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5,900 | Efficient Parametric Projection Pursuit Density Estimation | cs.LG | Product models of low dimensional experts are a powerful way to avoid the
curse of dimensionality. We present the ``under-complete product of experts'
(UPoE), where each expert models a one dimensional projection of the data. The
UPoE is fully tractable and may be interpreted as a parametric probabilistic
model for pro... | computer science |
5,901 | Boltzmann Machine Learning with the Latent Maximum Entropy Principle | cs.LG | We present a new statistical learning paradigm for Boltzmann machines based
on a new inference principle we have proposed: the latent maximum entropy
principle (LME). LME is different both from Jaynes maximum entropy principle
and from standard maximum likelihood estimation.We demonstrate the LME
principle BY deriving ... | computer science |
5,902 | Learning Measurement Models for Unobserved Variables | cs.LG | Observed associations in a database may be due in whole or part to variations
in unrecorded (latent) variables. Identifying such variables and their causal
relationships with one another is a principal goal in many scientific and
practical domains. Previous work shows that, given a partition of observed
variables such ... | computer science |
5,903 | Joint Training of Deep Boltzmann Machines | stat.ML | We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks. | computer science |
5,904 | Learning Sparse Low-Threshold Linear Classifiers | stat.ML | We consider the problem of learning a non-negative linear classifier with a
$1$-norm of at most $k$, and a fixed threshold, under the hinge-loss. This
problem generalizes the problem of learning a $k$-monotone disjunction. We
prove that we can learn efficiently in this setting, at a rate which is linear
in both $k$ and... | computer science |
5,905 | A Tutorial on Probabilistic Latent Semantic Analysis | stat.ML | In this tutorial, I will discuss the details about how Probabilistic Latent
Semantic Analysis (PLSA) is formalized and how different learning algorithms
are proposed to learn the model. | computer science |
5,906 | Bayesian Group Nonnegative Matrix Factorization for EEG Analysis | cs.LG | We propose a generative model of a group EEG analysis, based on appropriate
kernel assumptions on EEG data. We derive the variational inference update rule
using various approximation techniques. The proposed model outperforms the
current state-of-the-art algorithms in terms of common pattern extraction. The
validity o... | computer science |
5,907 | Random Spanning Trees and the Prediction of Weighted Graphs | cs.LG | We investigate the problem of sequentially predicting the binary labels on
the nodes of an arbitrary weighted graph. We show that, under a suitable
parametrization of the problem, the optimal number of prediction mistakes can
be characterized (up to logarithmic factors) by the cutsize of a random
spanning tree of the g... | computer science |
5,908 | Gaussian Process Regression with Heteroscedastic or Non-Gaussian
Residuals | stat.ML | Gaussian Process (GP) regression models typically assume that residuals are
Gaussian and have the same variance for all observations. However, applications
with input-dependent noise (heteroscedastic residuals) frequently arise in
practice, as do applications in which the residuals do not have a Gaussian
distribution. ... | computer science |
5,909 | On-line relational SOM for dissimilarity data | stat.ML | In some applications and in order to address real world situations better,
data may be more complex than simple vectors. In some examples, they can be
known through their pairwise dissimilarities only. Several variants of the Self
Organizing Map algorithm were introduced to generalize the original algorithm
to this fra... | computer science |
5,910 | Sparse Multiple Kernel Learning with Geometric Convergence Rate | cs.LG | In this paper, we study the problem of sparse multiple kernel learning (MKL),
where the goal is to efficiently learn a combination of a fixed small number of
kernels from a large pool that could lead to a kernel classifier with a small
prediction error. We develop an efficient algorithm based on the greedy
coordinate d... | computer science |
5,911 | Generalization Guarantees for a Binary Classification Framework for
Two-Stage Multiple Kernel Learning | cs.LG | We present generalization bounds for the TS-MKL framework for two stage
multiple kernel learning. We also present bounds for sparse kernel learning
formulations within the TS-MKL framework. | computer science |
5,912 | Update Rules for Parameter Estimation in Bayesian Networks | cs.LG | This paper re-examines the problem of parameter estimation in Bayesian
networks with missing values and hidden variables from the perspective of
recent work in on-line learning [Kivinen & Warmuth, 1994]. We provide a unified
framework for parameter estimation that encompasses both on-line learning,
where the model is c... | computer science |
5,913 | Models and Selection Criteria for Regression and Classification | cs.LG | When performing regression or classification, we are interested in the
