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12,902 | Ensembles of Protein Molecules as Statistical Analog Computers | cs.AI | A class of analog computers built from large numbers of microscopic
probabilistic machines is discussed. It is postulated that such computers are
implemented in biological systems as ensembles of protein molecules. The
formalism is based on an abstract computational model referred to as Protein
Molecule Machine (PMM). ... | computer science |
12,903 | Proceedings Fifth Workshop on Developments in Computational
Models--Computational Models From Nature | cs.CE | The special theme of DCM 2009, co-located with ICALP 2009, concerned
Computational Models From Nature, with a particular emphasis on computational
models derived from physics and biology. The intention was to bring together
different approaches - in a community with a strong foundational background as
proffered by the ... | computer science |
12,904 | Robust artificial neural networks and outlier detection. Technical
report | math.OC | Large outliers break down linear and nonlinear regression models. Robust
regression methods allow one to filter out the outliers when building a model.
By replacing the traditional least squares criterion with the least trimmed
squares criterion, in which half of data is treated as potential outliers, one
can fit accur... | computer science |
12,905 | Landau Theory of Adaptive Integration in Computational Intelligence | stat.ML | Computational Intelligence (CI) is a sub-branch of Artificial Intelligence
paradigm focusing on the study of adaptive mechanisms to enable or facilitate
intelligent behavior in complex and changing environments. There are several
paradigms of CI [like artificial neural networks, evolutionary computations,
swarm intelli... | computer science |
12,906 | Supervised Random Walks: Predicting and Recommending Links in Social
Networks | cs.SI | Predicting the occurrence of links is a fundamental problem in networks. In
the link prediction problem we are given a snapshot of a network and would like
to infer which interactions among existing members are likely to occur in the
near future or which existing interactions are we missing. Although this
problem has b... | computer science |
12,907 | A Sequence of Relaxations Constraining Hidden Variable Models | cs.AI | Many widely studied graphical models with latent variables lead to nontrivial
constraints on the distribution of the observed variables. Inspired by the Bell
inequalities in quantum mechanics, we refer to any linear inequality whose
violation rules out some latent variable model as a "hidden variable test" for
that mod... | computer science |
12,908 | Residual Component Analysis | stat.ML | Probabilistic principal component analysis (PPCA) seeks a low dimensional
representation of a data set in the presence of independent spherical Gaussian
noise, Sigma = (sigma^2)*I. The maximum likelihood solution for the model is an
eigenvalue problem on the sample covariance matrix. In this paper we consider
the situa... | computer science |
12,909 | Mixed-Membership Stochastic Block-Models for Transactional Networks | stat.ML | Transactional network data can be thought of as a list of one-to-many
communications(e.g., email) between nodes in a social network. Most social
network models convert this type of data into binary relations between pairs of
nodes. We develop a latent mixed membership model capable of modeling richer
forms of transacti... | computer science |
12,910 | Multigrid with rough coefficients and Multiresolution operator
decomposition from Hierarchical Information Games | math.NA | We introduce a near-linear complexity (geometric and meshless/algebraic)
multigrid/multiresolution method for PDEs with rough ($L^\infty$) coefficients
with rigorous a-priori accuracy and performance estimates. The method is
discovered through a decision/game theory formulation of the problems of (1)
identifying restri... | computer science |
12,911 | Supervised Quantum Learning without Measurements | cs.AI | We propose a quantum machine learning algorithm for efficiently solving a
class of problems encoded in quantum controlled unitary operations. The central
physical mechanism of the protocol is the iteration of a quantum time-delayed
equation that introduces feedback in the dynamics and eliminates the necessity
of interm... | computer science |
12,912 | Generalized Compression Dictionary Distance as Universal Similarity
Measure | stat.ML | We present a new similarity measure based on information theoretic measures
which is superior than Normalized Compression Distance for clustering problems
and inherits the useful properties of conditional Kolmogorov complexity. We
show that Normalized Compression Dictionary Size and Normalized Compression
