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4,600 | Belief Optimization for Binary Networks: A Stable Alternative to Loopy
Belief Propagation | cs.AI | We present a novel inference algorithm for arbitrary, binary, undirected
graphs. Unlike loopy belief propagation, which iterates fixed point equations,
we directly descend on the Bethe free energy. The algorithm consists of two
phases, first we update the pairwise probabilities, given the marginal
probabilities at each... | computer science |
4,601 | Planning by Prioritized Sweeping with Small Backups | cs.AI | Efficient planning plays a crucial role in model-based reinforcement
learning. Traditionally, the main planning operation is a full backup based on
the current estimates of the successor states. Consequently, its computation
time is proportional to the number of successor states. In this paper, we
introduce a new plann... | computer science |
4,602 | The IBMAP approach for Markov networks structure learning | cs.AI | In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorit... | computer science |
4,603 | Utilities as Random Variables: Density Estimation and Structure
Discovery | cs.AI | Decision theory does not traditionally include uncertainty over utility
functions. We argue that the a person's utility value for a given outcome can
be treated as we treat other domain attributes: as a random variable with a
density function over its possible values. We show that we can apply
statistical density estim... | computer science |
4,604 | Inference for Belief Networks Using Coupling From the Past | cs.AI | Inference for belief networks using Gibbs sampling produces a distribution
for unobserved variables that differs from the correct distribution by a
(usually) unknown error, since convergence to the right distribution occurs
only asymptotically. The method of "coupling from the past" samples from
exactly the correct dis... | computer science |
4,605 | PEGASUS: A Policy Search Method for Large MDPs and POMDPs | cs.AI | We propose a new approach to the problem of searching a space of policies for
a Markov decision process (MDP) or a partially observable Markov decision
process (POMDP), given a model. Our approach is based on the following
observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which
all state transit... | computer science |
4,606 | Discovering the Hidden Structure of Complex Dynamic Systems | cs.AI | Dynamic Bayesian networks provide a compact and natural representation for
complex dynamic systems. However, in many cases, there is no expert available
from whom a model can be elicited. Learning provides an alternative approach
for constructing models of dynamic systems. In this paper, we address some of
the crucial ... | computer science |
4,607 | Learning Polytrees | cs.AI | We consider the task of learning the maximum-likelihood polytree from data.
Our first result is a performance guarantee establishing that the optimal
branching (or Chow-Liu tree), which can be computed very easily, constitutes a
good approximation to the best polytree. We then show that it is not possible
to do very mu... | computer science |
4,608 | Model-Based Bayesian Exploration | cs.AI | Reinforcement learning systems are often concerned with balancing exploration
of untested actions against exploitation of actions that are known to be good.
The benefit of exploration can be estimated using the classical notion of Value
of Information - the expected improvement in future decision quality arising
from t... | computer science |
4,609 | Loopy Belief Propagation for Approximate Inference: An Empirical Study | cs.AI | Recently, researchers have demonstrated that loopy belief propagation - the
use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error-
correcting codes.The most dramatic instance OF this IS the near Shannon - limit
performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO
loopy belief ... | computer science |
4,610 | Learning Bayesian Networks from Incomplete Data with Stochastic Search
Algorithms | cs.AI | This paper describes stochastic search approaches, including a new stochastic
algorithm and an adaptive mutation operator, for learning Bayesian networks
from incomplete data. This problem is characterized by a huge solution space
with a highly multimodal landscape. State-of-the-art approaches all involve
using determi... | computer science |
4,611 | Learning the Structure of Dynamic Probabilistic Networks | cs.AI | Dynamic probabilistic networks are a compact representation of complex
stochastic processes. In this paper we examine how to learn the structure of a
DPN from data. We extend structure scoring rules for standard probabilistic
networks to the dynamic case, and show how to search for structure when some of
the variables ... | computer science |
4,612 | A Multivariate Discretization Method for Learning Bayesian Networks from
Mixed Data | cs.AI | In this paper we address the problem of discretization in the context of
learning Bayesian networks (BNs) from data containing both continuous and
discrete variables. We describe a new technique for <EM>multivariate</EM>
discretization, whereby each continuous variable is discretized while taking
into account its inter... | computer science |
4,613 | The Stochastic Gradient Descent for the Primal L1-SVM Optimization
Revisited | cs.LG | We reconsider the stochastic (sub)gradient approach to the unconstrained
primal L1-SVM optimization. We observe that if the learning rate is inversely
