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9,300 | The Rate of Convergence of AdaBoost | math.OC | The AdaBoost algorithm was designed to combine many "weak" hypotheses that
perform slightly better than random guessing into a "strong" hypothesis that
has very low error. We study the rate at which AdaBoost iteratively converges
to the minimum of the "exponential loss." Unlike previous work, our proofs do
not require ... | computer science |
9,301 | Kernels for Vector-Valued Functions: a Review | stat.ML | Kernel methods are among the most popular techniques in machine learning.
From a frequentist/discriminative perspective they play a central role in
regularization theory as they provide a natural choice for the hypotheses space
and the regularization functional through the notion of reproducing kernel
Hilbert spaces. F... | computer science |
9,302 | Representations and Ensemble Methods for Dynamic Relational
Classification | cs.AI | Temporal networks are ubiquitous and evolve over time by the addition,
deletion, and changing of links, nodes, and attributes. Although many
relational datasets contain temporal information, the majority of existing
techniques in relational learning focus on static snapshots and ignore the
temporal dynamics. We propose... | computer science |
9,303 | Robust Filtering and Smoothing with Gaussian Processes | cs.SY | We propose a principled algorithm for robust Bayesian filtering and smoothing
in nonlinear stochastic dynamic systems when both the transition function and
the measurement function are described by non-parametric Gaussian process (GP)
models. GPs are gaining increasing importance in signal processing, machine
learning,... | computer science |
9,304 | Directed Information Graphs | cs.IT | We propose a graphical model for representing networks of stochastic
processes, the minimal generative model graph. It is based on reduced
factorizations of the joint distribution over time. We show that under
appropriate conditions, it is unique and consistent with another type of
graphical model, the directed informa... | computer science |
9,305 | EHRs Connect Research and Practice: Where Predictive Modeling,
Artificial Intelligence, and Clinical Decision Support Intersect | cs.AI | Objectives: Electronic health records (EHRs) are only a first step in
capturing and utilizing health-related data - the challenge is turning that
data into useful information. Furthermore, EHRs are increasingly likely to
include data relating to patient outcomes, functionality such as clinical
decision support, and gen... | computer science |
9,306 | Learning AMP Chain Graphs under Faithfulness | stat.ML | This paper deals with chain graphs under the alternative
Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a
constraint based algorithm for learning an AMP chain graph a given probability
distribution is faithful to. We also show that the extension of Meek's
conjecture to AMP chain graphs does n... | computer science |
9,307 | A Junction Tree Framework for Undirected Graphical Model Selection | stat.ML | An undirected graphical model is a joint probability distribution defined on
an undirected graph G*, where the vertices in the graph index a collection of
random variables and the edges encode conditional independence relationships
among random variables. The undirected graphical model selection (UGMS) problem
is to es... | computer science |
9,308 | Inexact Coordinate Descent: Complexity and Preconditioning | math.OC | In this paper we consider the problem of minimizing a convex function using a
randomized block coordinate descent method. One of the key steps at each
iteration of the algorithm is determining the update to a block of variables.
Existing algorithms assume that in order to compute the update, a particular
subproblem is ... | computer science |
9,309 | Concept-based Recommendations for Internet Advertisement | cs.AI | The problem of detecting terms that can be interesting to the advertiser is
considered. If a company has already bought some advertising terms which
describe certain services, it is reasonable to find out the terms bought by
competing companies. A part of them can be recommended as future advertising
terms to the compa... | computer science |
9,310 | Reading Dependencies from Covariance Graphs | stat.ML | The covariance graph (aka bi-directed graph) of a probability distribution
$p$ is the undirected graph $G$ where two nodes are adjacent iff their
corresponding random variables are marginally dependent in $p$. In this paper,
we present a graphical criterion for reading dependencies from $G$, under the
assumption that $... | computer science |
9,311 | Finding Consensus Bayesian Network Structures | stat.ML | Suppose that multiple experts (or learning algorithms) provide us with
alternative Bayesian network (BN) structures over a domain, and that we are
interested in combining them into a single consensus BN structure.