Dictionary En... | computer science |
12,913 | Boolean Matrix Factorization and Noisy Completion via Message Passing | math.ST | Boolean matrix factorization and Boolean matrix completion from noisy
observations are desirable unsupervised data-analysis methods due to their
interpretability, but hard to perform due to their NP-hardness. We treat these
problems as maximum a posteriori inference problems in a graphical model and
present a message p... | computer science |
12,914 | Large Collection of Diverse Gene Set Search Queries Recapitulate Known
Protein-Protein Interactions and Gene-Gene Functional Associations | cs.AI | Popular online enrichment analysis tools from the field of molecular systems
biology provide users with the ability to submit their experimental results as
gene sets for individual analysis. Such queries are kept private, and have
never before been considered as a resource for integrative analysis. By
harnessing gene s... | computer science |
12,915 | Infinite-dimensional Log-Determinant divergences II: Alpha-Beta
divergences | math.FA | This work presents a parametrized family of divergences, namely Alpha-Beta
Log- Determinant (Log-Det) divergences, between positive definite unitized
trace class operators on a Hilbert space. This is a generalization of the
Alpha-Beta Log-Determinant divergences between symmetric, positive definite
matrices to the infi... | computer science |
12,916 | Edward: A library for probabilistic modeling, inference, and criticism | stat.CO | Probabilistic modeling is a powerful approach for analyzing empirical
information. We describe Edward, a library for probabilistic modeling. Edward's
design reflects an iterative process pioneered by George Box: build a model of
a phenomenon, make inferences about the model given data, and criticize the
model's fit to ... | computer science |
12,917 | Basic protocols in quantum reinforcement learning with superconducting
circuits | cs.AI | Superconducting circuit technologies have recently achieved quantum protocols
involving closed feedback loops. Quantum artificial intelligence and quantum
machine learning are emerging fields inside quantum technologies which may
enable quantum devices to acquire information from the outer world and improve
themselves ... | computer science |
12,918 | Duality of Graphical Models and Tensor Networks | math.ST | In this article we show the duality between tensor networks and undirected
graphical models with discrete variables. We study tensor networks on
hypergraphs, which we call tensor hypernetworks. We show that the tensor
hypernetwork on a hypergraph exactly corresponds to the graphical model given
by the dual hypergraph. ... | computer science |
12,919 | Marginal sequential Monte Carlo for doubly intractable models | stat.CO | Bayesian inference for models that have an intractable partition function is
known as a doubly intractable problem, where standard Monte Carlo methods are
not applicable. The past decade has seen the development of auxiliary variable
Monte Carlo techniques (M{\o}ller et al., 2006; Murray et al., 2006) for
tackling this... | computer science |
12,920 | "Dave...I can assure you...that it's going to be all right..." -- A
definition, case for, and survey of algorithmic assurances in human-autonomy
trust relationships | cs.CY | As technology becomes more advanced, those who design, use and are otherwise
affected by it want to know that it will perform correctly, and understand why
it does what it does, and how to use it appropriately. In essence they want to
be able to trust the systems that are being designed. In this survey we present
assur... | computer science |
12,921 | Bootstrapped synthetic likelihood | stat.CO | Approximate Bayesian computation (ABC) and synthetic likelihood (SL)
techniques have enabled the use of Bayesian inference for models that may be
simulated, but for which the likelihood cannot be evaluated pointwise at values
of an unknown parameter $\theta$. The main idea in ABC and SL is to, for
different values of $... | computer science |
12,922 | Modeling the Formation of Social Conventions in Multi-Agent Populations | cs.MA | In order to understand the formation of social conventions we need to know
the specific role of control and learning in multi-agent systems. To advance in
this direction, we propose, within the framework of the Distributed Adaptive
Control (DAC) theory, a novel Control-based Reinforcement Learning architecture
(CRL) th... | computer science |
12,923 | Using Simulated Annealing to Calculate the Trembles of Trembling Hand
Perfection | cs.GT | Within the literature on non-cooperative game theory, there have been a
number of attempts to propose logorithms which will compute Nash equilibria.