proportional to the number of steps, i.e., the number of times any training
pattern is presented to the algorithm, the update rule may be transformed into
the one of the... | computer science |
4,614 | On the Performance Bounds of some Policy Search Dynamic Programming
Algorithms | cs.AI | We consider the infinite-horizon discounted optimal control problem
formalized by Markov Decision Processes. We focus on Policy Search algorithms,
that compute an approximately optimal policy by following the standard Policy
Iteration (PI) scheme via an -approximate greedy operator (Kakade and Langford,
2002; Lazaric e... | computer science |
4,615 | Markov random fields factorization with context-specific independences | cs.AI | Markov random fields provide a compact representation of joint probability
distributions by representing its independence properties in an undirected
graph. The well-known Hammersley-Clifford theorem uses these conditional
independences to factorize a Gibbs distribution into a set of factors. However,
an important issu... | computer science |
4,616 | Solving Relational MDPs with Exogenous Events and Additive Rewards | cs.AI | We formalize a simple but natural subclass of service domains for relational
planning problems with object-centered, independent exogenous events and
additive rewards capturing, for example, problems in inventory control.
Focusing on this subclass, we present a new symbolic planning algorithm which
is the first algorit... | computer science |
4,617 | Evaluation Measures for Hierarchical Classification: a unified view and
novel approaches | cs.AI | Hierarchical classification addresses the problem of classifying items into a
hierarchy of classes. An important issue in hierarchical classification is the
evaluation of different classification algorithms, which is complicated by the
hierarchical relations among the classes. Several evaluation measures have been
prop... | computer science |
4,618 | A Minimum Relative Entropy Principle for Learning and Acting | cs.AI | This paper proposes a method to construct an adaptive agent that is universal
with respect to a given class of experts, where each expert is an agent that
has been designed specifically for a particular environment. This adaptive
control problem is formalized as the problem of minimizing the relative entropy
of the ada... | computer science |
4,619 | Temporal Difference Updating without a Learning Rate | cs.LG | We derive an equation for temporal difference learning from statistical
principles. Specifically, we start with the variational principle and then
bootstrap to produce an updating rule for discounted state value estimates. The
resulting equation is similar to the standard equation for temporal difference
learning with ... | computer science |
4,620 | A Convergent Online Single Time Scale Actor Critic Algorithm | cs.LG | Actor-Critic based approaches were among the first to address reinforcement
learning in a general setting. Recently, these algorithms have gained renewed
interest due to their generality, good convergence properties, and possible
biological relevance. In this paper, we introduce an online temporal difference
based acto... | computer science |
4,621 | Exploiting Unlabeled Data to Enhance Ensemble Diversity | cs.LG | Ensemble learning aims to improve generalization ability by using multiple
base learners. It is well-known that to construct a good ensemble, the base
learners should be accurate as well as diverse. In this paper, unlabeled data
is exploited to facilitate ensemble learning by helping augment the diversity
among the bas... | computer science |
4,622 | Lifted Graphical Models: A Survey | cs.AI | This article presents a survey of work on lifted graphical models. We review
a general form for a lifted graphical model, a par-factor graph, and show how a
number of existing statistical relational representations map to this
formalism. We discuss inference algorithms, including lifted inference
algorithms, that effic... | computer science |
4,623 | Submodular Optimization for Efficient Semi-supervised Support Vector
Machines | cs.LG | In this work we present a quadratic programming approximation of the
Semi-Supervised Support Vector Machine (S3VM) problem, namely approximate
QP-S3VM, that can be efficiently solved using off the shelf optimization
packages. We prove that this approximate formulation establishes a relation
between the low density sepa... | computer science |
4,624 | Asymptotically Optimal Agents | cs.AI | Artificial general intelligence aims to create agents capable of learning to
solve arbitrary interesting problems. We define two versions of asymptotic
optimality and prove that no agent can satisfy the strong version while in some
cases, depending on discounting, there does exist a non-computable weak
asymptotically o... | computer science |
4,625 | A Time and Space Efficient Junction Tree Architecture | cs.AI | The junction tree algorithm is a way of computing marginals of boolean
multivariate probability distributions that factorise over sets of random
variables. The junction tree algorithm first constructs a tree called a
junction tree who's vertices are sets of random variables. The algorithm then
performs a generalised ve... | computer science |
4,626 | An Enhanced Features Extractor for a Portfolio of Constraint Solvers | cs.AI | Recent research has shown that a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. The solver selection is usually done by means of (un)supervised
learning techniques which exploit features extracted from the problem
