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32,702 | A Gaussian Particle Filter Approach for Sensors to Track Multiple Moving
Targets | cs.LG | In a variety of problems, the number and state of multiple moving targets are
unknown and are subject to be inferred from their measurements obtained by a
sensor with limited sensing ability. This type of problems is raised in a
variety of applications, including monitoring of endangered species, cleaning,
and surveill... | computer science |
32,703 | Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC
dimension | cs.LG | The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning
machine, and a low VC dimension leads to good generalization. The recently
proposed Minimal Complexity Machine (MCM) learns a hyperplane classifier by
minimizing an exact bound on the VC dimension. This paper extends the MCM
classifier to the ... | computer science |
32,704 | Max-Cost Discrete Function Evaluation Problem under a Budget | cs.LG | We propose novel methods for max-cost Discrete Function Evaluation Problem
(DFEP) under budget constraints. We are motivated by applications such as
clinical diagnosis where a patient is subjected to a sequence of (possibly
expensive) tests before a decision is made. Our goal is to develop strategies
for minimizing max... | computer science |
32,705 | Deep Learning with Nonparametric Clustering | cs.LG | Clustering is an essential problem in machine learning and data mining. One
vital factor that impacts clustering performance is how to learn or design the
data representation (or features). Fortunately, recent advances in deep
learning can learn unsupervised features effectively, and have yielded state of
the art perfo... | computer science |
32,706 | Classification with Low Rank and Missing Data | cs.LG | We consider classification and regression tasks where we have missing data
and assume that the (clean) data resides in a low rank subspace. Finding a
hidden subspace is known to be computationally hard. Nevertheless, using a
non-proper formulation we give an efficient agnostic algorithm that classifies
as good as the b... | computer science |
32,707 | A Proximal Approach for Sparse Multiclass SVM | cs.LG | Sparsity-inducing penalties are useful tools to design multiclass support
vector machines (SVMs). In this paper, we propose a convex optimization
approach for efficiently and exactly solving the multiclass SVM learning
problem involving a sparse regularization and the multiclass hinge loss
formulated by Crammer and Sin... | computer science |
32,708 | Multi-view learning for multivariate performance measures optimization | cs.LG | In this paper, we propose the problem of optimizing multivariate performance
measures from multi-view data, and an effective method to solve it. This
problem has two features: the data points are presented by multiple views, and
the target of learning is to optimize complex multivariate performance
measures. We propose... | computer science |
32,709 | Generalised Random Forest Space Overview | cs.LG | Assuming a view of the Random Forest as a special case of a nested ensemble
of interchangeable modules, we construct a generalisation space allowing one to
easily develop novel methods based on this algorithm. We discuss the role and
required properties of modules at each level, especially in context of some
already pr... | computer science |
32,710 | Comment on "Clustering by fast search and find of density peaks" | cs.LG | In [1], a clustering algorithm was given to find the centers of clusters
quickly. However, the accuracy of this algorithm heavily depend on the
threshold value of d-c. Furthermore, [1] has not provided any efficient way to
select the threshold value of d-c, that is, one can have to estimate the value
of d_c depend on o... | computer science |
32,711 | Regularized maximum correntropy machine | cs.LG | In this paper we investigate the usage of regularized correntropy framework
for learning of classifiers from noisy labels. The class label predictors
learned by minimizing transitional loss functions are sensitive to the noisy
and outlying labels of training samples, because the transitional loss
functions are equally ... | computer science |
32,712 | Extreme Entropy Machines: Robust information theoretic classification | cs.LG | Most of the existing classification methods are aimed at minimization of
empirical risk (through some simple point-based error measured with loss
function) with added regularization. We propose to approach this problem in a
more information theoretic way by investigating applicability of entropy
measures as a classific... | computer science |
32,713 | Deep Transductive Semi-supervised Maximum Margin Clustering | cs.LG | Semi-supervised clustering is an very important topic in machine learning and
computer vision. The key challenge of this problem is how to learn a metric,
such that the instances sharing the same label are more likely close to each
other on the embedded space. However, little attention has been paid to learn
better rep... | computer science |
32,714 | On a Family of Decomposable Kernels on Sequences | cs.LG | In many applications data is naturally presented in terms of orderings of