conditional probability distribution for an outcome or class variable Y given a
set of explanatoryor input variables X. We consider Bayesian models for this
task. In particular, we examine a special class of models, which we call
Bayesian regression... | computer science |
5,914 | An Information-Theoretic Analysis of Hard and Soft Assignment Methods
for Clustering | cs.LG | Assignment methods are at the heart of many algorithms for unsupervised
learning and clustering - in particular, the well-known K-means and
Expectation-Maximization (EM) algorithms. In this work, we study several
different methods of assignment, including the "hard" assignments used by
K-means and the ?soft' assignment... | computer science |
5,915 | The trace norm constrained matrix-variate Gaussian process for multitask
bipartite ranking | cs.LG | We propose a novel hierarchical model for multitask bipartite ranking. The
proposed approach combines a matrix-variate Gaussian process with a generative
model for task-wise bipartite ranking. In addition, we employ a novel trace
constrained variational inference approach to impose low rank structure on the
posterior m... | computer science |
5,916 | Geometrical complexity of data approximators | stat.ML | There are many methods developed to approximate a cloud of vectors embedded
in high-dimensional space by simpler objects: starting from principal points
and linear manifolds to self-organizing maps, neural gas, elastic maps, various
types of principal curves and principal trees, and so on. For each type of
approximator... | computer science |
5,917 | On the Sample Complexity of Learning Bayesian Networks | cs.LG | In recent years there has been an increasing interest in learning Bayesian
networks from data. One of the most effective methods for learning such
networks is based on the minimum description length (MDL) principle. Previous
work has shown that this learning procedure is asymptotically successful: with
probability one,... | computer science |
5,918 | Density Ratio Hidden Markov Models | stat.ML | Hidden Markov models and their variants are the predominant sequential
classification method in such domains as speech recognition, bioinformatics and
natural language processing. Being generative rather than discriminative
models, however, their classification performance is a drawback. In this paper
we apply ideas fr... | computer science |
5,919 | Clustering validity based on the most similarity | cs.LG | One basic requirement of many studies is the necessity of classifying data.
Clustering is a proposed method for summarizing networks. Clustering methods
can be divided into two categories named model-based approaches and algorithmic
approaches. Since the most of clustering methods depend on their input
parameters, it i... | computer science |
5,920 | Metrics for Multivariate Dictionaries | cs.LG | Overcomplete representations and dictionary learning algorithms kept
attracting a growing interest in the machine learning community. This paper
addresses the emerging problem of comparing multivariate overcomplete
representations. Despite a recurrent need to rely on a distance for learning or
assessing multivariate ov... | computer science |
5,921 | Feature Multi-Selection among Subjective Features | cs.LG | When dealing with subjective, noisy, or otherwise nebulous features, the
"wisdom of crowds" suggests that one may benefit from multiple judgments of the
same feature on the same object. We give theoretically-motivated `feature
multi-selection' algorithms that choose, among a large set of candidate
features, not only wh... | computer science |
5,922 | Online Learning with Switching Costs and Other Adaptive Adversaries | cs.LG | We study the power of different types of adaptive (nonoblivious) adversaries
in the setting of prediction with expert advice, under both full-information
and bandit feedback. We measure the player's performance using a new notion of
regret, also known as policy regret, which better captures the adversary's
adaptiveness... | computer science |
5,923 | Maxout Networks | stat.ML | We consider the problem of designing models to leverage a recently introduced
approximate model averaging technique called dropout. We define a simple new
model called maxout (so named because its output is the max of a set of inputs,
and because it is a natural companion to dropout) designed to both facilitate
optimiz... | computer science |
5,924 | Breaking the Small Cluster Barrier of Graph Clustering | cs.LG | This paper investigates graph clustering in the planted cluster model in the
presence of {\em small clusters}. Traditional results dictate that for an
algorithm to provably correctly recover the clusters, {\em all} clusters must
be sufficiently large (in particular, $\tilde{\Omega}(\sqrt{n})$ where $n$ is
the number of... | computer science |
5,925 | Fast methods for denoising matrix completion formulations, with
applications to robust seismic data interpolation | stat.ML | Recent SVD-free matrix factorization formulations have enabled rank
minimization for systems with millions of rows and columns, paving the way for
matrix completion in extremely large-scale applications, such as seismic data
interpolation.