specification. In this paper ... | computer science |
4,627 | Design and Development of an Expert System to Help Head of University
Departments | cs.AI | One of the basic tasks which is responded for head of each university
department, is employing lecturers based on some default factors such as
experience, evidences, qualifies and etc. In this respect, to help the heads,
some automatic systems have been proposed until now using machine learning
methods, decision suppor... | computer science |
4,628 | Hidden Parameter Markov Decision Processes: A Semiparametric Regression
Approach for Discovering Latent Task Parametrizations | cs.LG | Control applications often feature tasks with similar, but not identical,
dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),
a framework that parametrizes a family of related dynamical systems with a
low-dimensional set of latent factors, and introduce a semiparametric
regression approach fo... | computer science |
4,629 | Low-complexity modular policies: learning to play Pac-Man and a new
framework beyond MDPs | cs.LG | In this paper we propose a method that learns to play Pac-Man. We define a
set of high-level observation and action modules. Actions are temporally
extended, and multiple action modules may be in effect concurrently. A decision
of the agent is represented as a rule-based policy. For learning, we apply the
cross-entropy... | computer science |
4,630 | Robustness and Regularization of Support Vector Machines | cs.LG | We consider regularized support vector machines (SVMs) and show that they are
precisely equivalent to a new robust optimization formulation. We show that
this equivalence of robust optimization and regularization has implications for
both algorithms, and analysis. In terms of algorithms, the equivalence suggests
more g... | computer science |
4,631 | The Offset Tree for Learning with Partial Labels | cs.LG | We present an algorithm, called the Offset Tree, for learning to make
decisions in situations where the payoff of only one choice is observed, rather
than all choices. The algorithm reduces this setting to binary classification,
allowing one to reuse of any existing, fully supervised binary classification
algorithm in ... | computer science |
4,632 | Client-server multi-task learning from distributed datasets | cs.LG | A client-server architecture to simultaneously solve multiple learning tasks
from distributed datasets is described. In such architecture, each client is
associated with an individual learning task and the associated dataset of
examples. The goal of the architecture is to perform information fusion from
multiple datase... | computer science |
4,633 | A parameter-free hedging algorithm | cs.LG | We study the problem of decision-theoretic online learning (DTOL). Motivated
by practical applications, we focus on DTOL when the number of actions is very
large. Previous algorithms for learning in this framework have a tunable
learning rate parameter, and a barrier to using online-learning in practical
applications i... | computer science |
4,634 | Optimistic Simulated Exploration as an Incentive for Real Exploration | cs.LG | Many reinforcement learning exploration techniques are overly optimistic and
try to explore every state. Such exploration is impossible in environments with
the unlimited number of states. I propose to use simulated exploration with an
optimistic model to discover promising paths for real exploration. This reduces
the ... | computer science |
4,635 | Conditional Probability Tree Estimation Analysis and Algorithms | cs.LG | We consider the problem of estimating the conditional probability of a label
in time $O(\log n)$, where $n$ is the number of possible labels. We analyze a
natural reduction of this problem to a set of binary regression problems
organized in a tree structure, proving a regret bound that scales with the
depth of the tree... | computer science |
4,636 | A Minimum Description Length Approach to Multitask Feature Selection | cs.LG | Many regression problems involve not one but several response variables
(y's). Often the responses are suspected to share a common underlying
structure, in which case it may be advantageous to share information across
them; this is known as multitask learning. As a special case, we can use
multiple responses to better ... | computer science |
4,637 | Large-Margin kNN Classification Using a Deep Encoder Network | cs.LG | KNN is one of the most popular classification methods, but it often fails to
work well with inappropriate choice of distance metric or due to the presence
of numerous class-irrelevant features. Linear feature transformation methods
have been widely applied to extract class-relevant information to improve kNN
classifica... | computer science |
4,638 | Reduced-Rank Hidden Markov Models | cs.LG | We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization
of HMMs that can model smooth state evolution as in Linear Dynamical Systems
(LDSs) as well as non-log-concave predictive distributions as in
continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n
discrete observations... | computer science |
4,639 | Kernel machines with two layers and multiple kernel learning | cs.LG | In this paper, the framework of kernel machines with two layers is
introduced, generalizing classical kernel methods. The new learning methodology
provide a formal connection between computational architectures with multiple
layers and the theme of kernel learning in standard regularization methods.