Specifically, we are interested in that the consensus BN structure only
represents independences all the g... | computer science |
9,312 | An expert system for detecting automobile insurance fraud using social
network analysis | cs.AI | The article proposes an expert system for detection, and subsequent
investigation, of groups of collaborating automobile insurance fraudsters. The
system is described and examined in great detail, several technical
difficulties in detecting fraud are also considered, for it to be applicable in
practice. Opposed to many... | computer science |
9,313 | Mining Permission Request Patterns from Android and Facebook
Applications (extended author version) | cs.CR | Android and Facebook provide third-party applications with access to users'
private data and the ability to perform potentially sensitive operations (e.g.,
post to a user's wall or place phone calls). As a security measure, these
platforms restrict applications' privileges with permission systems: users must
approve th... | computer science |
9,314 | Causal Discovery of Linear Cyclic Models from Multiple Experimental Data
Sets with Overlapping Variables | stat.ME | Much of scientific data is collected as randomized experiments intervening on
some and observing other variables of interest. Quite often, a given phenomenon
is investigated in several studies, and different sets of variables are
involved in each study. In this article we consider the problem of integrating
such knowle... | computer science |
9,315 | Solving Limited Memory Influence Diagrams | cs.AI | We present a new algorithm for exactly solving decision making problems
represented as influence diagrams. We do not require the usual assumptions of
no forgetting and regularity; this allows us to solve problems with
simultaneous decisions and limited information. The algorithm is empirically
shown to outperform a sta... | computer science |
9,316 | Towards Optimal Learning of Chain Graphs | stat.ML | In this paper, we extend Meek's conjecture (Meek 1997) from directed and
acyclic graphs to chain graphs, and prove that the extended conjecture is true.
Specifically, we prove that if a chain graph H is an independence map of the
independence model induced by another chain graph G, then (i) G can be
transformed into H ... | computer science |
9,317 | Free Energy and the Generalized Optimality Equations for Sequential
Decision Making | stat.ML | The free energy functional has recently been proposed as a variational
principle for bounded rational decision-making, since it instantiates a natural
trade-off between utility gains and information processing costs that can be
axiomatically derived. Here we apply the free energy principle to general
decision trees tha... | computer science |
9,318 | Relational Data Mining Through Extraction of Representative Exemplars | cs.AI | With the growing interest on Network Analysis, Relational Data Mining is
becoming an emphasized domain of Data Mining. This paper addresses the problem
of extracting representative elements from a relational dataset. After defining
the notion of degree of representativeness, computed using the Borda
aggregation procedu... | computer science |
9,319 | Integrated Pre-Processing for Bayesian Nonlinear System Identification
with Gaussian Processes | cs.AI | We introduce GP-FNARX: a new model for nonlinear system identification based
on a nonlinear autoregressive exogenous model (NARX) with filtered regressors
(F) where the nonlinear regression problem is tackled using sparse Gaussian
processes (GP). We integrate data pre-processing with system identification
into a fully ... | computer science |
9,320 | Stick-Breaking Policy Learning in Dec-POMDPs | cs.AI | Expectation maximization (EM) has recently been shown to be an efficient
algorithm for learning finite-state controllers (FSCs) in large decentralized
POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often
converge to maxima that are far from optimal. This paper considers a
variable-size FSC to rep... | computer science |
9,321 | Graphical Potential Games | cs.GT | Potential games, originally introduced in the early 1990's by Lloyd Shapley,
the 2012 Nobel Laureate in Economics, and his colleague Dov Monderer, are a
very important class of models in game theory. They have special properties
such as the existence of Nash equilibria in pure strategies. This note
introduces graphical... | computer science |
9,322 | Towards Data-Driven Autonomics in Data Centers | cs.DC | Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, an... | computer science |
9,323 | Correlated Equilibria for Approximate Variational Inference in MRFs | cs.AI | Almost all of the work in graphical models for game theory has mirrored
previous work in probabilistic graphical models. Our work considers the
opposite direction: Taking advantage of recent advances in equilibrium
computation for probabilistic inference. We present formulations of inference
problems in Markov random f... | computer science |
9,324 | Memory shapes time perception and intertemporal choices | cs.AI | There is a consensus that human and non-human subjects experience temporal
distortions in many stages of their perceptual and decision-making systems.