some basic elements or symbols. Reasoning about such data requires a notion of
similarity capable of handling sequences of different lengths. In this paper we
describe a family of Mercer kernel functions for such sequentially structured
data. The... | computer science |
32,715 | Compressed Support Vector Machines | cs.LG | Support vector machines (SVM) can classify data sets along highly non-linear
decision boundaries because of the kernel-trick. This expressiveness comes at a
price: During test-time, the SVM classifier needs to compute the kernel
inner-product between a test sample and all support vectors. With large
training data sets,... | computer science |
32,716 | Novel Approaches for Predicting Risk Factors of Atherosclerosis | cs.LG | Coronary heart disease (CHD) caused by hardening of artery walls due to
cholesterol known as atherosclerosis is responsible for large number of deaths
world-wide. The disease progression is slow, asymptomatic and may lead to
sudden cardiac arrest, stroke or myocardial infraction. Presently, imaging
techniques are being... | computer science |
32,717 | Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation | cs.LG | Applications involving dictionary learning, non-negative matrix
factorization, subspace clustering, and parallel factor tensor decomposition
tasks motivate well algorithms for per-block-convex and non-smooth optimization
problems. By leveraging the stochastic approximation paradigm and first-order
acceleration schemes,... | computer science |
32,718 | Efficient Divide-And-Conquer Classification Based on Feature-Space
Decomposition | cs.LG | This study presents a divide-and-conquer (DC) approach based on feature space
decomposition for classification. When large-scale datasets are present,
typical approaches usually employed truncated kernel methods on the feature
space or DC approaches on the sample space. However, this did not guarantee
separability betw... | computer science |
32,719 | Representing Objects, Relations, and Sequences | cs.LG | Vector Symbolic Architectures (VSAs) are high-dimensional vector
representations of objects (eg., words, image parts), relations (eg., sentence
structures), and sequences for use with machine learning algorithms. They
consist of a vector addition operator for representing a collection of
unordered objects, a Binding op... | computer science |
32,720 | Unsupervised Feature Selection with Adaptive Structure Learning | cs.LG | The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic ... | computer science |
32,721 | The Child is Father of the Man: Foresee the Success at the Early Stage | cs.LG | Understanding the dynamic mechanisms that drive the high-impact scientific
work (e.g., research papers, patents) is a long-debated research topic and has
many important implications, ranging from personal career development and
recruitment search, to the jurisdiction of research resources. Recent advances
in characteri... | computer science |
32,722 | EM-Based Channel Estimation from Crowd-Sourced RSSI Samples Corrupted by
Noise and Interference | cs.LG | We propose a method for estimating channel parameters from RSSI measurements
and the lost packet count, which can work in the presence of losses due to both
interference and signal attenuation below the noise floor. This is especially
important in the wireless networks, such as vehicular, where propagation model
change... | computer science |
32,723 | PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent | cs.LG | Stochastic Dual Coordinate Descent (SDCD) has become one of the most
efficient ways to solve the family of $\ell_2$-regularized empirical risk
minimization problems, including linear SVM, logistic regression, and many
others. The vanilla implementation of DCD is quite slow; however, by
maintaining primal variables whil... | computer science |
32,724 | Autonomous CRM Control via CLV Approximation with Deep Reinforcement
Learning in Discrete and Continuous Action Space | cs.LG | The paper outlines a framework for autonomous control of a CRM (customer
relationship management) system. First, it explores how a modified version of
the widely accepted Recency-Frequency-Monetary Value system of metrics can be
used to define the state space of clients or donors. Second, it describes a
procedure to de... | computer science |
32,725 | Data Mining for Prediction of Human Performance Capability in the
Software-Industry | cs.LG | The recruitment of new personnel is one of the most essential business
processes which affect the quality of human capital within any company. It is
highly essential for the companies to ensure the recruitment of right talent to
maintain a competitive edge over the others in the market. However IT companies
often face ... | computer science |
32,726 | Maximum Entropy Linear Manifold for Learning Discriminative
Low-dimensional Representation | cs.LG | Representation learning is currently a very hot topic in modern machine
learning, mostly due to the great success of the deep learning methods. In
particular low-dimensional representation which discriminates classes can not