In this paper, we consider matrix completion formulations designed to hit a
ta... | computer science |
5,926 | High-Dimensional Probability Estimation with Deep Density Models | stat.ML | One of the fundamental problems in machine learning is the estimation of a
probability distribution from data. Many techniques have been proposed to study
the structure of data, most often building around the assumption that
observations lie on a lower-dimensional manifold of high probability. It has
been more difficul... | computer science |
5,927 | Accelerated Linear SVM Training with Adaptive Variable Selection
Frequencies | stat.ML | Support vector machine (SVM) training is an active research area since the
dawn of the method. In recent years there has been increasing interest in
specialized solvers for the important case of linear models. The algorithm
presented by Hsieh et al., probably best known under the name of the
"liblinear" implementation,... | computer science |
5,928 | Sparse Signal Estimation by Maximally Sparse Convex Optimization | cs.LG | This paper addresses the problem of sparsity penalized least squares for
applications in sparse signal processing, e.g. sparse deconvolution. This paper
aims to induce sparsity more strongly than L1 norm regularization, while
avoiding non-convex optimization. For this purpose, this paper describes the
design and use of... | computer science |
5,929 | A Conformal Prediction Approach to Explore Functional Data | stat.ML | This paper applies conformal prediction techniques to compute simultaneous
prediction bands and clustering trees for functional data. These tools can be
used to detect outliers and clusters. Both our prediction bands and clustering
trees provide prediction sets for the underlying stochastic process with a
guaranteed fi... | computer science |
5,930 | An Introductory Study on Time Series Modeling and Forecasting | cs.LG | Time series modeling and forecasting has fundamental importance to various
practical domains. Thus a lot of active research works is going on in this
subject during several years. Many important models have been proposed in
literature for improving the accuracy and effectiveness of time series
forecasting. The aim of t... | computer science |
5,931 | Induction of Selective Bayesian Classifiers | cs.LG | In this paper, we examine previous work on the naive Bayesian classifier and
review its limitations, which include a sensitivity to correlated features. We
respond to this problem by embedding the naive Bayesian induction scheme within
an algorithm that c arries out a greedy search through the space of features.
We hyp... | computer science |
5,932 | Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization | stat.ML | How can we correlate neural activity in the human brain as it responds to
words, with behavioral data expressed as answers to questions about these same
words? In short, we want to find latent variables, that explain both the brain
activity, as well as the behavioral responses. We show that this is an instance
of the C... | computer science |
5,933 | Bayesian Consensus Clustering | stat.ML | The task of clustering a set of objects based on multiple sources of data
arises in several modern applications. We propose an integrative statistical
model that permits a separate clustering of the objects for each data source.