Similarly, intertemporal choice research has shown that decision-makers
undervalue future outcomes relative to immediate ones. Here we combine
techniques from informatio... | computer science |
9,325 | Probabilistic Graphical Models on Multi-Core CPUs using Java 8 | cs.AI | In this paper, we discuss software design issues related to the development
of parallel computational intelligence algorithms on multi-core CPUs, using the
new Java 8 functional programming features. In particular, we focus on
probabilistic graphical models (PGMs) and present the parallelisation of a
collection of algo... | computer science |
9,326 | High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso | stat.ML | The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this pa... | computer science |
9,327 | Recommender System Based on Algorithm of Bicluster Analysis RecBi | cs.AI | In this paper we propose two new algorithms based on biclustering analysis,
which can be used at the basis of a recommender system for educational
orientation of Russian School graduates. The first algorithm was designed to
help students make a choice between different university faculties when some of
their preference... | computer science |
9,328 | Concept Relation Discovery and Innovation Enabling Technology (CORDIET) | cs.AI | Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a
toolbox for gaining new knowledge from unstructured text data. At the core of
CORDIET is the C-K theory which captures the essential elements of innovation.
The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps
(ESOM) and... | computer science |
9,329 | OpenGM: A C++ Library for Discrete Graphical Models | cs.AI | OpenGM is a C++ template library for defining discrete graphical models and
performing inference on these models, using a wide range of state-of-the-art
algorithms. No restrictions are imposed on the factor graph to allow for
higher-order factors and arbitrary neighborhood structures. Large models with
repetitive struc... | computer science |
9,330 | Soil Data Analysis Using Classification Techniques and Soil Attribute
Prediction | cs.AI | Agricultural research has been profited by technical advances such as
automation, data mining. Today, data mining is used in a vast areas and many
off-the-shelf data mining system products and domain specific data mining
application soft wares are available, but data mining in agricultural soil
datasets is a relatively... | computer science |
9,331 | A Nonparametric Conjugate Prior Distribution for the Maximizing Argument
of a Noisy Function | stat.ML | We propose a novel Bayesian approach to solve stochastic optimization
problems that involve finding extrema of noisy, nonlinear functions. Previous
work has focused on representing possible functions explicitly, which leads to
a two-step procedure of first, doing inference over the function space and
second, finding th... | computer science |
9,332 | Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox | stat.ML | Gaussian processes (GP) are powerful tools for probabilistic modeling
purposes. They can be used to define prior distributions over latent functions
in hierarchical Bayesian models. The prior over functions is defined implicitly
by the mean and covariance function, which determine the smoothness and
variability of the ... | computer science |
9,333 | Stochastic Variational Inference | stat.ML | We develop stochastic variational inference, a scalable algorithm for
approximating posterior distributions. We develop this technique for a large
class of probabilistic models and we demonstrate it with two probabilistic
topic models, latent Dirichlet allocation and the hierarchical Dirichlet
process topic model. Usin... | computer science |
9,334 | Parallel Coordinate Descent Methods for Big Data Optimization | math.OC | In this work we show that randomized (block) coordinate descent methods can
be accelerated by parallelization when applied to the problem of minimizing the
sum of a partially separable smooth convex function and a simple separable
convex function. The theoretical speedup, as compared to the serial method, and
referring... | computer science |
9,335 | Multiscale Markov Decision Problems: Compression, Solution, and Transfer
Learning | cs.AI | Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challen... | computer science |
9,336 | Accelerating Inference: towards a full Language, Compiler and Hardware
stack | cs.SE | We introduce Dimple, a fully open-source API for probabilistic modeling.
