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22,202 | Gradient Importance Sampling | stat.ML | Adaptive Monte Carlo schemes developed over the last years usually seek to
ensure ergodicity of the sampling process in line with MCMC tradition. This
poses constraints on what is possible in terms of adaptation. In the general
case ergodicity can only be guaranteed if adaptation is diminished at a certain
rate. Import... | computer science |
22,203 | A statistical perspective of sampling scores for linear regression | stat.ML | In this paper, we consider a statistical problem of learning a linear model
from noisy samples. Existing work has focused on approximating the least
squares solution by using leverage-based scores as an importance sampling
distribution. However, no finite sample statistical guarantees and no
computationally efficient o... | computer science |
22,204 | Sparsity in Multivariate Extremes with Applications to Anomaly Detection | stat.ML | Capturing the dependence structure of multivariate extreme events is a major
concern in many fields involving the management of risks stemming from multiple
sources, e.g. portfolio monitoring, insurance, environmental risk management
and anomaly detection. One convenient (non-parametric) characterization of
extremal de... | computer science |
22,205 | Admissibility of a posterior predictive decision rule | stat.ML | Recent decades have seen an interest in prediction problems for which
Bayesian methodology has been used ubiquitously. Sampling from or approximating
the posterior predictive distribution in a Bayesian model allows one to make
inferential statements about potentially observable random quantities given
observed data. Th... | computer science |
22,206 | Optimal Learning Rates for Localized SVMs | stat.ML | One of the limiting factors of using support vector machines (SVMs) in large
scale applications are their super-linear computational requirements in terms
of the number of training samples. To address this issue, several approaches
that train SVMs on many small chunks of large data sets separately have been
proposed in... | computer science |
22,207 | Clustering of Modal Valued Symbolic Data | stat.ML | Symbolic Data Analysis is based on special descriptions of data - symbolic
objects (SO). Such descriptions preserve more detailed information about units
and their clusters than the usual representations with mean values. A special
kind of symbolic object is a representation with frequency or probability
distributions ... | computer science |
22,208 | String and Membrane Gaussian Processes | stat.ML | In this paper we introduce a novel framework for making exact nonparametric
Bayesian inference on latent functions, that is particularly suitable for Big
Data tasks. Firstly, we introduce a class of stochastic processes we refer to
as string Gaussian processes (string GPs), which are not to be mistaken for
Gaussian pro... | computer science |
22,209 | Context-aware learning for finite mixture models | stat.ML | This work introduces algorithms able to exploit contextual information in
order to improve maximum-likelihood (ML) parameter estimation in finite mixture
models (FMM), demonstrating their benefits and properties in several scenarios.
The proposed algorithms are derived in a probabilistic framework with regard to
situat... | computer science |
22,210 | Regularized Multi-Task Learning for Multi-Dimensional Log-Density
Gradient Estimation | stat.ML | Log-density gradient estimation is a fundamental statistical problem and
possesses various practical applications such as clustering and measuring
non-Gaussianity. A naive two-step approach of first estimating the density and
then taking its log-gradient is unreliable because an accurate density estimate
does not neces... | computer science |
22,211 | Direct Estimation of the Derivative of Quadratic Mutual Information with
Application in Supervised Dimension Reduction | stat.ML | A typical goal of supervised dimension reduction is to find a low-dimensional
subspace of the input space such that the projected input variables preserve
maximal information about the output variables. The dependence maximization
approach solves the supervised dimension reduction problem through maximizing a
statistic... | computer science |
22,212 | Non-isometric Curve to Surface Matching with Incomplete Data for
Functional Calibration | stat.ML | Calibration refers to the process of adjusting features of a computational
model that are not observed in the physical process so that the model matches
the real process. We propose a framework for calibration when the unobserved
features, i.e. calibration parameters, do not assume a single value, but are
functionally ... | computer science |
22,213 | Sparse Pseudo-input Local Kriging for Large Non-stationary Spatial
Datasets with Exogenous Variables | stat.ML | Gaussian process (GP) regression is a powerful tool for building predictive
models for spatial systems. However, it does not scale efficiently for large
datasets. Particularly, for high-dimensional spatial datasets, i.e., spatial
datasets that contain exogenous variables, the performance of GP regression
further deteri... | computer science |
22,214 | Distributional Equivalence and Structure Learning for Bow-free Acyclic
Path Diagrams | stat.ML | We consider the problem of structure learning for bow-free acyclic path
diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG
models that allow for certain hidden variables. We present a first method for
this problem using a greedy score-based search algorithm. We also prove some
necessary and ... | computer science |
22,215 | Convergence rates of sub-sampled Newton methods | stat.ML | We consider the problem of minimizing a sum of $n$ functions over a convex
parameter set $\mathcal{C} \subset \mathbb{R}^p$ where $n\gg p\gg 1$. In this
regime, algorithms which utilize sub-sampling techniques are known to be
effective. In this paper, we use sub-sampling techniques together with low-rank
approximation ... | computer science |
22,216 | Bayesian Dropout | stat.ML | Dropout has recently emerged as a powerful and simple method for training
neural networks preventing co-adaptation by stochastically omitting neurons.