First, a represente... | computer science |
4,640 | On the Foundations of Adversarial Single-Class Classification | cs.LG | Motivated by authentication, intrusion and spam detection applications we
consider single-class classification (SCC) as a two-person game between the
learner and an adversary. In this game the learner has a sample from a target
distribution and the goal is to construct a classifier capable of
distinguishing observation... | computer science |
4,641 | The Local Optimality of Reinforcement Learning by Value Gradients, and
its Relationship to Policy Gradient Learning | cs.LG | In this theoretical paper we are concerned with the problem of learning a
value function by a smooth general function approximator, to solve a
deterministic episodic control problem in a large continuous state space. It is
shown that learning the gradient of the value-function at every point along a
trajectory generate... | computer science |
4,642 | Review and Evaluation of Feature Selection Algorithms in Synthetic
Problems | cs.AI | The main purpose of Feature Subset Selection is to find a reduced subset of
attributes from a data set described by a feature set. The task of a feature
selection algorithm (FSA) is to provide with a computational solution motivated
by a certain definition of relevance or by a reliable evaluation measure. In
this paper... | computer science |
4,643 | Understanding Exhaustive Pattern Learning | cs.AI | Pattern learning in an important problem in Natural Language Processing
(NLP). Some exhaustive pattern learning (EPL) methods (Bod, 1992) were proved
to be flawed (Johnson, 2002), while similar algorithms (Och and Ney, 2004)
showed great advantages on other tasks, such as machine translation. In this
article, we first ... | computer science |
4,644 | Learning Undirected Graphical Models with Structure Penalty | cs.AI | In undirected graphical models, learning the graph structure and learning the
functions that relate the predictive variables (features) to the responses
given the structure are two topics that have been widely investigated in
machine learning and statistics. Learning graphical models in two stages will
have problems be... | computer science |
4,645 | Mean-Variance Optimization in Markov Decision Processes | cs.LG | We consider finite horizon Markov decision processes under performance
measures that involve both the mean and the variance of the cumulative reward.
We show that either randomized or history-based policies can improve
performance. We prove that the complexity of computing a policy that maximizes
the mean reward under ... | computer science |
4,646 | Optimistic Agents are Asymptotically Optimal | cs.AI | We use optimism to introduce generic asymptotically optimal reinforcement
learning agents. They achieve, with an arbitrary finite or compact class of
environments, asymptotically optimal behavior. Furthermore, in the finite
deterministic case we provide finite error bounds. | computer science |
4,647 | Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in
Meta-Mining | cs.LG | The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to ... | computer science |
4,648 | Multi-view constrained clustering with an incomplete mapping between
views | cs.LG | Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propos... | computer science |
4,649 | Crowdsourcing Control: Moving Beyond Multiple Choice | cs.AI | To ensure quality results from crowdsourced tasks, requesters often aggregate
worker responses and use one of a plethora of strategies to infer the correct
answer from the set of noisy responses. However, all current models assume
prior knowledge of all possible outcomes of the task. While not an unreasonable
assumptio... | computer science |
4,650 | Learning in the Model Space for Fault Diagnosis | cs.LG | The emergence of large scaled sensor networks facilitates the collection of
large amounts of real-time data to monitor and control complex engineering
systems. However, in many cases the collected data may be incomplete or
inconsistent, while the underlying environment may be time-varying or
un-formulated. In this pape... | computer science |
4,651 | First Experiments with PowerPlay | cs.AI | Like a scientist or a playing child, PowerPlay not only learns new skills to
solve given problems, but also invents new interesting problems by itself. By
design, it continually comes up with the fastest to find, initially novel, but
eventually solvable tasks. It also continually simplifies or compresses or
speeds up s... | computer science |
4,652 | Multi-objective Reinforcement Learning with Continuous Pareto Frontier
Approximation Supplementary Material | cs.AI | This document contains supplementary material for the paper "Multi-objective
Reinforcement Learning with Continuous Pareto Frontier Approximation",
published at the Twenty-Ninth AAAI Conference on Artificial Intelligence
(AAAI-15). The paper is about learning a continuous approximation of the Pareto
frontier in Multi-O... | computer science |
4,653 | Exhaustive and Efficient Constraint Propagation: A Semi-Supervised
Learning Perspective and Its Applications | cs.AI | This paper presents a novel pairwise constraint propagation approach by
decomposing the challenging constraint propagation problem into a set of
independent semi-supervised learning subproblems which can be solved in
quadratic time using label propagation based on k-nearest neighbor graphs.