Rather than derive a new algorithm, this paper shows that the family of
algorithms known as Markov chain Monte Carlo (MCMC) can be used to calculate
Nash equilibria. MCMC ... | computer science |
12,924 | Learning from Complex Systems: On the Roles of Entropy and Fisher
Information in Pairwise Isotropic Gaussian Markov Random Fields | cs.IT | Markov Random Field models are powerful tools for the study of complex
systems. However, little is known about how the interactions between the
elements of such systems are encoded, especially from an information-theoretic
perspective. In this paper, our goal is to enlight the connection between
Fisher information, Sha... | computer science |
12,925 | An iterative feature selection method for GRNs inference by exploring
topological properties | cs.CV | An important problem in bioinformatics is the inference of gene regulatory
networks (GRN) from temporal expression profiles. In general, the main
limitations faced by GRN inference methods is the small number of samples with
huge dimensionalities and the noisy nature of the expression measurements. In
face of these lim... | computer science |
12,926 | I'm sorry to say, but your understanding of image processing
fundamentals is absolutely wrong | cs.AI | The ongoing discussion whether modern vision systems have to be viewed as
visually-enabled cognitive systems or cognitively-enabled vision systems is
groundless, because perceptual and cognitive faculties of vision are separate
components of human (and consequently, artificial) information processing
system modeling. | computer science |
12,927 | Quantifying Creativity in Art Networks | cs.AI | Can we develop a computer algorithm that assesses the creativity of a
painting given its context within art history? This paper proposes a novel
computational framework for assessing the creativity of creative products, such
as paintings, sculptures, poetry, etc. We use the most common definition of
creativity, which e... | computer science |
12,928 | ShapeNet: An Information-Rich 3D Model Repository | cs.GR | We present ShapeNet: a richly-annotated, large-scale repository of shapes
represented by 3D CAD models of objects. ShapeNet contains 3D models from a
multitude of semantic categories and organizes them under the WordNet taxonomy.
It is a collection of datasets providing many semantic annotations for each 3D
model such ... | computer science |
12,929 | CAS-CNN: A Deep Convolutional Neural Network for Image Compression
Artifact Suppression | cs.CV | Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
addre... | computer science |
12,930 | Beautiful and damned. Combined effect of content quality and social ties
on user engagement | cs.SI | User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate h... | computer science |
12,931 | Visually-Aware Fashion Recommendation and Design with Generative Image
Models | cs.CV | Building effective recommender systems for domains like fashion is
challenging due to the high level of subjectivity and the semantic complexity
of the features involved (i.e., fashion styles). Recent work has shown that
approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made
more accurate by incor... | computer science |
12,932 | Learning audio and image representations with bio-inspired trainable
feature extractors | cs.CV | Recent advancements in pattern recognition and signal processing concern the
automatic learning of data representations from labeled training samples.
Typical approaches are based on deep learning and convolutional neural
networks, which require large amount of labeled training samples. In this work,
we propose novel f... | computer science |
12,933 | Computationally efficient algorithms for statistical image processing.
Implementation in R | stat.CO | In the series of our earlier papers on the subject, we proposed a novel
statistical hypothesis testing method for detection of objects in noisy images.
The method uses results from percolation theory and random graph theory. We
developed algorithms that allowed to detect objects of unknown shapes in the
presence of non... | computer science |
12,934 | Methods of Hierarchical Clustering | cs.IR | We survey agglomerative hierarchical clustering algorithms and discuss
efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing maps, and mixture models.
We review grid-based clustering, focusing on hierarchical density-based
approaches. Finally we descr... | computer science |
12,935 | Highly comparative time-series analysis: The empirical structure of time
series and their methods | cs.CV | The process of collecting and organizing sets of observations represents a
common theme throughout the history of science. However, despite the ubiquity
of scientists measuring, recording, and analyzing the dynamics of different
processes, an extensive organization of scientific time-series data and
analysis methods ha... | computer science |
12,936 | Adaptive-Rate Sparse Signal Reconstruction With Application in
Compressive Background Subtraction | math.OC | We propose and analyze an online algorithm for reconstructing a sequence of
signals from a limited number of linear measurements. The signals are assumed
sparse, with unknown support, and evolve over time according to a generic
nonlinear dynamical model. Our algorithm, based on recent theoretical results
for $\ell_1$-$... | computer science |
12,937 | On Transitive Consistency for Linear Invertible Transformations between
Euclidean Coordinate Systems | math.OC | Transitive consistency is an intrinsic property for collections of linear
invertible transformations between Euclidean coordinate frames. In practice,
when the transformations are estimated from data, this property is lacking.