These separate clusterings adhere loosely to an overall consensus clustering,
and hence the... | computer science |
5,934 | Inferring ground truth from multi-annotator ordinal data: a
probabilistic approach | stat.ML | A popular approach for large scale data annotation tasks is crowdsourcing,
wherein each data point is labeled by multiple noisy annotators. We consider
the problem of inferring ground truth from noisy ordinal labels obtained from
multiple annotators of varying and unknown expertise levels. Annotation models
for ordinal... | computer science |
5,935 | Efficient Estimation of the number of neighbours in Probabilistic K
Nearest Neighbour Classification | cs.LG | Probabilistic k-nearest neighbour (PKNN) classification has been introduced
to improve the performance of original k-nearest neighbour (KNN) classification
algorithm by explicitly modelling uncertainty in the classification of each
feature vector. However, an issue common to both KNN and PKNN is to select the
optimal n... | computer science |
5,936 | Regret Bounds for Reinforcement Learning with Policy Advice | stat.ML | In some reinforcement learning problems an agent may be provided with a set
of input policies, perhaps learned from prior experience or provided by
advisors. We present a reinforcement learning with policy advice (RLPA)
algorithm which leverages this input set and learns to use the best policy in
the set for the reinfo... | computer science |
5,937 | On the Convergence and Consistency of the Blurring Mean-Shift Process | stat.ML | The mean-shift algorithm is a popular algorithm in computer vision and image
processing. It can also be cast as a minimum gamma-divergence estimation. In
this paper we focus on the "blurring" mean shift algorithm, which is one
version of the mean-shift process that successively blurs the dataset. The
analysis of the bl... | computer science |
5,938 | Cover Tree Bayesian Reinforcement Learning | stat.ML | This paper proposes an online tree-based Bayesian approach for reinforcement
learning. For inference, we employ a generalised context tree model. This
defines a distribution on multivariate Gaussian piecewise-linear models, which
can be updated in closed form. The tree structure itself is constructed using
the cover tr... | computer science |
5,939 | Joint Topic Modeling and Factor Analysis of Textual Information and
Graded Response Data | stat.ML | Modern machine learning methods are critical to the development of
large-scale personalized learning systems that cater directly to the needs of
individual learners. The recently developed SPARse Factor Analysis (SPARFA)
framework provides a new statistical model and algorithms for machine
learning-based learning analy... | computer science |
5,940 | Calibrated Multivariate Regression with Application to Neural Semantic
Basis Discovery | stat.ML | We propose a calibrated multivariate regression method named CMR for fitting
high dimensional multivariate regression models. Compared with existing
methods, CMR calibrates regularization for each regression task with respect to
its noise level so that it simultaneously attains improved finite-sample
performance and tu... | computer science |
5,941 | On the Generalization Ability of Online Learning Algorithms for Pairwise
Loss Functions | cs.LG | In this paper, we study the generalization properties of online learning
based stochastic methods for supervised learning problems where the loss
function is dependent on more than one training sample (e.g., metric learning,
ranking). We present a generic decoupling technique that enables us to provide
Rademacher compl... | computer science |
5,942 | Learning Policies for Contextual Submodular Prediction | cs.LG | Many prediction domains, such as ad placement, recommendation, trajectory
prediction, and document summarization, require predicting a set or list of
options. Such lists are often evaluated using submodular reward functions that
measure both quality and diversity. We propose a simple, efficient, and
provably near-optim... | computer science |
5,943 | Accelerated Mini-Batch Stochastic Dual Coordinate Ascent | stat.ML | Stochastic dual coordinate ascent (SDCA) is an effective technique for
solving regularized loss minimization problems in machine learning. This paper
considers an extension of SDCA under the mini-batch setting that is often used
in practice. Our main contribution is to introduce an accelerated mini-batch
version of SDC... | computer science |
5,944 | Boosting with the Logistic Loss is Consistent | cs.LG | This manuscript provides optimization guarantees, generalization bounds, and
statistical consistency results for AdaBoost variants which replace the
exponential loss with the logistic and similar losses (specifically, twice
differentiable convex losses which are Lipschitz and tend to zero on one side).
The heart of t... | computer science |
5,945 | Horizon-Independent Optimal Prediction with Log-Loss in Exponential
Families | cs.LG | We study online learning under logarithmic loss with regular parametric
models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction
strategy with Jeffreys prior and sequential normalized maximum likelihood
(SNML) coincide and are optimal if and only if the latter is exchangeable, and
if and only if the opti... | computer science |
5,946 | Meta Path-Based Collective Classification in Heterogeneous Information
Networks | cs.LG | Collective classification has been intensively studied due to its impact in
many important applications, such as web mining, bioinformatics and citation
analysis. Collective classification approaches exploit the dependencies of a
group of linked objects whose class labels are correlated and need to be
predicted simulta... | computer science |
5,947 | Characterizing A Database of Sequential Behaviors with Latent Dirichlet
Hidden Markov Models | stat.ML | This paper proposes a generative model, the latent Dirichlet hidden Markov
models (LDHMM), for characterizing a database of sequential behaviors
(sequences). LDHMMs posit that each sequence is generated by an underlying
Markov chain process, which are controlled by the corresponding parameters
(i.e., the initial state ... | computer science |
5,948 | Normalized Online Learning | cs.LG | We introduce online learning algorithms which are independent of feature
scales, proving regret bounds dependent on the ratio of scales existent in the
data rather than the absolute scale. This has several useful effects: there is
no need to pre-normalize data, the test-time and test-space complexity are
reduced, and t... | computer science |
5,949 | Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process
Mixture | cs.LG | This paper presents a novel algorithm, based upon the dependent Dirichlet
process mixture model (DDPMM), for clustering batch-sequential data containing
an unknown number of evolving clusters. The algorithm is derived via a
low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM,
and provides a h... | computer science |
5,950 | Predicting the Severity of Breast Masses with Data Mining Methods | cs.LG | Mammography is the most effective and available tool for breast cancer
screening. However, the low positive predictive value of breast biopsy
resulting from mammogram interpretation leads to approximately 70% unnecessary
biopsies with benign outcomes. Data mining algorithms could be used to help
physicians in their dec... | computer science |
5,951 | Privileged Information for Data Clustering | cs.LG | Many machine learning algorithms assume that all input samples are
independently and identically distributed from some common distribution on
either the input space X, in the case of unsupervised learning, or the input
and output space X x Y in the case of supervised and semi-supervised learning.