only enhance the classification procedure, but also make it faster, while
contrary to the high-... | computer science |
32,727 | A Deep Embedding Model for Co-occurrence Learning | cs.LG | Co-occurrence Data is a common and important information source in many
areas, such as the word co-occurrence in the sentences, friends co-occurrence
in social networks and products co-occurrence in commercial transaction data,
etc, which contains rich correlation and clustering information about the
items. In this pap... | computer science |
32,728 | Classification with Extreme Learning Machine and Ensemble Algorithms
Over Randomly Partitioned Data | cs.LG | In this age of Big Data, machine learning based data mining methods are
extensively used to inspect large scale data sets. Deriving applicable
predictive modeling from these type of data sets is a challenging obstacle
because of their high complexity. Opportunity with high data availability
levels, automated classifica... | computer science |
32,729 | Convex Learning of Multiple Tasks and their Structure | cs.LG | Reducing the amount of human supervision is a key problem in machine learning
and a natural approach is that of exploiting the relations (structure) among
different tasks. This is the idea at the core of multi-task learning. In this
context a fundamental question is how to incorporate the tasks structure in the
learnin... | computer science |
32,730 | Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and
Beyond | cs.LG | In this paper, we consider the distributed stochastic multi-armed bandit
problem, where a global arm set can be accessed by multiple players
independently. The players are allowed to exchange their history of
observations with each other at specific points in time. We study the
relationship between regret and communica... | computer science |
32,731 | Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients | cs.LG | Nonlinear component analysis such as kernel Principle Component Analysis
(KPCA) and kernel Canonical Correlation Analysis (KCCA) are widely used in
machine learning, statistics and data analysis, but they can not scale up to
big datasets. Recent attempts have employed random feature approximations to
convert the proble... | computer science |
32,732 | Linear Maximum Margin Classifier for Learning from Uncertain Data | cs.LG | In this paper, we propose a maximum margin classifier that deals with
uncertainty in data input. More specifically, we reformulate the SVM framework
such that each training example can be modeled by a multi-dimensional Gaussian
distribution described by its mean vector and its covariance matrix -- the
latter modeling t... | computer science |
32,733 | The Nataf-Beta Random Field Classifier: An Extension of the Beta
Conjugate Prior to Classification Problems | cs.LG | This paper presents the Nataf-Beta Random Field Classifier, a discriminative
approach that extends the applicability of the Beta conjugate prior to
classification problems. The approach's key feature is to model the probability
of a class conditional on attribute values as a random field whose marginals
are Beta distri... | computer science |
32,734 | Performance Evaluation of Machine Learning Algorithms in Post-operative
Life Expectancy in the Lung Cancer Patients | cs.LG | The nature of clinical data makes it difficult to quickly select, tune and
apply machine learning algorithms to clinical prognosis. As a result, a lot of
time is spent searching for the most appropriate machine learning algorithms
applicable in clinical prognosis that contains either binary-valued or
multi-valued attri... | computer science |
32,735 | Instance Optimal Learning | cs.LG | We consider the following basic learning task: given independent draws from
an unknown distribution over a discrete support, output an approximation of the
distribution that is as accurate as possible in $\ell_1$ distance (i.e. total
variation or statistical distance). Perhaps surprisingly, it is often possible
to "de-... | computer science |
32,736 | Effective Discriminative Feature Selection with Non-trivial Solutions | cs.LG | Feature selection and feature transformation, the two main ways to reduce
dimensionality, are often presented separately. In this paper, a feature
selection method is proposed by combining the popular transformation based
dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity
regularization. We... | computer science |
32,737 | Temporal-Difference Networks | cs.LG | We introduce a generalization of temporal-difference (TD) learning to
networks of interrelated predictions. Rather than relating a single prediction
to itself at a later time, as in conventional TD methods, a TD network relates
each prediction in a set of predictions to other predictions in the set at a
later time. TD ... | computer science |
32,738 | Discriminative Switching Linear Dynamical Systems applied to
Physiological Condition Monitoring | cs.LG | We present a Discriminative Switching Linear Dynamical System (DSLDS) applied
to patient monitoring in Intensive Care Units (ICUs). Our approach is based on
identifying the state-of-health of a patient given their observed vital signs