Dropout is currently not grounded in explicit modelling assumptions which so
far has precluded its adoption in Bayesian modelling. Using Bayesian entropic
reasoning we s... | computer science |
22,217 | Neyman-Pearson Classification under High-Dimensional Settings | stat.ML | Most existing binary classification methods target on the optimization of the
overall classification risk and may fail to serve some real-world applications
such as cancer diagnosis, where users are more concerned with the risk of
misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm
was introd... | computer science |
22,218 | Unbounded Bayesian Optimization via Regularization | stat.ML | Bayesian optimization has recently emerged as a popular and efficient tool
for global optimization and hyperparameter tuning. Currently, the established
Bayesian optimization practice requires a user-defined bounding box which is
assumed to contain the optimizer. However, when little is known about the
probed objective... | computer science |
22,219 | Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo | stat.ML | We present a novel approach for fully non-stationary Gaussian process
regression (GPR), where all three key parameters -- noise variance, signal
variance and lengthscale -- can be simultaneously input-dependent. We develop
gradient-based inference methods to learn the unknown function and the
non-stationary model param... | computer science |
22,220 | Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector
Problems | stat.ML | We are interested in solving the multiple measurement vector (MMV) problem
for instances, where the underlying sparsity pattern exhibit spatio-temporal
structure motivated by the electroencephalogram (EEG) source localization
problem. We propose a probabilistic model that takes this structure into
account by generalizi... | computer science |
22,221 | Another Look at DWD: Thrifty Algorithm and Bayes Risk Consistency in
RKHS | stat.ML | Distance weighted discrimination (DWD) is a margin-based classifier with an
interesting geometric motivation. DWD was originally proposed as a superior
alternative to the support vector machine (SVM), however DWD is yet to be
popular compared with the SVM. The main reasons are twofold. First, the
state-of-the-art algor... | computer science |
22,222 | Calibration of One-Class SVM for MV set estimation | stat.ML | A general approach for anomaly detection or novelty detection consists in
estimating high density regions or Minimum Volume (MV) sets. The One-Class
Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating
such regions from high dimensional data. Yet it suffers from practical
limitations. When appl... | computer science |
22,223 | Stochastic gradient variational Bayes for gamma approximating
distributions | stat.ML | While stochastic variational inference is relatively well known for scaling
inference in Bayesian probabilistic models, related methods also offer ways to
circumnavigate the approximation of analytically intractable expectations. The
key challenge in either setting is controlling the variance of gradient
estimates: rec... | computer science |
22,224 | Matrix Factorisation with Linear Filters | stat.ML | This text investigates relations between two well-known family of algorithms,
matrix factorisations and recursive linear filters, by describing a
probabilistic model in which approximate inference corresponds to a matrix
factorisation algorithm. Using the probabilistic model, we derive a matrix
factorisation algorithm ... | computer science |
22,225 | Poisson Subsampling Algorithms for Large Sample Linear Regression in
Massive Data | stat.ML | Large sample size brings the computation bottleneck for modern data analysis.