Dimple allows the user to specify probabilistic models in the form of graphical
models, Bayesian networks, or factor graphs, and performs inference (by
automatically deriving an inference engine from a variety of algorithms) on the
model. Dimple a... | computer science |
9,337 | Towards common-sense reasoning via conditional simulation: legacies of
Turing in Artificial Intelligence | cs.AI | The problem of replicating the flexibility of human common-sense reasoning
has captured the imagination of computer scientists since the early days of
Alan Turing's foundational work on computation and the philosophy of artificial
intelligence. In the intervening years, the idea of cognition as computation
has emerged ... | computer science |
9,338 | Gaussian Process Kernels for Pattern Discovery and Extrapolation | stat.ML | Gaussian processes are rich distributions over functions, which provide a
Bayesian nonparametric approach to smoothing and interpolation. We introduce
simple closed form kernels that can be used with Gaussian processes to discover
patterns and enable extrapolation. These kernels are derived by modelling a
spectral dens... | computer science |
9,339 | Perturbed Message Passing for Constraint Satisfaction Problems | cs.AI | We introduce an efficient message passing scheme for solving Constraint
Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief
Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and
directly produce a single satisfying assignment. Our first CSP solver, called
Perturbed Blief ... | computer science |
9,340 | Information Aggregation in Exponential Family Markets | cs.AI | We consider the design of prediction market mechanisms known as automated
market makers. We show that we can design these mechanisms via the mold of
\emph{exponential family distributions}, a popular and well-studied probability
distribution template used in statistics. We give a full development of this
relationship a... | computer science |
9,341 | A Compilation Target for Probabilistic Programming Languages | cs.AI | Forward inference techniques such as sequential Monte Carlo and particle
Markov chain Monte Carlo for probabilistic programming can be implemented in
any programming language by creative use of standardized operating system
functionality including processes, forking, mutexes, and shared memory.
Exploiting this we have ... | computer science |
9,342 | Credal Model Averaging for classification: representing prior ignorance
and expert opinions | stat.ME | Bayesian model averaging (BMA) is the state of the art approach for
overcoming model uncertainty. Yet, especially on small data sets, the results
yielded by BMA might be sensitive to the prior over the models. Credal Model
Averaging (CMA) addresses this problem by substituting the single prior over
the models by a set ... | computer science |
9,343 | Speeding up Permutation Testing in Neuroimaging | stat.CO | Multiple hypothesis testing is a significant problem in nearly all
neuroimaging studies. In order to correct for this phenomena, we require a
reliable estimate of the Family-Wise Error Rate (FWER). The well known
Bonferroni correction method, while simple to implement, is quite conservative,
and can substantially under... | computer science |
9,344 | Two Timescale Stochastic Approximation with Controlled Markov noise and
Off-policy temporal difference learning | math.DS | We present for the first time an asymptotic convergence analysis of two
time-scale stochastic approximation driven by `controlled' Markov noise. In
particular, both the faster and slower recursions have non-additive controlled
Markov noise components in addition to martingale difference noise. We analyze
the asymptotic... | computer science |
9,345 | Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement
Learning | stat.ML | We study the online estimation of the optimal policy of a Markov decision
process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which
exploit the inherent minimax duality of Bellman equations. The SPD methods
update a few coordinates of the value and policy estimates as a new state
transition is obs... | computer science |
9,346 | New Results for the MAP Problem in Bayesian Networks | cs.AI | This paper presents new results for the (partial) maximum a posteriori (MAP)
problem in Bayesian networks, which is the problem of querying the most
probable state configuration of some of the network variables given evidence.
First, it is demonstrated that the problem remains hard even in networks with
very simple top... | computer science |
9,347 | Discovering shared and individual latent structure in multiple time
series | stat.ML | This paper proposes a nonparametric Bayesian method for exploratory data
analysis and feature construction in continuous time series. Our method focuses
on understanding shared features in a set of time series that exhibit
significant individual variability. Our method builds on the framework of
latent Diricihlet alloc... | computer science |
9,348 | Faithfulness in Chain Graphs: The Gaussian Case | stat.ML | This paper deals with chain graphs under the classic
Lauritzen-Wermuth-Frydenberg interpretation. We prove that the regular Gaussian
distributions that factorize with respect to a chain graph $G$ with $d$
parameters have positive Lebesgue measure with respect to $\mathbb{R}^d$,
whereas those that factorize with respect... | computer science |
9,349 | Epistemic irrelevance in credal nets: the case of imprecise Markov trees | cs.AI | We focus on credal nets, which are graphical models that generalise Bayesian
nets to imprecise probability. We replace the notion of strong independence
commonly used in credal nets with the weaker notion of epistemic irrelevance,
which is arguably more suited for a behavioural theory of probability. Focusing
on direct... | computer science |
9,350 | PMOG: The projected mixture of Gaussians model with application to blind
source separation | stat.ML | We extend the mixtures of Gaussians (MOG) model to the projected mixture of
Gaussians (PMOG) model. In the PMOG model, we assume that q dimensional input
data points z_i are projected by a q dimensional vector w into 1-D variables
u_i. The projected variables u_i are assumed to follow a 1-D MOG model. In the
PMOG model... | computer science |
9,351 | Machine Learning with Operational Costs | stat.ML | This work proposes a way to align statistical modeling with decision making.