using a discriminative classifier, and then inferring their underlying
physiological ... | computer science |
32,739 | Online Convex Optimization Using Predictions | cs.LG | Making use of predictions is a crucial, but under-explored, area of online
algorithms. This paper studies a class of online optimization problems where we
have external noisy predictions available. We propose a stochastic prediction
error model that generalizes prior models in the learning and stochastic
control commun... | computer science |
32,740 | Random Forest for the Contextual Bandit Problem - extended version | cs.LG | To address the contextual bandit problem, we propose an online random forest
algorithm. The analysis of the proposed algorithm is based on the sample
complexity needed to find the optimal decision stump. Then, the decision stumps
are assembled in a random collection of decision trees, Bandit Forest. We show
that the pr... | computer science |
32,741 | Accelerated kernel discriminant analysis | cs.LG | In this paper, using a novel matrix factorization and simultaneous reduction
to diagonal form approach (or in short simultaneous reduction approach),
Accelerated Kernel Discriminant Analysis (AKDA) and Accelerated Kernel Subclass
Discriminant Analysis (AKSDA) are proposed. Specifically, instead of performing
the simult... | computer science |
32,742 | Surrogate regret bounds for generalized classification performance
metrics | cs.LG | We consider optimization of generalized performance metrics for binary
classification by means of surrogate losses. We focus on a class of metrics,
which are linear-fractional functions of the false positive and false negative
rates (examples of which include $F_{\beta}$-measure, Jaccard similarity
coefficient, AM meas... | computer science |
32,743 | Or's of And's for Interpretable Classification, with Application to
Context-Aware Recommender Systems | cs.LG | We present a machine learning algorithm for building classifiers that are
comprised of a small number of disjunctions of conjunctions (or's of and's). An
example of a classifier of this form is as follows: If X satisfies (x1 = 'blue'
AND x3 = 'middle') OR (x1 = 'blue' AND x2 = '<15') OR (x1 = 'yellow'), then we
predict... | computer science |
32,744 | Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems | cs.LG | We analyze the problem of using Explore-Exploit techniques to improve
precision in multi-result ranking systems such as web search, query
autocompletion and news recommendation. Adopting an exploration policy directly
online, without understanding its impact on the production system, may have
unwanted consequences - th... | computer science |
32,745 | Learning Contextualized Music Semantics from Tags via a Siamese Network | cs.LG | Music information retrieval faces a challenge in modeling contextualized
musical concepts formulated by a set of co-occurring tags. In this paper, we
investigate the suitability of our recently proposed approach based on a
Siamese neural network in fighting off this challenge. By means of tag features
and probabilistic... | computer science |
32,746 | Note on Equivalence Between Recurrent Neural Network Time Series Models
and Variational Bayesian Models | cs.LG | We observe that the standard log likelihood training objective for a
Recurrent Neural Network (RNN) model of time series data is equivalent to a
variational Bayesian training objective, given the proper choice of generative
and inference models. This perspective may motivate extensions to both RNNs and
variational Baye... | computer science |
32,747 | Copeland Dueling Bandits | cs.LG | A version of the dueling bandit problem is addressed in which a Condorcet
winner may not exist. Two algorithms are proposed that instead seek to minimize
regret with respect to the Copeland winner, which, unlike the Condorcet winner,
is guaranteed to exist. The first, Copeland Confidence Bound (CCB), is designed
for sm... | computer science |
32,748 | Unsupervised Learning on Neural Network Outputs: with Application in
Zero-shot Learning | cs.LG | The outputs of a trained neural network contain much richer information than
just an one-hot classifier. For example, a neural network might give an image
of a dog the probability of one in a million of being a cat but it is still
much larger than the probability of being a car. To reveal the hidden structure
in them, ... | computer science |
32,749 | Global and Local Structure Preserving Sparse Subspace Learning: An
Iterative Approach to Unsupervised Feature Selection | cs.LG | As we aim at alleviating the curse of high-dimensionality, subspace learning
is becoming more popular. Existing approaches use either information about
global or local structure of the data, and few studies simultaneously focus on
global and local structures as the both of them contain important information.
In this pa... | computer science |
32,750 | On bicluster aggregation and its benefits for enumerative solutions | cs.LG | Biclustering involves the simultaneous clustering of objects and their
attributes, thus defining local two-way clustering models. Recently, efficient
algorithms were conceived to enumerate all biclusters in real-valued datasets.