Subsampling is one of efficient strategies to handle this problem. In previous
studies, researchers make more fo- cus on subsampling with replacement (SSR)
than on subsampling without replacement (SSWR). In this paper we investigate a
kind of... | computer science |
22,226 | Modelling time evolving interactions in networks through a non
stationary extension of stochastic block models | stat.ML | In this paper, we focus on the stochastic block model (SBM),a probabilistic
tool describing interactions between nodes of a network using latent clusters.
The SBM assumes that the networkhas a stationary structure, in which
connections of time varying intensity are not taken into account. In other
words, interactions b... | computer science |
22,227 | Empirical risk minimization is consistent with the mean absolute
percentage error | stat.ML | We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We also show that, under some asumptions,
universal consisten... | computer science |
22,228 | A Variational Bayesian State-Space Approach to Online Passive-Aggressive
Regression | stat.ML | Online Passive-Aggressive (PA) learning is a class of online margin-based
algorithms suitable for a wide range of real-time prediction tasks, including
classification and regression. PA algorithms are formulated in terms of
deterministic point-estimation problems governed by a set of user-defined
hyperparameters: the a... | computer science |
22,229 | Fast Second-Order Stochastic Backpropagation for Variational Inference | stat.ML | We propose a second-order (Hessian or Hessian-free) based optimization method
for variational inference inspired by Gaussian backpropagation, and argue that
quasi-Newton optimization can be developed as well. This is accomplished by
generalizing the gradient computation in stochastic backpropagation via a
reparametriza... | computer science |
22,230 | Sélection de variables par le GLM-Lasso pour la prédiction du risque
palustre | stat.ML | In this study, we propose an automatic learning method for variables
selection based on Lasso in epidemiology context. One of the aim of this
approach is to overcome the pretreatment of experts in medicine and
epidemiology on collected data. These pretreatment consist in recoding some
variables and to choose some inter... | computer science |
22,231 | Learning the Number of Autoregressive Mixtures in Time Series Using the
Gap Statistics | stat.ML | Using a proper model to characterize a time series is crucial in making
accurate predictions. In this work we use time-varying autoregressive process
(TVAR) to describe non-stationary time series and model it as a mixture of
multiple stable autoregressive (AR) processes. We introduce a new model
selection technique bas... | computer science |
22,232 | When are Kalman-filter restless bandits indexable? | stat.ML | We study the restless bandit associated with an extremely simple scalar
Kalman filter model in discrete time. Under certain assumptions, we prove that
the problem is indexable in the sense that the Whittle index is a
non-decreasing function of the relevant belief state. In spite of the long
history of this problem, thi... | computer science |
22,233 | Macau: Scalable Bayesian Multi-relational Factorization with Side
Information using MCMC | stat.ML | We propose Macau, a powerful and flexible Bayesian factorization method for
heterogeneous data. Our model can factorize any set of entities and relations
that can be represented by a relational model, including tensors and also
multiple relations for each entity. Macau can also incorporate side
information, specificall... | computer science |
22,234 | Large-Scale Optimization Algorithms for Sparse Conditional Gaussian
Graphical Models | stat.ML | This paper addresses the problem of scalable optimization for L1-regularized
conditional Gaussian graphical models. Conditional Gaussian graphical models
generalize the well-known Gaussian graphical models to conditional
distributions to model the output network influenced by conditioning input
variables. While highly ... | computer science |
22,235 | Dirichlet Fragmentation Processes | stat.ML | Tree structures are ubiquitous in data across many domains, and many datasets
are naturally modelled by unobserved tree structures. In this paper, first we
review the theory of random fragmentation processes [Bertoin, 2006], and a
number of existing methods for modelling trees, including the popular nested
Chinese rest... | computer science |
22,236 | A Statistical Theory of Deep Learning via Proximal Splitting | stat.ML | In this paper we develop a statistical theory and an implementation of deep
learning models. We show that an elegant variable splitting scheme for the
alternating direction method of multipliers optimises a deep learning
objective. We allow for non-smooth non-convex regularisation penalties to
induce sparsity in parame... | computer science |
22,237 | Density Estimation via Discrepancy | stat.ML | Given i.i.d samples from some unknown continuous density on hyper-rectangle