We provide a method that propagates the uncertainty in predictive modeling to
the uncertainty in operational cost, where operational cost is the amount spent
by the practitioner in solving the problem. The method allows us to explore the
range... | computer science |
9,352 | Augur: a Modeling Language for Data-Parallel Probabilistic Inference | stat.ML | It is time-consuming and error-prone to implement inference procedures for
each new probabilistic model. Probabilistic programming addresses this problem
by allowing a user to specify the model and having a compiler automatically
generate an inference procedure for it. For this approach to be practical, it
is important... | computer science |
9,353 | Abstraction in decision-makers with limited information processing
capabilities | cs.AI | A distinctive property of human and animal intelligence is the ability to
form abstractions by neglecting irrelevant information which allows to separate
structure from noise. From an information theoretic point of view abstractions
are desirable because they allow for very efficient information processing. In
artifici... | computer science |
9,354 | A General Algorithm for Deciding Transportability of Experimental
Results | cs.AI | Generalizing empirical findings to new environments, settings, or populations
is essential in most scientific explorations. This article treats a particular
problem of generalizability, called "transportability", defined as a license to
transfer information learned in experimental studies to a different population,
on ... | computer science |
9,355 | Venture: a higher-order probabilistic programming platform with
programmable inference | cs.AI | We describe Venture, an interactive virtual machine for probabilistic
programming that aims to be sufficiently expressive, extensible, and efficient
for general-purpose use. Like Church, probabilistic models and inference
problems in Venture are specified via a Turing-complete, higher-order
probabilistic language desce... | computer science |
9,356 | Subjectivity, Bayesianism, and Causality | cs.AI | Bayesian probability theory is one of the most successful frameworks to model
reasoning under uncertainty. Its defining property is the interpretation of
probabilities as degrees of belief in propositions about the state of the world
relative to an inquiring subject. This essay examines the notion of
subjectivity by dr... | computer science |
9,357 | Multi-Context Models for Reasoning under Partial Knowledge: Generative
Process and Inference Grammar | cs.AI | Arriving at the complete probabilistic knowledge of a domain, i.e., learning
how all variables interact, is indeed a demanding task. In reality, settings
often arise for which an individual merely possesses partial knowledge of the
domain, and yet, is expected to give adequate answers to a variety of posed
queries. Tha... | computer science |
9,358 | Particle Gibbs with Ancestor Sampling for Probabilistic Programs | stat.ML | Particle Markov chain Monte Carlo techniques rank among current
state-of-the-art methods for probabilistic program inference. A drawback of
these techniques is that they rely on importance resampling, which results in
degenerate particle trajectories and a low effective sample size for variables
sampled early in a prog... | computer science |
9,359 | Expectation Particle Belief Propagation | stat.CO | We propose an original particle-based implementation of the Loopy Belief
Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a
continuous state space. The algorithm constructs adaptively efficient proposal
distributions approximating the local beliefs at each note of the MRF. This is
achieved by cons... | computer science |
9,360 | Top-N recommendations in the presence of sparsity: An NCD-based approach | cs.IR | Making recommendations in the presence of sparsity is known to present one of
the most challenging problems faced by collaborative filtering methods. In this
work we tackle this problem by exploiting the innately hierarchical structure
of the item space following an approach inspired by the theory of
Decomposability. W... | computer science |
9,361 | A New Approach to Probabilistic Programming Inference | stat.ML | We introduce and demonstrate a new approach to inference in expressive
probabilistic programming languages based on particle Markov chain Monte Carlo.