In the last number of ... | computer science |
5,952 | Learning without Concentration | cs.LG | We obtain sharp bounds on the performance of Empirical Risk Minimization
performed in a convex class and with respect to the squared loss, without
assuming that class members and the target are bounded functions or have
rapidly decaying tails.
Rather than resorting to a concentration-based argument, the method used h... | computer science |
5,953 | Generalization Bounds for Representative Domain Adaptation | cs.LG | In this paper, we propose a novel framework to analyze the theoretical
properties of the learning process for a representative type of domain
adaptation, which combines data from multiple sources and one target (or
briefly called representative domain adaptation). In particular, we use the
integral probability metric t... | computer science |
5,954 | Efficient unimodality test in clustering by signature testing | cs.LG | This paper provides a new unimodality test with application in hierarchical
clustering methods. The proposed method denoted by signature test (Sigtest),
transforms the data based on its statistics. The transformed data has much
smaller variation compared to the original data and can be evaluated in a
simple proposed un... | computer science |
5,955 | Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts | cs.LG | We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-m... | computer science |
5,956 | Lasso and equivalent quadratic penalized models | stat.ML | The least absolute shrinkage and selection operator (lasso) and ridge
regression produce usually different estimates although input, loss function
and parameterization of the penalty are identical. In this paper we look for
ridge and lasso models with identical solution set.
It turns out, that the lasso model with sh... | computer science |
5,957 | Multi-Step-Ahead Time Series Prediction using Multiple-Output Support
Vector Regression | cs.LG | Accurate time series prediction over long future horizons is challenging and
of great interest to both practitioners and academics. As a well-known
intelligent algorithm, the standard formulation of Support Vector Regression
(SVR) could be taken for multi-step-ahead time series prediction, only relying
either on iterat... | computer science |
5,958 | Stochastic Optimization with Importance Sampling | stat.ML | Uniform sampling of training data has been commonly used in traditional
stochastic optimization algorithms such as Proximal Stochastic Gradient Descent
(prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although
uniform sampling can guarantee that the sampled stochastic quantity is an
unbiased estim... | computer science |
5,959 | Binary Classifier Calibration: Bayesian Non-Parametric Approach | stat.ML | A set of probabilistic predictions is well calibrated if the events that are
predicted to occur with probability p do in fact occur about p fraction of the
time. Well calibrated predictions are particularly important when machine
learning models are used in decision analysis. This paper presents two new
non-parametric ... | computer science |
5,960 | Binary Classifier Calibration: Non-parametric approach | stat.ML | Accurate calibration of probabilistic predictive models learned is critical
for many practical prediction and decision-making tasks. There are two main
categories of methods for building calibrated classifiers. One approach is to
develop methods for learning probabilistic models that are well-calibrated, ab
initio. The... | computer science |
5,961 | Coordinate Descent with Online Adaptation of Coordinate Frequencies | stat.ML | Coordinate descent (CD) algorithms have become the method of choice for
solving a number of optimization problems in machine learning. They are
particularly popular for training linear models, including linear support
vector machine classification, LASSO regression, and logistic regression.