In this case, the solution composes a complete set of maximal and non-redundant
biclusters.... | computer science |
32,751 | Towards Structured Deep Neural Network for Automatic Speech Recognition | cs.LG | In this paper we propose the Structured Deep Neural Network (Structured DNN)
as a structured and deep learning algorithm, learning to find the best
structured object (such as a label sequence) given a structured input (such as
a vector sequence) by globally considering the mapping relationships between
the structure ra... | computer science |
32,752 | Unsupervised Feature Analysis with Class Margin Optimization | cs.LG | Unsupervised feature selection has been always attracting research attention
in the communities of machine learning and data mining for decades. In this
paper, we propose an unsupervised feature selection method seeking a feature
coefficient matrix to select the most distinctive features. Specifically, our
proposed alg... | computer science |
32,753 | Exploiting an Oracle that Reports AUC Scores in Machine Learning
Contests | cs.LG | In machine learning contests such as the ImageNet Large Scale Visual
Recognition Challenge and the KDD Cup, contestants can submit candidate
solutions and receive from an oracle (typically the organizers of the
competition) the accuracy of their guesses compared to the ground-truth labels.
One of the most commonly used... | computer science |
32,754 | Semidefinite and Spectral Relaxations for Multi-Label Classification | cs.LG | In this paper, we address the problem of multi-label classification. We
consider linear classifiers and propose to learn a prior over the space of
labels to directly leverage the performance of such methods. This prior takes
the form of a quadratic function of the labels and permits to encode both
attractive and repuls... | computer science |
32,755 | Learning Multiple Tasks with Multilinear Relationship Networks | cs.LG | Deep networks trained on large-scale data can learn transferable features to
promote learning multiple tasks. Since deep features eventually transition from
general to specific along deep networks, a fundamental problem of multi-task
learning is how to exploit the task relatedness underlying parameter tensors
and impro... | computer science |
32,756 | A Recurrent Latent Variable Model for Sequential Data | cs.LG | In this paper, we explore the inclusion of latent random variables into the
dynamic hidden state of a recurrent neural network (RNN) by combining elements
of the variational autoencoder. We argue that through the use of high-level
latent random variables, the variational RNN (VRNN)1 can model the kind of
variability ob... | computer science |
32,757 | Efficient Learning of Ensembles with QuadBoost | cs.LG | We first present a general risk bound for ensembles that depends on the Lp
norm of the weighted combination of voters which can be selected from a
continuous set. We then propose a boosting method, called QuadBoost, which is
strongly supported by the general risk bound and has very simple rules for
assigning the voters... | computer science |
32,758 | On Convergence of Emphatic Temporal-Difference Learning | cs.LG | We consider emphatic temporal-difference learning algorithms for policy
evaluation in discounted Markov decision processes with finite spaces. Such
algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an
improved solution to the problem of divergence of off-policy
temporal-difference learning with ... | computer science |
32,759 | Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection | cs.LG | In this paper, a novel framework of sparse kernel learning for Support Vector
Data Description (SVDD) based anomaly detection is presented. In this work,
optimal sparse feature selection for anomaly detection is first modeled as a
Mixed Integer Programming (MIP) problem. Due to the prohibitively high
computational comp... | computer science |
32,760 | On the Interpretability of Conditional Probability Estimates in the
Agnostic Setting | cs.LG | We study the interpretability of conditional probability estimates for binary
classification under the agnostic setting or scenario. Under the agnostic
setting, conditional probability estimates do not necessarily reflect the true
conditional probabilities. Instead, they have a certain calibration property:
among all d... | computer science |
32,761 | Max-Entropy Feed-Forward Clustering Neural Network | cs.LG | The outputs of non-linear feed-forward neural network are positive, which
could be treated as probability when they are normalized to one. If we take
Entropy-Based Principle into consideration, the outputs for each sample could
be represented as the distribution of this sample for different clusters.