Our approach is simple to implement and easy to parallelize. It applies to
Turing-complete probabilistic programming languages and supports accurate
inference in models ... | computer science |
9,362 | Online Model Evaluation in a Large-Scale Computational Advertising
Platform | cs.AI | Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing me... | computer science |
9,363 | Mapping Heritability of Large-Scale Brain Networks with a Billion
Connections {\em via} Persistent Homology | cs.AI | In many human brain network studies, we do not have sufficient number (n) of
images relative to the number (p) of voxels due to the prohibitively expensive
cost of scanning enough subjects. Thus, brain network models usually suffer the
small-n large-p problem. Such a problem is often remedied by sparse network
models, ... | computer science |
9,364 | Energy saving in smart homes based on consumer behaviour: A case study | stat.ML | This paper presents a case study of a recommender system that can be used to
save energy in smart homes without lowering the comfort of the inhabitants. We
present an algorithm that uses consumer behavior data only and uses machine
learning to suggest actions for inhabitants to reduce the energy consumption of
their ho... | computer science |
9,365 | Minimum Weight Perfect Matching via Blossom Belief Propagation | cs.DS | Max-product Belief Propagation (BP) is a popular message-passing algorithm
for computing a Maximum-A-Posteriori (MAP) assignment over a distribution
represented by a Graphical Model (GM). It has been shown that BP can solve a
number of combinatorial optimization problems including minimum weight
matching, shortest path... | computer science |
9,366 | Multimodal Hierarchical Dirichlet Process-based Active Perception | cs.RO | In this paper, we propose an active perception method for recognizing object
categories based on the multimodal hierarchical Dirichlet process (MHDP). The
MHDP enables a robot to form object categories using multimodal information,
e.g., visual, auditory, and haptic information, which can be observed by
performing acti... | computer science |
9,367 | Latent Bayesian melding for integrating individual and population models | stat.ML | In many statistical problems, a more coarse-grained model may be suitable for
population-level behaviour, whereas a more detailed model is appropriate for
accurate modelling of individual behaviour. This raises the question of how to
integrate both types of models. Methods such as posterior regularization follow
the id... | computer science |
9,368 | From random walks to distances on unweighted graphs | stat.ML | Large unweighted directed graphs are commonly used to capture relations
between entities. A fundamental problem in the analysis of such networks is to
properly define the similarity or dissimilarity between any two vertices.
Despite the significance of this problem, statistical characterization of the
proposed metrics ... | computer science |
9,369 | CrossCat: A Fully Bayesian Nonparametric Method for Analyzing
Heterogeneous, High Dimensional Data | cs.AI | There is a widespread need for statistical methods that can analyze
high-dimensional datasets with- out imposing restrictive or opaque modeling
assumptions. This paper describes a domain-general data analysis method called
CrossCat. CrossCat infers multiple non-overlapping views of the data, each
consisting of a subset... | computer science |
9,370 | Data-driven Sequential Monte Carlo in Probabilistic Programming | cs.AI | Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)
algorithms in existing probabilistic programming systems suboptimally use only
model priors as proposal distributions. In this work, we describe an approach
for training a discriminative model, namely a neural network, in order to
approximate the ... | computer science |
9,371 | Information-Theoretic Bounded Rationality | stat.ML | Bounded rationality, that is, decision-making and planning under resource
limitations, is widely regarded as an important open problem in artificial
intelligence, reinforcement learning, computational neuroscience and economics.
This paper offers a consolidated presentation of a theory of bounded
rationality based on i... | computer science |
9,372 | Machine olfaction using time scattering of sensor multiresolution graphs | cs.AI | In this paper we construct a learning architecture for high dimensional time
series sampled by sensor arrangements. Using a redundant wavelet decomposition
on a graph constructed over the sensor locations, our algorithm is able to
construct discriminative features that exploit the mutual information between
the sensors... | computer science |
9,373 | Inverse Reinforcement Learning in Swarm Systems | stat.ML | Inverse reinforcement learning (IRL) has become a useful tool for learning
behavioral models from demonstration data. However, IRL remains mostly
unexplored for multi-agent systems. In this paper, we show how the principle of
IRL can be extended to homogeneous large-scale problems, inspired by the
collective swarming b... | computer science |
9,374 | Top-N Recommendation with Novel Rank Approximation | cs.IR | The importance of accurate recommender systems has been widely recognized by
academia and industry. However, the recommendation quality is still rather low.