$[0, 1]^d$, we attempt to learn a piecewise constant function that approximates
this underlying density non-parametrically. Our density estimate is defined on
a binary split of $[0, 1]^d$ and built up sequentially according to discrepancy
crite... | computer science |
22,238 | High Dimensional Data Modeling Techniques for Detection of Chemical
Plumes and Anomalies in Hyperspectral Images and Movies | stat.ML | We briefly review recent progress in techniques for modeling and analyzing
hyperspectral images and movies, in particular for detecting plumes of both
known and unknown chemicals. For detecting chemicals of known spectrum, we
extend the technique of using a single subspace for modeling the background to
a "mixture of s... | computer science |
22,239 | Unbiased Bayesian Inference for Population Markov Jump Processes via
Random Truncations | stat.ML | We consider continuous time Markovian processes where populations of
individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous tim... | computer science |
22,240 | Tractable Fully Bayesian Inference via Convex Optimization and Optimal
Transport Theory | stat.ML | We consider the problem of transforming samples from one continuous source
distribution into samples from another target distribution. We demonstrate with
optimal transport theory that when the source distribution can be easily
sampled from and the target distribution is log-concave, this can be tractably
solved with c... | computer science |
22,241 | Estimating network edge probabilities by neighborhood smoothing | stat.ML | The estimation of probabilities of network edges from the observed adjacency
matrix has important applications to predicting missing links and network
denoising. It has usually been addressed by estimating the graphon, a function
that determines the matrix of edge probabilities, but this is ill-defined
without strong a... | computer science |
22,242 | Maximum Likelihood Latent Space Embedding of Logistic Random Dot Product
Graphs | stat.ML | A latent space model for a family of random graphs assigns real-valued
vectors to nodes of the graph such that edge probabilities are determined by
latent positions. Latent space models provide a natural statistical framework
for graph visualizing and clustering. A latent space model of particular
interest is the Rando... | computer science |
22,243 | Bayesian Estimation of Multidimensional Latent Variables and Its
Asymptotic Accuracy | stat.ML | Hierarchical learning models, such as mixture models and Bayesian networks,
are widely employed for unsupervised learning tasks, such as clustering
analysis. They consist of observable and hidden variables, which represent the
given data and their hidden generation process, respectively. It has been
pointed out that co... | computer science |
22,244 | Asymptotically Optimal Sequential Experimentation Under Generalized
Ranking | stat.ML | We consider the \mnk{classical} problem of a controller activating (or
sampling) sequentially from a finite number of $N \geq 2$ populations,
specified by unknown distributions. Over some time horizon, at each time $n =
1, 2, \ldots$, the controller wishes to select a population to sample, with the
goal of sampling fro... | computer science |
22,245 | The Knowledge Gradient with Logistic Belief Models for Binary
Classification | stat.ML | We consider sequential decision making problems for binary classification
scenario in which the learner takes an active role in repeatedly selecting
samples from the action pool and receives the binary label of the selected
alternatives. Our problem is motivated by applications where observations are
time consuming and... | computer science |
22,246 | p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian
Nonparametric Time Series Forecasting | stat.ML | In this paper we introduce a novel online time series forecasting model we
refer to as the pM-GP filter. We show that our model is equivalent to Gaussian
process regression, with the advantage that both online forecasting and online
learning of the hyper-parameters have a constant (rather than cubic) time
complexity an... | computer science |
22,247 | Consistent Estimation of Low-Dimensional Latent Structure in
High-Dimensional Data | stat.ML | We consider the problem of extracting a low-dimensional, linear latent
variable structure from high-dimensional random variables. Specifically, we
show that under mild conditions and when this structure manifests itself as a
linear space that spans the conditional means, it is possible to consistently
recover the struc... | computer science |
22,248 | Robust Learning for Optimal Treatment Decision with NP-Dimensionality | stat.ML | In order to identify important variables that are involved in making optimal
treatment decision, Lu et al. (2013) proposed a penalized least squared
regression framework for a fixed number of predictors, which is robust against
the misspecification of the conditional mean model. Two problems arise: (i) in
a world of ex... | computer science |