Considering that this time c... | computer science |
4,654 | Exploring compact reinforcement-learning representations with linear
regression | cs.LG | This paper presents a new algorithm for online linear regression whose
efficiency guarantees satisfy the requirements of the KWIK (Knows What It
Knows) framework. The algorithm improves on the complexity bounds of the
current state-of-the-art procedure in this setting. We explore several
applications of this algorithm ... | computer science |
4,655 | Convexifying the Bethe Free Energy | cs.AI | The introduction of loopy belief propagation (LBP) revitalized the
application of graphical models in many domains. Many recent works present
improvements on the basic LBP algorithm in an attempt to overcome convergence
and local optima problems. Notable among these are convexified free energy
approximations that lead ... | computer science |
4,656 | Convergent message passing algorithms - a unifying view | cs.AI | Message-passing algorithms have emerged as powerful techniques for
approximate inference in graphical models. When these algorithms converge, they
can be shown to find local (or sometimes even global) optima of variational
formulations to the inference problem. But many of the most popular algorithms
are not guaranteed... | computer science |
4,657 | Language-Constraint Reachability Learning in Probabilistic Graphs | cs.AI | The probabilistic graphs framework models the uncertainty inherent in
real-world domains by means of probabilistic edges whose value quantifies the
likelihood of the edge existence or the strength of the link it represents. The
goal of this paper is to provide a learning method to compute the most likely
relationship b... | computer science |
4,658 | A Conditional Random Field for Discriminatively-trained Finite-state
String Edit Distance | cs.LG | The need to measure sequence similarity arises in information extraction,
object identity, data mining, biological sequence analysis, and other domains.
This paper presents discriminative string-edit CRFs, a finitestate conditional
random field model for edit sequences between strings. Conditional random
fields have ad... | computer science |
4,659 | On the Choice of Regions for Generalized Belief Propagation | cs.AI | Generalized belief propagation (GBP) has proven to be a promising technique
for approximate inference tasks in AI and machine learning. However, the choice
of a good set of clusters to be used in GBP has remained more of an art then a
science until this day. This paper proposes a sequential approach to adding new
clust... | computer science |
4,660 | Predictive State Representations: A New Theory for Modeling Dynamical
Systems | cs.AI | Modeling dynamical systems, both for control purposes and to make predictions
about their behavior, is ubiquitous in science and engineering. Predictive
state representations (PSRs) are a recently introduced class of models for
discrete-time dynamical systems. The key idea behind PSRs and the closely
related OOMs (Jaeg... | computer science |
4,661 | MCTS Based on Simple Regret | cs.AI | UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games
and Markov decision processes, is based on UCB, a sampling policy for the
Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However,
search differs from MAB in that in MCTS it is usually only the final "arm pull"
(the act... | computer science |
4,662 | VOI-aware MCTS | cs.AI | UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games
and Markov decision processes, is based on UCB1, a sampling policy for the
Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However,
search differs from MAB in that in MCTS it is usually only the final "arm pull"
(the ac... | computer science |
4,663 | Structure Learning of Probabilistic Logic Programs by Searching the
Clause Space | cs.LG | Learning probabilistic logic programming languages is receiving an increasing
attention and systems are available for learning the parameters (PRISM,
LeProbLog, LFI-ProbLog and EMBLEM) or both the structure and the parameters
(SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the
algorithm SLIPCOV... | computer science |
4,664 | Optimal Time-Series Motifs | cs.AI | Motifs are the most repetitive/frequent patterns of a time-series. The
discovery of motifs is crucial for practitioners in order to understand and
interpret the phenomena occurring in sequential data. Currently, motifs are
searched among series sub-sequences, aiming at selecting the most frequently
occurring ones. Sear... | computer science |
4,665 | The Configurable SAT Solver Challenge (CSSC) | cs.AI | It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SA... | computer science |
4,666 | Fast Differentially Private Matrix Factorization | cs.LG | Differentially private collaborative filtering is a challenging task, both in