This work addresses the problem of synchronizing transformations that are not
transitively co... | computer science |
12,938 | Complete Dictionary Recovery over the Sphere I: Overview and the
Geometric Picture | cs.IT | We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb{R}^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to theoretical
understanding of dictionary learnin... | computer science |
12,939 | Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian
Trust-region Method | cs.IT | We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb{R}^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to theoretical
understanding of dictionary learnin... | computer science |
12,940 | High Accuracy Classification of Parkinson's Disease through Shape
Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging | stat.AP | Early and accurate identification of parkinsonian syndromes (PS) involving
presynaptic degeneration from non-degenerative variants such as Scans Without
Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for
effective patient management as the course, therapy and prognosis differ
substantially ... | computer science |
12,941 | Iteratively Linearized Reweighted Alternating Direction Method of
Multipliers for a Class of Nonconvex Problems | cs.NA | In this paper, we consider solving a class of nonconvex and nonsmooth
problems frequently appearing in signal processing and machine learning
research. The traditional alternating direction method of multipliers
encounters troubles in both mathematics and computations in solving the
nonconvex and nonsmooth subproblem. ... | computer science |
12,942 | Towards a Universal Theory of Artificial Intelligence based on
Algorithmic Probability and Sequential Decision Theory | cs.AI | Decision theory formally solves the problem of rational agents in uncertain
worlds if the true environmental probability distribution is known.
Solomonoff's theory of universal induction formally solves the problem of
sequence prediction for unknown distribution. We unify both theories and give
strong arguments that th... | computer science |
12,943 | On the Existence and Convergence Computable Universal Priors | cs.LG | Solomonoff unified Occam's razor and Epicurus' principle of multiple
explanations to one elegant, formal, universal theory of inductive inference,
which initiated the field of algorithmic information theory. His central result
is that the posterior of his universal semimeasure M converges rapidly to the
true sequence g... | computer science |
12,944 | On the Convergence Speed of MDL Predictions for Bernoulli Sequences | cs.LG | We consider the Minimum Description Length principle for online sequence
prediction. If the underlying model class is discrete, then the total expected
square loss is a particularly interesting performance measure: (a) this
quantity is bounded, implying convergence with probability one, and (b) it
additionally specifie... | computer science |
12,945 | Distributed Kernel Regression: An Algorithm for Training Collaboratively | cs.LG | This paper addresses the problem of distributed learning under communication
constraints, motivated by distributed signal processing in wireless sensor
networks and data mining with distributed databases. After formalizing a
general model for distributed learning, an algorithm for collaboratively
training regularized k... | computer science |
12,946 | Equivalence of LP Relaxation and Max-Product for Weighted Matching in
General Graphs | cs.IT | Max-product belief propagation is a local, iterative algorithm to find the
mode/MAP estimate of a probability distribution. While it has been successfully
employed in a wide variety of applications, there are relatively few
theoretical guarantees of convergence and correctness for general loopy graphs
that may have man... | computer science |
12,947 | On Tsallis Entropy Bias and Generalized Maximum Entropy Models | cs.LG | In density estimation task, maximum entropy model (Maxent) can effectively
use reliable prior information via certain constraints, i.e., linear
constraints without empirical parameters. However, reliable prior information
is often insufficient, and the selection of uncertain constraints becomes
necessary but poses cons... | computer science |
12,948 | Collective Classification of Textual Documents by Guided
Self-Organization in T-Cell Cross-Regulation Dynamics | cs.IR | We present and study an agent-based model of T-Cell cross-regulation in the
adaptive immune system, which we apply to binary classification. Our method
expands an existing analytical model of T-cell cross-regulation (Carneiro et
al. in Immunol Rev 216(1):48-68, 2007) that was used to study the
self-organizing dynamics ... | computer science |
12,949 | Counterfactual Reasoning and Learning Systems | cs.LG | This work shows how to leverage causal inference to understand the behavior
of complex learning systems interacting with their environment and predict the
consequences of changes to the system. Such predictions allow both humans and
algorithms to select changes that improve both the short-term and long-term
performance... | computer science |
12,950 | Probabilities on Sentences in an Expressive Logic | cs.LO | Automated reasoning about uncertain knowledge has many applications. One
difficulty when developing such systems is the lack of a completely