22,249 | Change Detection in Multivariate Datastreams: Likelihood and
Detectability Loss | stat.ML | We address the problem of detecting changes in multivariate datastreams, and
we investigate the intrinsic difficulty that change-detection methods have to
face when the data dimension scales. In particular, we consider a general
approach where changes are detected by comparing the distribution of the
log-likelihood of ... | computer science |
22,250 | A General Method for Robust Bayesian Modeling | stat.ML | Robust Bayesian models are appealing alternatives to standard models,
providing protection from data that contains outliers or other departures from
the model assumptions. Historically, robust models were mostly developed on a
case-by-case basis; examples include robust linear regression, robust mixture
models, and bur... | computer science |
22,251 | Scalable inference for a full multivariate stochastic volatility model | stat.ML | We introduce a multivariate stochastic volatility model for asset returns
that imposes no restrictions to the structure of the volatility matrix and
treats all its elements as functions of latent stochastic processes. When the
number of assets is prohibitively large, we propose a factor multivariate
stochastic volatili... | computer science |
22,252 | Modularity Component Analysis versus Principal Component Analysis | stat.ML | In this paper the exact linear relation between the leading eigenvectors of
the modularity matrix and the singular vectors of an uncentered data matrix is
developed. Based on this analysis the concept of a modularity component is
defined, and its properties are developed. It is shown that modularity
component analysis ... | computer science |
22,253 | Optimization for Gaussian Processes via Chaining | stat.ML | In this paper, we consider the problem of stochastic optimization under a
bandit feedback model. We generalize the GP-UCB algorithm [Srinivas and al.,
2012] to arbitrary kernels and search spaces. To do so, we use a notion of
localized chaining to control the supremum of a Gaussian process, and provide a
novel optimiza... | computer science |
22,254 | NYTRO: When Subsampling Meets Early Stopping | stat.ML | Early stopping is a well known approach to reduce the time complexity for
performing training and model selection of large scale learning machines. On
the other hand, memory/space (rather than time) complexity is the main
constraint in many applications, and randomized subsampling techniques have
been proposed to tackl... | computer science |
22,255 | Multiple co-clustering based on nonparametric mixture models with
heterogeneous marginal distributions | stat.ML | We propose a novel method for multiple clustering that assumes a
co-clustering structure (partitions in both rows and columns of the data
matrix) in each view. The new method is applicable to high-dimensional data. It
is based on a nonparametric Bayesian approach in which the number of views and
the number of feature-/... | computer science |
22,256 | GLASSES: Relieving The Myopia Of Bayesian Optimisation | stat.ML | We present GLASSES: Global optimisation with Look-Ahead through Stochastic
Simulation and Expected-loss Search. The majority of global optimisation
approaches in use are myopic, in only considering the impact of the next
function value; the non-myopic approaches that do exist are able to consider
only a handful of futu... | computer science |
22,257 | Inventory Control Involving Unknown Demand of Discrete Nonperishable
Items - Analysis of a Newsvendor-based Policy | stat.ML | Inventory control with unknown demand distribution is considered, with
emphasis placed on the case involving discrete nonperishable items. We focus on
an adaptive policy which in every period uses, as much as possible, the optimal
newsvendor ordering quantity for the empirical distribution learned up to that
period. Th... | computer science |
22,258 | Cascaded High Dimensional Histograms: A Generative Approach to Density
Estimation | stat.ML | We present tree- and list- structured density estimation methods for high
dimensional binary/categorical data. Our density estimation models are high
dimensional analogies to variable bin width histograms. In each leaf of the
tree (or list), the density is constant, similar to the flat density within the
bin of a histo... | computer science |
22,259 | A Framework to Adjust Dependency Measure Estimates for Chance | stat.ML | Estimating the strength of dependency between two variables is fundamental
for exploratory analysis and many other applications in data mining. For
example: non-linear dependencies between two continuous variables can be
explored with the Maximal Information Coefficient (MIC); and categorical
variables that are depende... | computer science |
22,260 | Blitzkriging: Kronecker-structured Stochastic Gaussian Processes | stat.ML | We present Blitzkriging, a new approach to fast inference for Gaussian
processes, applicable to regression, optimisation and classification.