Entropy-Based Prin... | computer science |
32,762 | Margin-Based Feed-Forward Neural Network Classifiers | cs.LG | Margin-Based Principle has been proposed for a long time, it has been proved
that this principle could reduce the structural risk and improve the
performance in both theoretical and practical aspects. Meanwhile, feed-forward
neural network is a traditional classifier, which is very hot at present with a
deeper architec... | computer science |
32,763 | On the Equivalence of CoCoA+ and DisDCA | cs.LG | In this document, we show that the algorithm CoCoA+ (Ma et al., ICML, 2015)
under the setting used in their experiments, which is also the best setting
suggested by the authors that proposed this algorithm, is equivalent to the
practical variant of DisDCA (Yang, NIPS, 2013). | computer science |
32,764 | Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to
Novel Algorithms | cs.LG | This paper studies the generalization performance of multi-class
classification algorithms, for which we obtain, for the first time, a
data-dependent generalization error bound with a logarithmic dependence on the
class size, substantially improving the state-of-the-art linear dependence in
the existing data-dependent ... | computer science |
32,765 | Localized Multiple Kernel Learning---A Convex Approach | cs.LG | We propose a localized approach to multiple kernel learning that can be
formulated as a convex optimization problem over a given cluster structure. For
which we obtain generalization error guarantees and derive an optimization
algorithm based on the Fenchel dual representation. Experiments on real-world
datasets from t... | computer science |
32,766 | A Fast Incremental Gaussian Mixture Model | cs.LG | This work builds upon previous efforts in online incremental learning, namely
the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of
learning from data streams in a single-pass by improving its model after
analyzing each data point and discarding it thereafter. Nevertheless, it
suffers from the scalabi... | computer science |
32,767 | Dual Memory Architectures for Fast Deep Learning of Stream Data via an
Online-Incremental-Transfer Strategy | cs.LG | The online learning of deep neural networks is an interesting problem of
machine learning because, for example, major IT companies want to manage the
information of the massive data uploaded on the web daily, and this technology
can contribute to the next generation of lifelong learning. We aim to train
deep models fro... | computer science |
32,768 | Learning Deep Generative Models with Doubly Stochastic MCMC | cs.LG | We present doubly stochastic gradient MCMC, a simple and generic method for
(approximate) Bayesian inference of deep generative models (DGMs) in a
collapsed continuous parameter space. At each MCMC sampling step, the algorithm
randomly draws a mini-batch of data samples to estimate the gradient of
log-posterior and fur... | computer science |
32,769 | Latent Regression Bayesian Network for Data Representation | cs.LG | Deep directed generative models have attracted much attention recently due to
their expressive representation power and the ability of ancestral sampling.
One major difficulty of learning directed models with many latent variables is
the intractable inference. To address this problem, most existing algorithms
make assu... | computer science |
32,770 | Cheap Bandits | cs.LG | We consider stochastic sequential learning problems where the learner can
observe the \textit{average reward of several actions}. Such a setting is
interesting in many applications involving monitoring and surveillance, where
the set of the actions to observe represent some (geographical) area. The
importance of this s... | computer science |
32,771 | Online Gradient Boosting | cs.LG | We extend the theory of boosting for regression problems to the online
learning setting. Generalizing from the batch setting for boosting, the notion
of a weak learning algorithm is modeled as an online learning algorithm with
linear loss functions that competes with a base class of regression functions,
while a strong... | computer science |
32,772 | Learning with Clustering Structure | cs.LG | We study supervised learning problems using clustering constraints to impose
structure on either features or samples, seeking to help both prediction and
interpretation. The problem of clustering features arises naturally in text
classification for instance, to reduce dimensionality by grouping words
together and ident... | computer science |
32,773 | Numeric Input Relations for Relational Learning with Applications to
Community Structure Analysis | cs.LG | Most work in the area of statistical relational learning (SRL) is focussed on
discrete data, even though a few approaches for hybrid SRL models have been
proposed that combine numerical and discrete variables. In this paper we
distinguish numerical random variables for which a probability distribution is
defined by the... | computer science |
32,774 | On the Depth of Deep Neural Networks: A Theoretical View | cs.LG | People believe that depth plays an important role in success of deep neural
networks (DNN). However, this belief lacks solid theoretical justifications as
far as we know. We investigate role of depth from perspective of margin bound.