terms of accuracy and speed. We present a simple algorithm that is provably
differentially private, while offering good performance, using a novel
connection of differential privacy to Bayesian posterior sampling via
Stochastic Gradient Lange... | computer science |
4,667 | Fuzzy Least Squares Twin Support Vector Machines | cs.AI | Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient
and fast version of SVM algorithm for binary classification. LSTSVM combines
the idea of Least Squares SVM and Twin SVM in which two non-parallel
hyperplanes are found by solving two systems of linear equations. Although the
algorithm is very ... | computer science |
4,668 | On the relation between accuracy and fairness in binary classification | cs.LG | Our study revisits the problem of accuracy-fairness tradeoff in binary
classification. We argue that comparison of non-discriminatory classifiers
needs to account for different rates of positive predictions, otherwise
conclusions about performance may be misleading, because accuracy and
discrimination of naive baseline... | computer science |
4,669 | Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning | cs.LG | In this paper we present a new way of predicting the performance of a
reinforcement learning policy given historical data that may have been
generated by a different policy. The ability to evaluate a policy from
historical data is important for applications where the deployment of a bad
policy can be dangerous or costl... | computer science |
4,670 | Bounded Optimal Exploration in MDP | cs.AI | Within the framework of probably approximately correct Markov decision
processes (PAC-MDP), much theoretical work has focused on methods to attain
near optimality after a relatively long period of learning and exploration.
However, practical concerns require the attainment of satisfactory behavior
within a short period... | computer science |
4,671 | Back to the Basics: Bayesian extensions of IRT outperform neural
networks for proficiency estimation | cs.AI | Estimating student proficiency is an important task for computer based
learning systems. We compare a family of IRT-based proficiency estimation
methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural
network model with promising initial results. We evaluate how well each model
predicts a student'... | computer science |
4,672 | Symbolic Knowledge Extraction using Łukasiewicz Logics | cs.AI | This work describes a methodology that combines logic-based systems and
connectionist systems. Our approach uses finite truth-valued {\L}ukasiewicz
logic, wherein every connective can be defined by a neuron in an artificial
network. This allowed the injection of first-order formulas into a network
architecture, and als... | computer science |
4,673 | Efficient Classification of Multi-Labelled Text Streams by Clashing | cs.AI | We present a method for the classification of multi-labelled text documents
explicitly designed for data stream applications that require to process a
virtually infinite sequence of data using constant memory and constant
processing time. Our method is composed of an online procedure used to
efficiently map text into a... | computer science |
4,674 | Empirical study of PROXTONE and PROXTONE$^+$ for Fast Learning of Large
Scale Sparse Models | cs.LG | PROXTONE is a novel and fast method for optimization of large scale
non-smooth convex problem \cite{shi2015large}. In this work, we try to use
PROXTONE method in solving large scale \emph{non-smooth non-convex} problems,
for example training of sparse deep neural network (sparse DNN) or sparse
convolutional neural netw... | computer science |
4,675 | Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage
Weight Promotion, Redundant Encoding and Carousel Shaping | cs.AI | 2048 is an engaging single-player, nondeterministic video puzzle game, which,
thanks to the simple rules and hard-to-master gameplay, has gained massive
popularity in recent years. As 2048 can be conveniently embedded into the
discrete-state Markov decision processes framework, we treat it as a testbed
for evaluating e... | computer science |
4,676 | Sketching and Neural Networks | cs.LG | High-dimensional sparse data present computational and statistical challenges
for supervised learning. We propose compact linear sketches for reducing the
dimensionality of the input, followed by a single layer neural network. We show
that any sparse polynomial function can be computed, on nearly all sparse
binary vect... | computer science |
4,677 | Latent Contextual Bandits and their Application to Personalized
Recommendations for New Users | cs.LG | Personalized recommendations for new users, also known as the cold-start
problem, can be formulated as a contextual bandit problem. Existing contextual
bandit algorithms generally rely on features alone to capture user variability.