State-of-the-art (stochastic) inference for Gaussian processes on very large
datasets scales cubically in the number of 'inducing inputs', variables
introduced to factorise the mo... | computer science |
22,261 | Spectral Convergence Rate of Graph Laplacian | stat.ML | Laplacian Eigenvectors of the graph constructed from a data set are used in
many spectral manifold learning algorithms such as diffusion maps and spectral
clustering. Given a graph constructed from a random sample of a $d$-dimensional
compact submanifold $M$ in $\mathbb{R}^D$, we establish the spectral
convergence rate... | computer science |
22,262 | Fast Landmark Subspace Clustering | stat.ML | Kernel methods obtain superb performance in terms of accuracy for various
machine learning tasks since they can effectively extract nonlinear relations.
However, their time complexity can be rather large especially for clustering
tasks. In this paper we define a general class of kernels that can be easily
approximated ... | computer science |
22,263 | Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso | stat.ML | Gaussian Graphical Models (GGMs) are popular tools for studying network
structures. However, many modern applications such as gene network discovery
and social interactions analysis often involve high-dimensional noisy data with
outliers or heavier tails than the Gaussian distribution. In this paper, we
propose the Tri... | computer science |
22,264 | Nonconvex Penalization in Sparse Estimation: An Approach Based on the
Bernstein Function | stat.ML | In this paper we study nonconvex penalization using Bernstein functions whose
first-order derivatives are completely monotone. The Bernstein function can
induce a class of nonconvex penalty functions for high-dimensional sparse
estimation problems. We derive a thresholding function based on the Bernstein
penalty and di... | computer science |
22,265 | PCA-Based Out-of-Sample Extension for Dimensionality Reduction | stat.ML | Dimensionality reduction methods are very common in the field of high
dimensional data analysis. Typically, algorithms for dimensionality reduction
are computationally expensive. Therefore, their applications for the analysis
of massive amounts of data are impractical. For example, repeated computations
due to accumula... | computer science |
22,266 | Lasso based feature selection for malaria risk exposure prediction | stat.ML | In life sciences, the experts generally use empirical knowledge to recode
variables, choose interactions and perform selection by classical approach. The
aim of this work is to perform automatic learning algorithm for variables
selection which can lead to know if experts can be help in they decision or
simply replaced ... | computer science |
22,267 | Neutralized Empirical Risk Minimization with Generalization Neutrality
Bound | stat.ML | Currently, machine learning plays an important role in the lives and
individual activities of numerous people. Accordingly, it has become necessary
to design machine learning algorithms to ensure that discrimination, biased
views, or unfair treatment do not result from decision making or predictions
made via machine le... | computer science |
22,268 | Streaming regularization parameter selection via stochastic gradient
descent | stat.ML | We propose a framework to perform streaming covariance selection. Our
approach employs regularization constraints where a time-varying sparsity
parameter is iteratively estimated via stochastic gradient descent. This allows
for the regularization parameter to be efficiently learnt in an online manner.