satisfactory integration of logic and probability. We address this problem
directly. Expressive languages like higher-order logic are ideally suited for
representing and reasoning... | computer science |
12,951 | HOL(y)Hammer: Online ATP Service for HOL Light | cs.AI | HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable)
mathematics encoded in the HOL Light system. The service allows its users to
upload and automatically process an arbitrary formal development (project)
based on HOL Light, and to attack arbitrary conjectures that use the concepts
defined in s... | computer science |
12,952 | Safe Probability | stat.ME | We formalize the idea of probability distributions that lead to reliable
predictions about some, but not all aspects of a domain. The resulting notion
of `safety' provides a fresh perspective on foundational issues in statistics,
providing a middle ground between imprecise probability and multiple-prior
models on the o... | computer science |
12,953 | Decentralized Data Fusion and Active Sensing with Mobile Sensors for
Modeling and Predicting Spatiotemporal Traffic Phenomena | cs.LG | The problem of modeling and predicting spatiotemporal traffic phenomena over
an urban road network is important to many traffic applications such as
detecting and forecasting congestion hotspots. This paper presents a
decentralized data fusion and active sensing (D2FAS) algorithm for mobile
sensors to actively explore ... | computer science |
12,954 | Cortical prediction markets | cs.AI | We investigate cortical learning from the perspective of mechanism design.
First, we show that discretizing standard models of neurons and synaptic
plasticity leads to rational agents maximizing simple scoring rules. Second,
our main result is that the scoring rules are proper, implying that neurons
faithfully encode e... | computer science |
12,955 | Highly comparative feature-based time-series classification | cs.LG | A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time ser... | computer science |
12,956 | Rate of Convergence and Error Bounds for LSTD($λ$) | cs.LG | We consider LSTD($\lambda$), the least-squares temporal-difference algorithm
with eligibility traces algorithm proposed by Boyan (2002). It computes a
linear approximation of the value function of a fixed policy in a large Markov
Decision Process. Under a $\beta$-mixing assumption, we derive, for any value
of $\lambda ... | computer science |
12,957 | TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in
Cellular Radio Access Networks | cs.NI | Recent works have validated the possibility of improving energy efficiency in
radio access networks (RANs), achieved by dynamically turning on/off some base
stations (BSs). In this paper, we extend the research over BS switching
operations, which should match up with traffic load variations. Instead of
depending on the... | computer science |
12,958 | MizAR 40 for Mizar 40 | cs.AI | As a present to Mizar on its 40th anniversary, we develop an AI/ATP system
that in 30 seconds of real time on a 14-CPU machine automatically proves 40% of
the theorems in the latest official version of the Mizar Mathematical Library
(MML). This is a considerable improvement over previous performance of large-
theory AI... | computer science |
12,959 | A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion
Systems | cs.AI | We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive... | computer science |
12,960 | Private Disclosure of Information in Health Tele-monitoring | cs.CR | We present a novel framework, called Private Disclosure of Information (PDI),
which is aimed to prevent an adversary from inferring certain sensitive
information about subjects using the data that they disclosed during
communication with an intended recipient. We show cases where it is possible to
achieve perfect priva... | computer science |
12,961 | Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge
Evolution | cs.SY | Cloud controllers aim at responding to application demands by automatically
scaling the compute resources at runtime to meet performance guarantees and
minimize resource costs. Existing cloud controllers often resort to scaling
strategies that are codified as a set of adaptation rules. However, for a cloud
provider, ap... | computer science |
12,962 | Robust Learning of Fixed-Structure Bayesian Networks | cs.DS | We investigate the problem of learning Bayesian networks in an agnostic model
where an $\epsilon$-fraction of the samples are adversarially corrupted. Our
agnostic learning model is similar to -- in fact, stronger than -- Huber's
contamination model in robust statistics. In this work, we study the fully
observable Bern... | computer science |
12,963 | Mammalian Value Systems | cs.AI | Characterizing human values is a topic deeply interwoven with the sciences,
humanities, art, and many other human endeavors. In recent years, a number of
thinkers have argued that accelerating trends in computer science, cognitive
science, and related disciplines foreshadow the creation of intelligent
machines which me... | computer science |
12,964 | Spectral learning of dynamic systems from nonequilibrium data | cs.LG | Observable operator models (OOMs) and related models are one of the most
important and powerful tools for modeling and analyzing stochastic systems.