Such methods are inefficient in learning new users' interests. In this paper we
propose ... | computer science |
4,678 | A Computational Model for Situated Task Learning with Interactive
Instruction | cs.AI | Learning novel tasks is a complex cognitive activity requiring the learner to
acquire diverse declarative and procedural knowledge. Prior ACT-R models of
acquiring task knowledge from instruction focused on learning procedural
knowledge from declarative instructions encoded in semantic memory. In this
paper, we identif... | computer science |
4,679 | A Deep Hierarchical Approach to Lifelong Learning in Minecraft | cs.AI | We propose a lifelong learning system that has the ability to reuse and
transfer knowledge from one task to another while efficiently retaining the
previously learned knowledge-base. Knowledge is transferred by learning
reusable skills to solve tasks in Minecraft, a popular video game which is an
unsolved and high-dime... | computer science |
4,680 | On the representation and embedding of knowledge bases beyond binary
relations | cs.LG | The models developed to date for knowledge base embedding are all based on
the assumption that the relations contained in knowledge bases are binary. For
the training and testing of these embedding models, multi-fold (or n-ary)
relational data are converted to triples (e.g., in FB15K dataset) and
interpreted as instanc... | computer science |
4,681 | Building an interpretable fuzzy rule base from data using Orthogonal
Least Squares Application to a depollution problem | cs.LG | In many fields where human understanding plays a crucial role, such as
bioprocesses, the capacity of extracting knowledge from data is of critical
importance. Within this framework, fuzzy learning methods, if properly used,
can greatly help human experts. Amongst these methods, the aim of orthogonal
transformations, wh... | computer science |
4,682 | Multi-Instance Multi-Label Learning | cs.LG | In this paper, we propose the MIML (Multi-Instance Multi-Label learning)
framework where an example is described by multiple instances and associated
with multiple class labels. Compared to traditional learning frameworks, the
MIML framework is more convenient and natural for representing complicated
objects which have... | computer science |
4,683 | Hybrid Batch Bayesian Optimization | cs.AI | Bayesian Optimization aims at optimizing an unknown non-convex/concave
function that is costly to evaluate. We are interested in application scenarios
where concurrent function evaluations are possible. Under such a setting, BO
could choose to either sequentially evaluate the function, one input at a time
and wait for ... | computer science |
4,684 | A Mixed Observability Markov Decision Process Model for Musical Pitch | cs.AI | Partially observable Markov decision processes have been widely used to
provide models for real-world decision making problems. In this paper, we will
provide a method in which a slightly different version of them called Mixed
observability Markov decision process, MOMDP, is going to join with our
problem. Basically, w... | computer science |
4,685 | Simple Regret Optimization in Online Planning for Markov Decision
Processes | cs.AI | We consider online planning in Markov decision processes (MDPs). In online
planning, the agent focuses on its current state only, deliberates about the
set of possible policies from that state onwards and, when interrupted, uses
the outcome of that exploratory deliberation to choose what action to perform
next. The per... | computer science |
4,686 | Learning the Experts for Online Sequence Prediction | cs.LG | Online sequence prediction is the problem of predicting the next element of a
sequence given previous elements. This problem has been extensively studied in
the context of individual sequence prediction, where no prior assumptions are
made on the origin of the sequence. Individual sequence prediction algorithms
work qu... | computer science |
4,687 | Adaptive Regularization for Weight Matrices | cs.LG | Algorithms for learning distributions over weight-vectors, such as AROW were
recently shown empirically to achieve state-of-the-art performance at various
problems, with strong theoretical guaranties. Extending these algorithms to
matrix models pose challenges since the number of free parameters in the
covariance of th... | computer science |
4,688 | The Most Persistent Soft-Clique in a Set of Sampled Graphs | cs.LG | When searching for characteristic subpatterns in potentially noisy graph
data, it appears self-evident that having multiple observations would be better
than having just one. However, it turns out that the inconsistencies introduced