The proposed fram... | computer science |
22,269 | Learning Instrumental Variables with Non-Gaussianity Assumptions:
Theoretical Limitations and Practical Algorithms | stat.ML | Learning a causal effect from observational data is not straightforward, as
this is not possible without further assumptions. If hidden common causes
between treatment $X$ and outcome $Y$ cannot be blocked by other measurements,
one possibility is to use an instrumental variable. In principle, it is
possible under some... | computer science |
22,270 | Black-box $α$-divergence Minimization | stat.ML | Black-box alpha (BB-$\alpha$) is a new approximate inference method based on
the minimization of $\alpha$-divergences. BB-$\alpha$ scales to large datasets
because it can be implemented using stochastic gradient descent. BB-$\alpha$
can be applied to complex probabilistic models with little effort since it only
require... | computer science |
22,271 | Stochastic Expectation Propagation for Large Scale Gaussian Process
Classification | stat.ML | A method for large scale Gaussian process classification has been recently
proposed based on expectation propagation (EP). Such a method allows Gaussian
process classifiers to be trained on very large datasets that were out of the
reach of previous deployments of EP and has been shown to be competitive with
related tec... | computer science |
22,272 | Training Deep Gaussian Processes using Stochastic Expectation
Propagation and Probabilistic Backpropagation | stat.ML | Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations
of Gaussian processes (GPs) and are formally equivalent to neural networks with
multiple, infinitely wide hidden layers. DGPs are probabilistic and
non-parametric and as such are arguably more flexible, have a greater capacity
to generalise, an... | computer science |
22,273 | Automatic Inference of the Quantile Parameter | stat.ML | Supervised learning is an active research area, with numerous applications in
diverse fields such as data analytics, computer vision, speech and audio
processing, and image understanding. In most cases, the loss functions used in
machine learning assume symmetric noise models, and seek to estimate the
unknown function ... | computer science |
22,274 | $k$-means: Fighting against Degeneracy in Sequential Monte Carlo with an
Application to Tracking | stat.ML | For regular particle filter algorithm or Sequential Monte Carlo (SMC)
methods, the initial weights are traditionally dependent on the proposed
distribution, the posterior distribution at the current timestamp in the
sampled sequence, and the target is the posterior distribution of the previous
timestamp. This is techni... | computer science |
22,275 | Lass-0: sparse non-convex regression by local search | stat.ML | We compute approximate solutions to L0 regularized linear regression using L1
regularization, also known as the Lasso, as an initialization step. Our
algorithm, the Lass-0 ("Lass-zero"), uses a computationally efficient stepwise
search to determine a locally optimal L0 solution given any L1 regularization
solution. We ... | computer science |
22,276 | Scalable Gaussian Processes for Characterizing Multidimensional Change
Surfaces | stat.ML | We present a scalable Gaussian process model for identifying and
characterizing smooth multidimensional changepoints, and automatically learning
changes in expressive covariance structure. We use Random Kitchen Sink features
to flexibly define a change surface in combination with expressive spectral
mixture kernels to ... | computer science |
22,277 | Probabilistic Segmentation via Total Variation Regularization | stat.ML | We present a convex approach to probabilistic segmentation and modeling of
time series data. Our approach builds upon recent advances in multivariate
total variation regularization, and seeks to learn a separate set of parameters
for the distribution over the observations at each time point, but with an
additional pena... | computer science |
22,278 | Predictive Entropy Search for Multi-objective Bayesian Optimization | stat.ML | We present PESMO, a Bayesian method for identifying the Pareto set of
multi-objective optimization problems, when the functions are expensive to
evaluate. The central idea of PESMO is to choose evaluation points so as to
maximally reduce the entropy of the posterior distribution over the Pareto set.
Critically, the PES... | computer science |
22,279 | The Kernel Two-Sample Test for Brain Networks | stat.ML | In clinical and neuroscientific studies, systematic differences between two
populations of brain networks are investigated in order to characterize mental
diseases or processes. Those networks are usually represented as graphs built
from neuroimaging data and studied by means of graph analysis methods. The
typical mach... | computer science |
22,280 | PLDA with Two Sources of Inter-session Variability | stat.ML | In some speaker recognition scenarios we find conversations recorded
simultaneously over multiple channels. That is the case of the interviews in
the NIST SRE dataset. To take advantage of that, we propose a modification of
the PLDA model that considers two different inter-session variability terms.