They exactly describe dynamics of finite-rank systems and can be efficiently
and consistently estimated through spectral learning under the assumption of
identically distr... | computer science |
12,965 | Learn&Fuzz: Machine Learning for Input Fuzzing | cs.AI | Fuzzing consists of repeatedly testing an application with modified, or
fuzzed, inputs with the goal of finding security vulnerabilities in
input-parsing code. In this paper, we show how to automate the generation of an
input grammar suitable for input fuzzing using sample inputs and
neural-network-based statistical ma... | computer science |
12,966 | Robust Budget Allocation via Continuous Submodular Functions | cs.LG | The optimal allocation of resources for maximizing influence, spread of
information or coverage, has gained attention in the past years, in particular
in machine learning and data mining. But in applications, the parameters of the
problem are rarely known exactly, and using wrong parameters can lead to
undesirable outc... | computer science |
12,967 | Guarantees for Greedy Maximization of Non-submodular Functions with
Applications | cs.DM | We investigate the performance of the standard Greedy algorithm for
cardinality constrained maximization of non-submodular nondecreasing set
functions. While there are strong theoretical guarantees on the performance of
Greedy for maximizing submodular functions, there are few guarantees for
non-submodular ones. Howeve... | computer science |
12,968 | A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular
Communications | cs.ET | This work presents a new multi-chemical experimental platform for molecular
communication where the transmitter can release different chemicals. This
platform is designed to be inexpensive and accessible, and it can be expanded
to simulate different environments including the cardiovascular system and
complex network o... | computer science |
12,969 | Optimization by a quantum reinforcement algorithm | cs.AI | A reinforcement algorithm solves a classical optimization problem by
introducing a feedback to the system which slowly changes the energy landscape
and converges the algorithm to an optimal solution in the configuration space.
Here, we use this strategy to concentrate (localize) preferentially the wave
function of a qu... | computer science |
12,970 | Fairness Testing: Testing Software for Discrimination | cs.SE | This paper defines software fairness and discrimination and develops a
testing-based method for measuring if and how much software discriminates,
focusing on causality in discriminatory behavior. Evidence of software
discrimination has been found in modern software systems that recommend
criminal sentences, grant acces... | computer science |
12,971 | Learners that Use Little Information | cs.LG | We study learning algorithms that are restricted to using a small amount of
information from their input sample. We introduce a category of learning
algorithms we term $d$-bit information learners, which are algorithms whose
output conveys at most $d$ bits of information of their input. A central theme
in this work is ... | computer science |
12,972 | Discovering More Precise Process Models from Event Logs by Filtering Out
Chaotic Activities | cs.DB | Process Discovery is concerned with the automatic generation of a process
model that describes a business process from execution data of that business
process. Real life event logs can contain chaotic activities. These activities
are independent of the state of the process and can, therefore, happen at
rather arbitrary... | computer science |
12,973 | PFAx: Predictable Feature Analysis to Perform Control | cs.LG | Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is an
algorithm that performs dimensionality reduction on high dimensional input
signal. It extracts those subsignals that are most predictable according to a
certain prediction model. We refer to these extracted signals as predictable
features.
In ... | computer science |
12,974 | Inverse Reinforcement Learning for Marketing | cs.AI | Learning customer preferences from an observed behaviour is an important
topic in the marketing literature. Structural models typically model
forward-looking customers or firms as utility-maximizing agents whose utility
is estimated using methods of Stochastic Optimal Control. We suggest an
alternative approach to stud... | computer science |
12,975 | DataBright: Towards a Global Exchange for Decentralized Data Ownership
and Trusted Computation | cs.ET | It is safe to assume that, for the foreseeable future, machine learning,
especially deep learning will remain both data- and computation-hungry. In this
paper, we ask: Can we build a global exchange where everyone can contribute
computation and data to train the next generation of machine learning
applications?
