Barto and Duff (1994)
subsequently showed relations between this technique and standard dynamic
programming a... | computer science |
9,881 | Machine Learning in Proof General: Interfacing Interfaces | cs.AI | We present ML4PG - a machine learning extension for Proof General. It allows
users to gather proof statistics related to shapes of goals, sequences of
applied tactics, and proof tree structures from the libraries of interactive
higher-order proofs written in Coq and SSReflect. The gathered data is
clustered using the s... | computer science |
9,882 | Fast Damage Recovery in Robotics with the T-Resilience Algorithm | cs.RO | Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algo... | computer science |
9,883 | Multi-Robot Informative Path Planning for Active Sensing of
Environmental Phenomena: A Tale of Two Algorithms | cs.LG | A key problem of robotic environmental sensing and monitoring is that of
active sensing: How can a team of robots plan the most informative observation
paths to minimize the uncertainty in modeling and predicting an environmental
phenomenon? This paper presents two principled approaches to efficient
information-theoret... | computer science |
9,884 | Information-Theoretic Approach to Efficient Adaptive Path Planning for
Mobile Robotic Environmental Sensing | cs.LG | Recent research in robot exploration and mapping has focused on sampling
environmental hotspot fields. This exploration task is formalized by Low,
Dolan, and Khosla (2008) in a sequential decision-theoretic planning under
uncertainty framework called MASP. The time complexity of solving MASP
approximately depends on th... | computer science |
9,885 | Optimal Demand Response Using Device Based Reinforcement Learning | cs.LG | Demand response (DR) for residential and small commercial buildings is
estimated to account for as much as 65% of the total energy savings potential
of DR, and previous work shows that a fully automated Energy Management System
(EMS) is a necessary prerequisite to DR in these areas. In this paper, we
propose a novel EM... | computer science |
9,886 | A Supervised Goal Directed Algorithm in Economical Choice Behaviour: An
Actor-Critic Approach | cs.GT | This paper aims to find an algorithmic structure that affords to predict and
explain economical choice behaviour particularly under uncertainty(random
policies) by manipulating the prevalent Actor-Critic learning method to comply
with the requirements we have been entrusted ever since the field of
neuroeconomics dawned... | computer science |
9,887 | Machine Learner for Automated Reasoning 0.4 and 0.5 | cs.LG | Machine Learner for Automated Reasoning (MaLARea) is a learning and reasoning
system for proving in large formal libraries where thousands of theorems are
available when attacking a new conjecture, and a large number of related
problems and proofs can be used to learn specific theorem-proving knowledge.
The last versio... | computer science |
9,888 | Learning-assisted Theorem Proving with Millions of Lemmas | 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 |
9,889 | Network-based Isoform Quantification with RNA-Seq Data for Cancer
Transcriptome Analysis | cs.CE | High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript
quantification of gene isoforms. Since RNA-Seq data alone is often not
sufficient to accurately identify the read origins from the isoforms for
quantification, we propose to explore protein domain-domain interactions as
prior knowledge for integrat... | computer science |
9,890 | Multi-agent Inverse Reinforcement Learning for Zero-sum Games | cs.GT | In this paper we introduce a Bayesian framework for solving a class of
problems termed Multi-agent Inverse Reinforcement Learning (MIRL). Compared to
the well-known Inverse Reinforcement Learning (IRL) problem, MIRL is formalized
in the context of a stochastic game rather than a Markov decision process
(MDP). Games bri... | computer science |
9,891 | A Mathematical Theory of Learning | cs.LG | In this paper, a mathematical theory of learning is proposed that has many
parallels with information theory. We consider Vapnik's General Setting of
Learning in which the learning process is defined to be the act of selecting a
hypothesis in response to a given training set. Such hypothesis can, for
example, be a deci... | computer science |
9,892 | Efficient Model Learning for Human-Robot Collaborative Tasks | cs.RO | We present a framework for learning human user models from joint-action
demonstrations that enables the robot to compute a robust policy for a
collaborative task with a human. The learning takes place completely
automatically, without any human intervention. First, we describe the
clustering of demonstrated action sequ... | computer science |
9,893 | A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian
Networks | cs.AI | Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
$O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the
Bayesian netw... | computer science |
9,894 | Using Learned Predictions as Feedback to Improve Control and
Communication with an Artificial Limb: Preliminary Findings | cs.AI | Many people suffer from the loss of a limb. Learning to get by without an arm
or hand can be very challenging, and existing prostheses do not yet fulfil the
needs of individuals with amputations. One promising solution is to provide
greater communication between a prosthesis and its user. Towards this end, we
present a... | computer science |
9,895 | The Power of Randomization: Distributed Submodular Maximization on
Massive Datasets | cs.LG | A wide variety of problems in machine learning, including exemplar
clustering, document summarization, and sensor placement, can be cast as
constrained submodular maximization problems. Unfortunately, the resulting
submodular optimization problems are often too large to be solved on a single
machine. We develop a simpl... | computer science |
9,896 | Joint Optimization of Masks and Deep Recurrent Neural Networks for
Monaural Source Separation | cs.SD | Monaural source separation is important for many real world applications. It
is challenging because, with only a single channel of information available,
without any constraints, an infinite number of solutions are possible. In this
paper, we explore joint optimization of masking functions and deep recurrent
neural net... | computer science |
9,897 | The Linearization of Belief Propagation on Pairwise Markov Networks | cs.AI | Belief Propagation (BP) is a widely used approximation for exact
probabilistic inference in graphical models, such as Markov Random Fields
(MRFs). In graphs with cycles, however, no exact convergence guarantees for BP
are known, in general. For the case when all edges in the MRF carry the same
symmetric, doubly stochas... | computer science |
9,898 | Approval Voting and Incentives in Crowdsourcing | cs.GT | The growing need for labeled training data has made crowdsourcing an
important part of machine learning. The quality of crowdsourced labels is,
however, adversely affected by three factors: (1) the workers are not experts;
(2) the incentives of the workers are not aligned with those of the requesters;
and (3) the inter... | computer science |
9,899 | Universal Memory Architectures for Autonomous Machines | cs.AI | We propose a self-organizing memory architecture for perceptual experience,
capable of supporting autonomous learning and goal-directed problem solving in
the absence of any prior information about the agent's environment. The
architecture is simple enough to ensure (1) a quadratic bound (in the number of
available sen... | computer science |
9,900 | 23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and
Management | cs.DB | The global influence of Big Data is not only growing but seemingly endless.
The trend is leaning towards knowledge that is attained easily and quickly from
massive pools of Big Data. Today we are living in the technological world that
Dr. Usama Fayyad and his distinguished research fellows discussed in the
introductory... | computer science |
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