Recently, a linear sparse and low-rank representation of the user-item matrix
has been applied to produce Top-N recommendations. This approach uses the
nuclear nor... | computer science |
9,375 | Composing inference algorithms as program transformations | stat.ML | Probabilistic inference procedures are usually coded painstakingly from
scratch, for each target model and each inference algorithm. We reduce this
effort by generating inference procedures from models automatically. We make
this code generation modular by decomposing inference algorithms into reusable
program-to-progr... | computer science |
9,376 | COCO: A Platform for Comparing Continuous Optimizers in a Black-Box
Setting | cs.AI | COCO is a platform for Comparing Continuous Optimizers in a black-box
setting. It aims at automatizing the tedious and repetitive task of
benchmarking numerical optimization algorithms to the greatest possible extent.
We present the rationals behind the development of the platform as a general
proposition for a guideli... | computer science |
9,377 | Adaptive Maximization of Pointwise Submodular Functions With Budget
Constraint | cs.AI | We study the worst-case adaptive optimization problem with budget constraint
that is useful for modeling various practical applications in artificial
intelligence and machine learning. We investigate the near-optimality of greedy
algorithms for this problem with both modular and non-modular cost functions.
In both case... | computer science |
9,378 | Wisdom of Crowds cluster ensemble | stat.ML | The Wisdom of Crowds is a phenomenon described in social science that
suggests four criteria applicable to groups of people. It is claimed that, if
these criteria are satisfied, then the aggregate decisions made by a group will
often be better than those of its individual members. Inspired by this concept,
we present a... | computer science |
9,379 | A note on privacy preserving iteratively reweighted least squares | cs.CR | Iteratively reweighted least squares (IRLS) is a widely-used method in
machine learning to estimate the parameters in the generalised linear models.
In particular, IRLS for L1 minimisation under the linear model provides a
closed-form solution in each step, which is a simple multiplication between the
inverse of the we... | computer science |
9,380 | Posterior Dispersion Indices | stat.ML | Probabilistic modeling is cyclical: we specify a model, infer its posterior,
and evaluate its performance. Evaluation drives the cycle, as we revise our
model based on how it performs. This requires a metric. Traditionally,
predictive accuracy prevails. Yet, predictive accuracy does not tell the whole
story. We propose... | computer science |
9,381 | MCMC assisted by Belief Propagaion | stat.ML | Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most
popular algorithms for computational inference in Graphical Models (GM). In
principle, MCMC is an exact probabilistic method which, however, often suffers
from exponentially slow mixing. In contrast, BP is a deterministic method,
which is typicall... | computer science |
9,382 | Information Theoretically Aided Reinforcement Learning for Embodied
Agents | cs.AI | Reinforcement learning for embodied agents is a challenging problem. The
accumulated reward to be optimized is often a very rugged function, and
gradient methods are impaired by many local optimizers. We demonstrate, in an
experimental setting, that incorporating an intrinsic reward can smoothen the
optimization landsc... | computer science |
9,383 | Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization | cs.NA | The Schatten-p quasi-norm $(0<p<1)$ is usually used to replace the standard
nuclear norm in order to approximate the rank function more accurately.