We pr... | computer science |
12,976 | A Geometric Method to Obtain the Generation Probability of a Sentence | cs.CL | "How to generate a sentence" is the most critical and difficult problem in
all the natural language processing technologies. In this paper, we present a
new approach to explain the generation process of a sentence from the
perspective of mathematics. Our method is based on the premise that in our
brain a sentence is a ... | computer science |
12,977 | The Loss Rank Principle for Model Selection | math.ST | We introduce a new principle for model selection in regression and
classification. Many regression models are controlled by some smoothness or
flexibility or complexity parameter c, e.g. the number of neighbors to be
averaged over in k nearest neighbor (kNN) regression or the polynomial degree
in regression with polyno... | computer science |
12,978 | Getting started in probabilistic graphical models | cs.LG | Probabilistic graphical models (PGMs) have become a popular tool for
computational analysis of biological data in a variety of domains. But, what
exactly are they and how do they work? How can we use PGMs to discover patterns
that are biologically relevant? And to what extent can PGMs help us formulate
new hypotheses t... | computer science |
12,979 | Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^-
Halflives | cs.LG | Statistical modeling of nuclear data provides a novel approach to nuclear
systematics complementary to established theoretical and phenomenological
approaches based on quantum theory. Continuing previous studies in which global
statistical modeling is pursued within the general framework of machine
learning theory, we ... | computer science |
12,980 | A survey of statistical network models | stat.ME | Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs dat... | computer science |
12,981 | Smoothing proximal gradient method for general structured sparse
regression | stat.ML | We study the problem of estimating high-dimensional regression models
regularized by a structured sparsity-inducing penalty that encodes prior
structural information on either the input or output variables. We consider two
widely adopted types of penalties of this kind as motivating examples: (1) the
general overlappin... | computer science |
12,982 | Tight Sample Complexity of Large-Margin Learning | cs.LG | We obtain a tight distribution-specific characterization of the sample
complexity of large-margin classification with L_2 regularization: We introduce
the \gamma-adapted-dimension, which is a simple function of the spectrum of a
distribution's covariance matrix, and show distribution-specific upper and
lower bounds on ... | computer science |
12,983 | Sparse Bayesian Methods for Low-Rank Matrix Estimation | stat.ML | Recovery of low-rank matrices has recently seen significant activity in many
areas of science and engineering, motivated by recent theoretical results for
exact reconstruction guarantees and interesting practical applications. A
number of methods have been developed for this recovery problem. However, a
principled meth... | computer science |
12,984 | Collaborative Filtering via Group-Structured Dictionary Learning | math.OC | Structured sparse coding and the related structured dictionary learning
problems are novel research areas in machine learning. In this paper we present
a new application of structured dictionary learning for collaborative filtering
based recommender systems. Our extensive numerical experiments demonstrate that
the pres... | computer science |
12,985 | Identification of relevant subtypes via preweighted sparse clustering | stat.ME | Cluster analysis methods are used to identify homogeneous subgroups in a data
set. In biomedical applications, one frequently applies cluster analysis in
order to identify biologically interesting subgroups. In particular, one may
wish to identify subgroups that are associated with a particular outcome of
interest. Con... | computer science |
12,986 | Spectral Compressed Sensing via Structured Matrix Completion | cs.IT | The paper studies the problem of recovering a spectrally sparse object from a
small number of time domain samples. Specifically, the object of interest with
ambient dimension $n$ is assumed to be a mixture of $r$ complex
multi-dimensional sinusoids, while the underlying frequencies can assume any
value in the unit disk... | computer science |
12,987 | Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau
Functional Minimization | stat.ML | We present a graph-based variational algorithm for classification of
high-dimensional data, generalizing the binary diffuse interface model to the
case of multiple classes. Motivated by total variation techniques, the method
involves minimizing an energy functional made up of three terms. The first two
terms promote a ... | computer science |
12,988 | A Randomized Nonmonotone Block Proximal Gradient Method for a Class of
Structured Nonlinear Programming | math.OC | We propose a randomized nonmonotone block proximal gradient (RNBPG) method
for minimizing the sum of a smooth (possibly nonconvex) function and a
block-separable (possibly nonconvex nonsmooth) function. At each iteration,
this method randomly picks a block according to any prescribed probability
distribution and solves... | computer science |
12,989 | Understanding the Predictive Power of Computational Mechanics and Echo
State Networks in Social Media | cs.SI | There is a large amount of interest in understanding users of social media in
order to predict their behavior in this space. Despite this interest, user
predictability in social media is not well-understood. To examine this
question, we consider a network of fifteen thousand users on Twitter over a
seven week period. W... | computer science |
12,990 | Distributed Matrix Completion and Robust Factorization | cs.LG | If learning methods are to scale to the massive sizes of modern datasets, it
is essential for the field of machine learning to embrace parallel and
distributed computing. Inspired by the recent development of matrix
factorization methods with rich theory but poor computational complexity and by
the relative ease of map... | computer science |
12,991 | Compressive Network Analysis | stat.ML | Modern data acquisition routinely produces massive amounts of network data.