We consider general CD wit... | computer science |
5,962 | Excess Risk Bounds for Exponentially Concave Losses | cs.LG | The overarching goal of this paper is to derive excess risk bounds for
learning from exp-concave loss functions in passive and sequential learning
settings. Exp-concave loss functions encompass several fundamental problems in
machine learning such as squared loss in linear regression, logistic loss in
classification, a... | computer science |
5,963 | Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM | stat.ML | Discovering causal relations among observed variables in a given data set is
a major objective in studies of statistics and artificial intelligence.
Recently, some techniques to discover a unique causal model have been explored
based on non-Gaussianity of the observed data distribution. However, most of
these are limit... | computer science |
5,964 | Kernel Least Mean Square with Adaptive Kernel Size | stat.ML | Kernel adaptive filters (KAF) are a class of powerful nonlinear filters
developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is
usually the default kernel in KAF algorithms, but selecting the proper kernel
size (bandwidth) is still an open important issue especially for learning with
small sample s... | computer science |
5,965 | Matrix factorization with Binary Components | stat.ML | Motivated by an application in computational biology, we consider low-rank
matrix factorization with $\{0,1\}$-constraints on one of the factors and
optionally convex constraints on the second one. In addition to the
non-convexity shared with other matrix factorization schemes, our problem is
further complicated by a c... | computer science |
5,966 | Predicting Nearly As Well As the Optimal Twice Differentiable Regressor | cs.LG | We study nonlinear regression of real valued data in an individual sequence
manner, where we provide results that are guaranteed to hold without any
statistical assumptions. We address the convergence and undertraining issues of
conventional nonlinear regression methods and introduce an algorithm that
elegantly mitigat... | computer science |
5,967 | Bayesian nonparametric comorbidity analysis of psychiatric disorders | stat.ML | The analysis of comorbidity is an open and complex research field in the
branch of psychiatry, where clinical experience and several studies suggest
that the relation among the psychiatric disorders may have etiological and
treatment implications. In this paper, we are interested in applying latent
feature modeling to ... | computer science |
5,968 | Support vector comparison machines | stat.ML | In ranking problems, the goal is to learn a ranking function from labeled
pairs of input points. In this paper, we consider the related comparison
problem, where the label indicates which element of the pair is better, or if
there is no significant difference. We cast the learning problem as a margin
maximization, and ... | computer science |
5,969 | Online Clustering of Bandits | cs.LG | We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number... | computer science |
5,970 | Neural Variational Inference and Learning in Belief Networks | cs.LG | Highly expressive directed latent variable models, such as sigmoid belief
networks, are difficult to train on large datasets because exact inference in
them is intractable and none of the approximate inference methods that have
been applied to them scale well. We propose a fast non-iterative approximate
inference metho... | computer science |
5,971 | Markov Blanket Ranking using Kernel-based Conditional Dependence
Measures | stat.ML | Developing feature selection algorithms that move beyond a pure correlational
to a more causal analysis of observational data is an important problem in the
sciences. Several algorithms attempt to do so by discovering the Markov blanket
of a target, but they all contain a forward selection step which variables must
pas... | computer science |
5,972 | Randomized Nonlinear Component Analysis | stat.ML | Classical methods such as Principal Component Analysis (PCA) and Canonical
Correlation Analysis (CCA) are ubiquitous in statistics. However, these
techniques are only able to reveal linear relationships in data. Although
nonlinear variants of PCA and CCA have been proposed, these are computationally
prohibitive in the ... | computer science |
5,973 | Transductive Learning with Multi-class Volume Approximation | cs.LG | Given a hypothesis space, the large volume principle by Vladimir Vapnik
prioritizes equivalence classes according to their volume in the hypothesis
space. The volume approximation has hitherto been successfully applied to
binary learning problems. In this paper, we extend it naturally to a more
general definition which... | computer science |
5,974 | Applying Supervised Learning Algorithms and a New Feature Selection
Method to Predict Coronary Artery Disease | cs.LG | From a fresh data science perspective, this thesis discusses the prediction
of coronary artery disease based on genetic variations at the DNA base pair
level, called Single-Nucleotide Polymorphisms (SNPs), collected from the
Ontario Heart Genomics Study (OHGS).