In margin bound, expected error is upper bounded by empirical margin error plus
Radema... | computer science |
32,775 | Gradient Estimation Using Stochastic Computation Graphs | cs.LG | In a variety of problems originating in supervised, unsupervised, and
reinforcement learning, the loss function is defined by an expectation over a
collection of random variables, which might be part of a probabilistic model or
the external world. Estimating the gradient of this loss function, using
samples, lies at th... | computer science |
32,776 | Scalable Semi-Supervised Aggregation of Classifiers | cs.LG | We present and empirically evaluate an efficient algorithm that learns to
aggregate the predictions of an ensemble of binary classifiers. The algorithm
uses the structure of the ensemble predictions on unlabeled data to yield
significant performance improvements. It does this without making assumptions
on the structure... | computer science |
32,777 | The Extreme Value Machine | cs.LG | It is often desirable to be able to recognize when inputs to a recognition
function learned in a supervised manner correspond to classes unseen at
training time. With this ability, new class labels could be assigned to these
inputs by a human operator, allowing them to be incorporated into the
recognition function --- ... | computer science |
32,778 | Strategic Classification | cs.LG | Machine learning relies on the assumption that unseen test instances of a
classification problem follow the same distribution as observed training data.
However, this principle can break down when machine learning is used to make
important decisions about the welfare (employment, education, health) of
strategic individ... | computer science |
32,779 | Unconfused ultraconservative multiclass algorithms | cs.LG | We tackle the problem of learning linear classifiers from noisy datasets in a
multiclass setting. The two-class version of this problem was studied a few
years ago where the proposed approaches to combat the noise revolve around a
Per-ceptron learning scheme fed with peculiar examples computed through a
weighted averag... | computer science |
32,780 | Flexible Multi-layer Sparse Approximations of Matrices and Applications | cs.LG | The computational cost of many signal processing and machine learning
techniques is often dominated by the cost of applying certain linear operators
to high-dimensional vectors. This paper introduces an algorithm aimed at
reducing the complexity of applying linear operators in high dimension by
approximately factorizin... | computer science |
32,781 | Splash: User-friendly Programming Interface for Parallelizing Stochastic
Algorithms | cs.LG | Stochastic algorithms are efficient approaches to solving machine learning
and optimization problems. In this paper, we propose a general framework called
Splash for parallelizing stochastic algorithms on multi-node distributed
systems. Splash consists of a programming interface and an execution engine.
Using the progr... | computer science |
32,782 | Conservativeness of untied auto-encoders | cs.LG | We discuss necessary and sufficient conditions for an auto-encoder to define
a conservative vector field, in which case it is associated with an energy
function akin to the unnormalized log-probability of the data. We show that the
conditions for conservativeness are more general than for encoder and decoder
weights to... | computer science |
32,783 | Occam's Gates | cs.LG | We present a complimentary objective for training recurrent neural networks
(RNN) with gating units that helps with regularization and interpretability of
the trained model. Attention-based RNN models have shown success in many
difficult sequence to sequence classification problems with long and short term
dependencies... | computer science |
32,784 | Non-convex Regularizations for Feature Selection in Ranking With Sparse
SVM | cs.LG | Feature selection in learning to rank has recently emerged as a crucial
issue. Whereas several preprocessing approaches have been proposed, only a few
works have been focused on integrating the feature selection into the learning
process. In this work, we propose a general framework for feature selection in
learning to... | computer science |
32,785 | Optimal Transport for Domain Adaptation | cs.LG | Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another ob... | computer science |
32,786 | Combining Models of Approximation with Partial Learning | cs.LG | In Gold's framework of inductive inference, the model of partial learning
requires the learner to output exactly one correct index for the target object
and only the target object infinitely often. Since infinitely many of the
learner's hypotheses may be incorrect, it is not obvious whether a partial
learner can be mod... | computer science |
32,787 | A Simple Algorithm for Maximum Margin Classification, Revisited | cs.LG | In this note, we revisit the algorithm of Har-Peled et. al. [HRZ07] for
computing a linear maximum margin classifier. Our presentation is self
contained, and the algorithm itself is slightly simpler than the original
algorithm. The algorithm itself is a simple Perceptron like iterative
algorithm. For more details and b... | computer science |
32,788 | A Bayesian Approach for Online Classifier Ensemble | cs.LG | We propose a Bayesian approach for recursively estimating the classifier
weights in online learning of a classifier ensemble. In contrast with past
methods, such as stochastic gradient descent or online boosting, our approach
estimates the weights by recursively updating its posterior distribution. For a
specified clas... | computer science |
32,789 | An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams
of Graphs | cs.LG | Kernel methods are considered an effective technique for on-line learning.
Many approaches have been developed for compactly representing the dual
solution of a kernel method when the problem imposes memory constraints.