Though many methods and models have been proposed to analyze such data, the
research of network data is largely disconnected with the classical theory of
statistical learning and signal processing. In this paper, we present a new
framework for ... | computer science |
12,992 | Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data | stat.AP | MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play
important regulatory roles in post-transcriptional gene regulation by
inhibiting the translation of the mRNA into proteins or otherwise cleaving the
target mRNA. Inferring miRNA targets provides useful information for
understanding the roles of... | computer science |
12,993 | Mean-Field Learning: a Survey | cs.LG | In this paper we study iterative procedures for stationary equilibria in
games with large number of players. Most of learning algorithms for games with
continuous action spaces are limited to strict contraction best reply maps in
which the Banach-Picard iteration converges with geometrical convergence rate.
When the be... | computer science |
12,994 | High quality topic extraction from business news explains abnormal
financial market volatility | stat.ML | Understanding the mutual relationships between information flows and social
activity in society today is one of the cornerstones of the social sciences. In
financial economics, the key issue in this regard is understanding and
quantifying how news of all possible types (geopolitical, environmental,
social, financial, e... | computer science |
12,995 | Enhancing the functional content of protein interaction networks | cs.CE | Protein interaction networks are a promising type of data for studying
complex biological systems. However, despite the rich information embedded in
these networks, they face important data quality challenges of noise and
incompleteness that adversely affect the results obtained from their analysis.
Here, we explore th... | computer science |
12,996 | Convex Total Least Squares | stat.ML | We study the total least squares (TLS) problem that generalizes least squares
regression by allowing measurement errors in both dependent and independent
variables. TLS is widely used in applied fields including computer vision,
system identification and econometrics. The special case when all dependent and
independent... | computer science |
12,997 | Inference of Sparse Networks with Unobserved Variables. Application to
Gene Regulatory Networks | stat.ML | Networks are a unifying framework for modeling complex systems and network
inference problems are frequently encountered in many fields. Here, I develop
and apply a generative approach to network inference (RCweb) for the case when
the network is sparse and the latent (not observed) variables affect the
observed ones. ... | computer science |
12,998 | Supervised classification-based stock prediction and portfolio
optimization | cs.CE | As the number of publicly traded companies as well as the amount of their
financial data grows rapidly, it is highly desired to have tracking, analysis,
and eventually stock selections automated. There have been few works focusing
on estimating the stock prices of individual companies. However, many of those
have worke... | computer science |
12,999 | Fast and Flexible ADMM Algorithms for Trend Filtering | stat.ML | This paper presents a fast and robust algorithm for trend filtering, a
recently developed nonparametric regression tool. It has been shown that, for
estimating functions whose derivatives are of bounded variation, trend
filtering achieves the minimax optimal error rate, while other popular methods
like smoothing spline... | computer science |
13,000 | Rows vs Columns for Linear Systems of Equations - Randomized Kaczmarz or
Coordinate Descent? | math.OC | This paper is about randomized iterative algorithms for solving a linear
system of equations $X \beta = y$ in different settings. Recent interest in the
topic was reignited when Strohmer and Vershynin (2009) proved the linear
convergence rate of a Randomized Kaczmarz (RK) algorithm that works on the rows
of $X$ (data p... | computer science |
13,001 | Causality Networks | cs.LG | While correlation measures are used to discern statistical relationships
between observed variables in almost all branches of data-driven scientific
inquiry, what we are really interested in is the existence of causal
dependence. Designing an efficient causality test, that may be carried out in
the absence of restricti... | computer science |
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