However, existing Schatten-p quasi-norm minimization algorithms involve
singular value decomposition (SVD) or eigenvalue decomposition (EVD) in each
iteration, and thus may... | computer science |
9,384 | Efficient Attack Graph Analysis through Approximate Inference | cs.CR | Attack graphs provide compact representations of the attack paths that an
attacker can follow to compromise network resources by analysing network
vulnerabilities and topology. These representations are a powerful tool for
security risk assessment. Bayesian inference on attack graphs enables the
estimation of the risk ... | computer science |
9,385 | On Gaussian Markov models for conditional independence | stat.ME | Markov models lie at the interface between statistical independence in a
probability distribution and graph separation properties. We review model
selection and estimation in directed and undirected Markov models with Gaussian
parametrization, emphasizing the main similarities and differences. These two
models are simi... | computer science |
9,386 | One-Shot Session Recommendation Systems with Combinatorial Items | stat.ML | In recent years, content recommendation systems in large websites (or
\emph{content providers}) capture an increased focus. While the type of content
varies, e.g.\ movies, articles, music, advertisements, etc., the high level
problem remains the same. Based on knowledge obtained so far on the user,
recommend the most d... | computer science |
9,387 | Leveraging Unstructured Data to Detect Emerging Reliability Issues | cs.AI | Unstructured data refers to information that does not have a predefined data
model or is not organized in a pre-defined manner. Loosely speaking,
unstructured data refers to text data that is generated by humans. In
after-sales service businesses, there are two main sources of unstructured
data: customer complaints, wh... | computer science |
9,388 | Deep Survival Analysis | stat.ML | The electronic health record (EHR) provides an unprecedented opportunity to
build actionable tools to support physicians at the point of care. In this
paper, we investigate survival analysis in the context of EHR data. We
introduce deep survival analysis, a hierarchical generative approach to
survival analysis. It depa... | computer science |
9,389 | Revisiting Causality Inference in Memory-less Transition Networks | stat.ML | Several methods exist to infer causal networks from massive volumes of
observational data. However, almost all existing methods require a considerable
length of time series data to capture cause and effect relationships. In
contrast, memory-less transition networks or Markov Chain data, which refers to
one-step transit... | computer science |
9,390 | QPass: a Merit-based Evaluation of Soccer Passes | cs.AI | Quantitative analysis of soccer players' passing ability focuses on
descriptive statistics without considering the players' real contribution to
the passing and ball possession strategy of their team. Which player is able to
help the build-up of an attack, or to maintain the possession of the ball? We
introduce a novel... | computer science |
9,391 | On Generation of Time-based Label Refinements | stat.ME | Process mining is a research field focused on the analysis of event data with
the aim of extracting insights in processes. Applying process mining techniques
on data from smart home environments has the potential to provide valuable
insights in (un)healthy habits and to contribute to ambient assisted living
solutions. ... | computer science |
9,392 | Enabling Dark Energy Science with Deep Generative Models of Galaxy
Images | cs.AI | Understanding the nature of dark energy, the mysterious force driving the
accelerated expansion of the Universe, is a major challenge of modern
cosmology. The next generation of cosmological surveys, specifically designed
to address this issue, rely on accurate measurements of the apparent shapes of
distant galaxies. H... | computer science |
9,393 | Predictive modelling of football injuries | stat.AP | The goal of this thesis is to investigate the potential of predictive
modelling for football injuries. This work was conducted in close collaboration
with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation
of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the... | computer science |
9,394 | Flexible constrained sampling with guarantees for pattern mining | cs.AI | Pattern sampling has been proposed as a potential solution to the infamous
pattern explosion. Instead of enumerating all patterns that satisfy the
constraints, individual patterns are sampled proportional to a given quality
measure. Several sampling algorithms have been proposed, but each of them has
its limitations wh... | computer science |
9,395 | Normalizing Flows on Riemannian Manifolds | stat.ML | We consider the problem of density estimation on Riemannian manifolds.
Density estimation on manifolds has many applications in fluid-mechanics,
optics and plasma physics and it appears often when dealing with angular
variables (such as used in protein folding, robot limbs, gene-expression) and
in general directional s... | computer science |
9,396 | Entropic Causal Inference | cs.AI | We consider the problem of identifying the causal direction between two
discrete random variables using observational data. Unlike previous work, we
keep the most general functional model but make an assumption on the unobserved
exogenous variable: Inspired by Occam's razor, we assume that the exogenous
variable is sim... | computer science |
9,397 | Quantum Enhanced Inference in Markov Logic Networks | stat.ML | Markov logic networks (MLNs) reconcile two opposing schools in machine
learning and artificial intelligence: causal networks, which account for
uncertainty extremely well, and first-order logic, which allows for formal
deduction. An MLN is essentially a first-order logic template to generate
Markov networks. Inference ... | computer science |
9,398 | Playtime Measurement with Survival Analysis | stat.AP | Maximizing product use is a central goal of many businesses, which makes
retention and monetization two central analytics metrics in games. Player
retention may refer to various duration variables quantifying product use:
total playtime or session playtime are popular research targets, and active
playtime is well-suite... | computer science |
9,399 | Unknowable Manipulators: Social Network Curator Algorithms | cs.AI | For a social networking service to acquire and retain users, it must find
ways to keep them engaged. By accurately gauging their preferences, it is able
to serve them with the subset of available content that maximises revenue for
the site. Without the constraints of an appropriate regulatory framework, we
argue that a... | computer science |
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