However, in literature no work is specifically tailored to streams of graphs.
Motivated by the fact ... | computer science |
32,790 | Extending local features with contextual information in graph kernels | cs.LG | Graph kernels are usually defined in terms of simpler kernels over local
substructures of the original graphs. Different kernels consider different
types of substructures. However, in some cases they have similar predictive
performances, probably because the substructures can be interpreted as
approximations of the sub... | computer science |
32,791 | Utility-based Dueling Bandits as a Partial Monitoring Game | cs.LG | Partial monitoring is a generic framework for sequential decision-making with
incomplete feedback. It encompasses a wide class of problems such as dueling
bandits, learning with expect advice, dynamic pricing, dark pools, and label
efficient prediction. We study the utility-based dueling bandit problem as an
instance o... | computer science |
32,792 | Spectral Smoothing via Random Matrix Perturbations | cs.LG | We consider stochastic smoothing of spectral functions of matrices using
perturbations commonly studied in random matrix theory. We show that a spectral
function remains spectral when smoothed using a unitarily invariant
perturbation distribution. We then derive state-of-the-art smoothing bounds for
the maximum eigenva... | computer science |
32,793 | A new boosting algorithm based on dual averaging scheme | cs.LG | The fields of machine learning and mathematical optimization increasingly
intertwined. The special topic on supervised learning and convex optimization
examines this interplay. The training part of most supervised learning
algorithms can usually be reduced to an optimization problem that minimizes a
loss between model ... | computer science |
32,794 | Cluster-Aided Mobility Predictions | cs.LG | Predicting the future location of users in wireless net- works has numerous
applications, and can help service providers to improve the quality of service
perceived by their clients. The location predictors proposed so far estimate
the next location of a specific user by inspecting the past individual
trajectories of t... | computer science |
32,795 | Ordered Decompositional DAG Kernels Enhancements | cs.LG | In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel
framework, a framework that allows the definition of graph kernels from tree
kernels, allows to easily define new state-of-the-art graph kernels. Here we
consider a fast graph kernel based on the Subtree kernel (ST), and we propose
various enhanceme... | computer science |
32,796 | Training artificial neural networks to learn a nondeterministic game | cs.LG | It is well known that artificial neural networks (ANNs) can learn
deterministic automata. Learning nondeterministic automata is another matter.
This is important because much of the world is nondeterministic, taking the
form of unpredictable or probabilistic events that must be acted upon. If ANNs
are to engage such ph... | computer science |
32,797 | Towards Predicting First Daily Departure Times: a Gaussian Modeling
Approach for Load Shift Forecasting | cs.LG | This work provides two statistical Gaussian forecasting methods for
predicting First Daily Departure Times (FDDTs) of everyday use electric
vehicles. This is important in smart grid applications to understand
disconnection times of such mobile storage units, for instance to forecast
storage of non dispatchable loads (e... | computer science |
32,798 | Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed
Bandits | cs.LG | In this paper, we study the problem of estimating uniformly well the mean
values of several distributions given a finite budget of samples. If the
variance of the distributions were known, one could design an optimal sampling
strategy by collecting a number of independent samples per distribution that is
proportional t... | computer science |
32,799 | Maximum Entropy Deep Inverse Reinforcement Learning | cs.LG | This paper presents a general framework for exploiting the representational
capacity of neural networks to approximate complex, nonlinear reward functions
in the context of solving the inverse reinforcement learning (IRL) problem. We
show in this context that the Maximum Entropy paradigm for IRL lends itself
naturally ... | computer science |
32,800 | Lower Bounds for Multi-armed Bandit with Non-equivalent Multiple Plays | cs.LG | We study the stochastic multi-armed bandit problem with non-equivalent
multiple plays where, at each step, an agent chooses not only a set of arms,
but also their order, which influences reward distribution. In several problem
formulations with different assumptions, we provide lower bounds for regret
with standard asy... | computer science |
32,801 | 2 Notes on Classes with Vapnik-Chervonenkis Dimension 1 | cs.LG | The Vapnik-Chervonenkis dimension is a combinatorial parameter that reflects
the "complexity" of a set of sets (a.k.a. concept classes). It has been
introduced by Vapnik and Chervonenkis in their seminal 1971 paper and has since
found many applications, most notably in machine learning theory and in
computational geome... | computer science |
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