First, the thesis explains two commonly used supervised ... | computer science |
5,975 | Efficient Gradient-Based Inference through Transformations between Bayes
Nets and Neural Nets | cs.LG | Hierarchical Bayesian networks and neural networks with stochastic hidden
units are commonly perceived as two separate types of models. We show that
either of these types of models can often be transformed into an instance of
the other, by switching between centered and differentiable non-centered
parameterizations of ... | computer science |
5,976 | Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits | cs.LG | We present a new algorithm for the contextual bandit learning problem, where
the learner repeatedly takes one of $K$ actions in response to the observed
context, and observes the reward only for that chosen action. Our method
assumes access to an oracle for solving fully supervised cost-sensitive
classification problem... | computer science |
5,977 | UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting
methods | cs.LG | Convex optimization is an essential tool for machine learning, as many of its
problems can be formulated as minimization problems of specific objective
functions. While there is a large variety of algorithms available to solve
convex problems, we can argue that it becomes more and more important to focus
on efficient, ... | computer science |
5,978 | Sequential Model-Based Ensemble Optimization | cs.LG | One of the most tedious tasks in the application of machine learning is model
selection, i.e. hyperparameter selection. Fortunately, recent progress has been
made in the automation of this process, through the use of sequential
model-based optimization (SMBO) methods. This can be used to optimize a
cross-validation per... | computer science |
5,979 | Discovering Latent Network Structure in Point Process Data | stat.ML | Networks play a central role in modern data analysis, enabling us to reason
about systems by studying the relationships between their parts. Most often in
network analysis, the edges are given. However, in many systems it is difficult
or impossible to measure the network directly. Examples of latent networks
include ec... | computer science |
5,980 | Learning Ordered Representations with Nested Dropout | stat.ML | In this paper, we study ordered representations of data in which different
dimensions have different degrees of importance. To learn these representations
we introduce nested dropout, a procedure for stochastically removing coherent
nested sets of hidden units in a neural network. We first present a sequence of
theoret... | computer science |
5,981 | Input Warping for Bayesian Optimization of Non-stationary Functions | stat.ML | Bayesian optimization has proven to be a highly effective methodology for the
global optimization of unknown, expensive and multimodal functions. The ability
to accurately model distributions over functions is critical to the
effectiveness of Bayesian optimization. Although Gaussian processes provide a
flexible prior o... | computer science |
5,982 | Dissimilarity-based Ensembles for Multiple Instance Learning | stat.ML | In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototyp... | computer science |
5,983 | Distributed Variational Inference in Sparse Gaussian Process Regression
and Latent Variable Models | stat.ML | Gaussian processes (GPs) are a powerful tool for probabilistic inference over
functions. They have been applied to both regression and non-linear
dimensionality reduction, and offer desirable properties such as uncertainty
estimates, robustness to over-fitting, and principled ways for tuning
hyper-parameters. However t... | computer science |
5,984 | Binary Excess Risk for Smooth Convex Surrogates | cs.LG | In statistical learning theory, convex surrogates of the 0-1 loss are highly
preferred because of the computational and theoretical virtues that convexity
brings in. This is of more importance if we consider smooth surrogates as
witnessed by the fact that the smoothness is further beneficial both
computationally- by at... | computer science |
5,985 | An Inequality with Applications to Structured Sparsity and Multitask
Dictionary Learning | cs.LG | From concentration inequalities for the suprema of Gaussian or Rademacher
processes an inequality is derived. It is applied to sharpen existing and to
derive novel bounds on the empirical Rademacher complexities of unit balls in
various norms appearing in the context of structured sparsity and multitask
dictionary lear... | computer science |
5,986 | A comparison of linear and non-linear calibrations for speaker
recognition | stat.ML | In recent work on both generative and discriminative score to
log-likelihood-ratio calibration, it was shown that linear transforms give good
accuracy only for a limited range of operating points. Moreover, these methods
required tailoring of the calibration training objective functions in order to
target the desired r... | computer science |
5,987 | On Zeroth-Order Stochastic Convex Optimization via Random Walks | cs.LG | We propose a method for zeroth order stochastic convex optimization that
attains the suboptimality rate of $\tilde{\mathcal{O}}(n^{7}T^{-1/2})$ after