when different graph instances have different edge sets pose a serious
challenge. In th... | computer science |
4,689 | Scaling Life-long Off-policy Learning | cs.AI | We pursue a life-long learning approach to artificial intelligence that makes
extensive use of reinforcement learning algorithms. We build on our prior work
with general value functions (GVFs) and the Horde architecture. GVFs have been
shown able to represent a wide variety of facts about the world's dynamics that
may ... | computer science |
4,690 | Compositional Planning Using Optimal Option Models | cs.AI | In this paper we introduce a framework for option model composition. Option
models are temporal abstractions that, like macro-operators in classical
planning, jump directly from a start state to an end state. Prior work has
focused on constructing option models from primitive actions, by intra-option
model learning; or... | computer science |
4,691 | An Empirical Comparison of Algorithms for Aggregating Expert Predictions | cs.AI | Predicting the outcomes of future events is a challenging problem for which a
variety of solution methods have been explored and attempted. We present an
empirical comparison of a variety of online and offline adaptive algorithms for
aggregating experts' predictions of the outcomes of five years of US National
Football... | computer science |
4,692 | Continuous Time Markov Networks | cs.AI | A central task in many applications is reasoning about processes that change
in a continuous time. The mathematical framework of Continuous Time Markov
Processes provides the basic foundations for modeling such systems. Recently,
Nodelman et al introduced continuous time Bayesian networks (CTBNs), which
allow a compact... | computer science |
4,693 | An Empirical Evaluation of Portfolios Approaches for solving CSPs | cs.AI | Recent research in areas such as SAT solving and Integer Linear Programming
has shown that the performances of a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. We report an empirical evaluation and comparison of portfolio
approaches applied to... | computer science |
4,694 | Bag-of-Words Representation for Biomedical Time Series Classification | cs.LG | Automatic analysis of biomedical time series such as electroencephalogram
(EEG) and electrocardiographic (ECG) signals has attracted great interest in
the community of biomedical engineering due to its important applications in
medicine. In this work, a simple yet effective bag-of-words representation that
is able to c... | computer science |
4,695 | Exploring Parallelism in Learning Belief Networks | cs.AI | It has been shown that a class of probabilistic domain models cannot be
learned correctly by several existing algorithms which employ a single-link
look ahead search. When a multi-link look ahead search is used, the
computational complexity of the learning algorithm increases. We study how to
use parallelism to tackle ... | computer science |
4,696 | Sequential Update of Bayesian Network Structure | cs.AI | There is an obvious need for improving the performance and accuracy of a
Bayesian network as new data is observed. Because of errors in model
construction and changes in the dynamics of the domains, we cannot afford to
ignore the information in new data. While sequential update of parameters for a
fixed structure can b... | computer science |
4,697 | Learning Bayesian Nets that Perform Well | cs.AI | A Bayesian net (BN) is more than a succinct way to encode a probabilistic
distribution; it also corresponds to a function used to answer queries. A BN
can therefore be evaluated by the accuracy of the answers it returns. Many
algorithms for learning BNs, however, attempt to optimize another criterion
(usually likelihoo... | computer science |
4,698 | Learning Belief Networks in Domains with Recursively Embedded Pseudo
Independent Submodels | cs.AI | A pseudo independent (PI) model is a probabilistic domain model (PDM) where
proper subsets of a set of collectively dependent variables display marginal
independence. PI models cannot be learned correctly by many algorithms that
rely on a single link search. Earlier work on learning PI models has suggested
a straightfo... | computer science |
4,699 | Structure and Parameter Learning for Causal Independence and Causal
Interaction Models | cs.AI | This paper discusses causal independence models and a generalization of these
models called causal interaction models. Causal interaction models are models
that have independent mechanisms where a mechanism can have several causes. In
addition to introducing several particular types of causal interaction models,
we sho... | computer science |
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