$T$ queries for a convex bounded function $f:{\mathbb R}^n\to{\mathbb R}$. The
method is based on a random walk (the \emph{Ball Walk}) on the epigraph of the
function. Th... | computer science |
5,988 | Regularization for Multiple Kernel Learning via Sum-Product Networks | stat.ML | In this paper, we are interested in constructing general graph-based
regularizers for multiple kernel learning (MKL) given a structure which is used
to describe the way of combining basis kernels. Such structures are represented
by sum-product networks (SPNs) in our method. Accordingly we propose a new
convex regulariz... | computer science |
5,989 | A Robust Ensemble Approach to Learn From Positive and Unlabeled Data
Using SVM Base Models | stat.ML | We present a novel approach to learn binary classifiers when only positive
and unlabeled instances are available (PU learning). This problem is routinely
cast as a supervised task with label noise in the negative set. We use an
ensemble of SVM models trained on bootstrap resamples of the training data for
increased rob... | computer science |
5,990 | The Random Forest Kernel and other kernels for big data from random
partitions | stat.ML | We present Random Partition Kernels, a new class of kernels derived by
demonstrating a natural connection between random partitions of objects and
kernels between those objects. We show how the construction can be used to
create kernels from methods that would not normally be viewed as random
partitions, such as Random... | computer science |
5,991 | Automatic Construction and Natural-Language Description of Nonparametric
Regression Models | stat.ML | This paper presents the beginnings of an automatic statistician, focusing on
regression problems. Our system explores an open-ended space of statistical
models to discover a good explanation of a data set, and then produces a
detailed report with figures and natural-language text. Our approach treats
unknown regression... | computer science |
5,992 | Hybrid SRL with Optimization Modulo Theories | cs.LG | Generally speaking, the goal of constructive learning could be seen as, given
an example set of structured objects, to generate novel objects with similar
properties. From a statistical-relational learning (SRL) viewpoint, the task
can be interpreted as a constraint satisfaction problem, i.e. the generated
objects must... | computer science |
5,993 | Classification with Sparse Overlapping Groups | cs.LG | Classification with a sparsity constraint on the solution plays a central
role in many high dimensional machine learning applications. In some cases, the
features can be grouped together so that entire subsets of features can be
selected or not selected. In many applications, however, this can be too
restrictive. In th... | computer science |
5,994 | Subspace Learning with Partial Information | cs.LG | The goal of subspace learning is to find a $k$-dimensional subspace of
$\mathbb{R}^d$, such that the expected squared distance between instance
vectors and the subspace is as small as possible. In this paper we study
subspace learning in a partial information setting, in which the learner can
only observe $r \le d$ att... | computer science |
5,995 | Learning the Parameters of Determinantal Point Process Kernels | stat.ML | Determinantal point processes (DPPs) are well-suited for modeling repulsion
and have proven useful in many applications where diversity is desired. While
DPPs have many appealing properties, such as efficient sampling, learning the
parameters of a DPP is still considered a difficult problem due to the
non-convex nature... | computer science |
5,996 | Variational Particle Approximations | stat.ML | Approximate inference in high-dimensional, discrete probabilistic models is a
central problem in computational statistics and machine learning. This paper
describes discrete particle variational inference (DPVI), a new approach that
combines key strengths of Monte Carlo, variational and search-based techniques.
DPVI is... | computer science |
5,997 | Avoiding pathologies in very deep networks | stat.ML | Choosing appropriate architectures and regularization strategies for deep
networks is crucial to good predictive performance. To shed light on this
problem, we analyze the analogous problem of constructing useful priors on
compositions of functions. Specifically, we study the deep Gaussian process, a
type of infinitely... | computer science |
5,998 | Predictive Interval Models for Non-parametric Regression | cs.LG | Having a regression model, we are interested in finding two-sided intervals
that are guaranteed to contain at least a desired proportion of the conditional
distribution of the response variable given a specific combination of
predictors. We name such intervals predictive intervals. This work presents a
new method to fi... | computer science |
5,999 | Manifold Gaussian Processes for Regression | stat.ML | Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness
assumptions on the structure of the function to be modeled. To model complex
and non-differentiable functions, these smoothness assumptions are often too
restrictive. One way to alleviate this limitation is to find a different
representation of ... | computer science |
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