The first term is t... | computer science |
22,281 | Stochastic Parallel Block Coordinate Descent for Large-scale Saddle
Point Problems | stat.ML | We consider convex-concave saddle point problems with a separable structure
and non-strongly convex functions. We propose an efficient stochastic block
coordinate descent method using adaptive primal-dual updates, which enables
flexible parallel optimization for large-scale problems. Our method shares the
efficiency an... | computer science |
22,282 | Bayesian SPLDA | stat.ML | In this document we are going to derive the equations needed to implement a
Variational Bayes estimation of the parameters of the simplified probabilistic
linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA
from one database to another with few development data or to implement the
fully Bayesian... | computer science |
22,283 | Black box variational inference for state space models | stat.ML | Latent variable time-series models are among the most heavily used tools from
machine learning and applied statistics. These models have the advantage of
learning latent structure both from noisy observations and from the temporal
ordering in the data, where it is assumed that meaningful correlation structure
exists ac... | computer science |
22,284 | Unsupervised Adaptation of SPLDA | stat.ML | State-of-the-art speaker recognition relays on models that need a large
amount of training data. This models are successful in tasks like NIST SRE
because there is sufficient data available. However, in real applications, we
usually do not have so much data and, in many cases, the speaker labels are
unknown. We present... | computer science |
22,285 | Variational Bayes Factor Analysis for i-Vector Extraction | stat.ML | In this document we are going to derive the equations needed to implement a
Variational Bayes i-vector extractor. This can be used to extract longer
i-vectors reducing the risk of overfittig or to adapt an i-vector extractor
from a database to another with scarce development data. This work is based on
Patrick Kenny's ... | computer science |
22,286 | Statistical Properties of the Single Linkage Hierarchical Clustering
Estimator | stat.ML | Distance-based hierarchical clustering (HC) methods are widely used in
unsupervised data analysis but few authors take account of uncertainty in the
distance data. We incorporate a statistical model of the uncertainty through
corruption or noise in the pairwise distances and investigate the problem of
estimating the HC... | computer science |
22,287 | Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering | stat.ML | We derive a statistical model for estimation of a dendrogram from single
linkage hierarchical clustering (SLHC) that takes account of uncertainty
through noise or corruption in the measurements of separation of data. Our
focus is on just the estimation of the hierarchy of partitions afforded by the
dendrogram, rather t... | computer science |
22,288 | Gradient Estimation with Simultaneous Perturbation and Compressive
Sensing | stat.ML | This paper aims at achieving a "good" estimator for the gradient of a
function on a high-dimensional space. Often such functions are not sensitive in
all coordinates and the gradient of the function is almost sparse. We propose a
method for gradient estimation that combines ideas from Spall's Simultaneous
Perturbation ... | computer science |
22,289 | A General Framework for Constrained Bayesian Optimization using
Information-based Search | stat.ML | We present an information-theoretic framework for solving global black-box
optimization problems that also have black-box constraints. Of particular
interest to us is to efficiently solve problems with decoupled constraints, in
which subsets of the objective and constraint functions may be evaluated
independently. For ... | computer science |
22,290 | Highly Scalable Tensor Factorization for Prediction of Drug-Protein
Interaction Type | stat.ML | The understanding of the type of inhibitory interaction plays an important
role in drug design. Therefore, researchers are interested to know whether a
drug has competitive or non-competitive interaction to particular protein
targets.
Method: to analyze the interaction types we propose factorization method
Macau whic... | computer science |
22,291 | Adjusting for Chance Clustering Comparison Measures | stat.ML | Adjusted for chance measures are widely used to compare
partitions/clusterings of the same data set. In particular, the Adjusted Rand
Index (ARI) based on pair-counting, and the Adjusted Mutual Information (AMI)
based on Shannon information theory are very popular in the clustering
community. Nonetheless it is an open ... | computer science |
22,292 | Stochastic Collapsed Variational Inference for Hidden Markov Models | stat.ML | Stochastic variational inference for collapsed models has recently been
successfully applied to large scale topic modelling. In this paper, we propose
a stochastic collapsed variational inference algorithm for hidden Markov
models, in a sequential data setting. Given a collapsed hidden Markov Model, we
break its long M... | computer science |
22,293 | Stochastic Collapsed Variational Inference for Sequential Data | stat.ML | Stochastic variational inference for collapsed models has recently been
successfully applied to large scale topic modelling. In this paper, we propose
a stochastic collapsed variational inference algorithm in the sequential data
setting. Our algorithm is applicable to both finite hidden Markov models and
hierarchical D... | computer science |
22,294 | Learning population and subject-specific brain connectivity networks via
Mixed Neighborhood Selection | stat.ML | In neuroimaging data analysis, Gaussian graphical models are often used to
model statistical dependencies across spatially remote brain regions known as
functional connectivity. Typically, data is collected across a cohort of
subjects and the scientific objectives consist of estimating population and
subject-specific g... | computer science |
22,295 | Gibbs-type Indian buffet processes | stat.ML | We investigate a class of feature allocation models that generalize the
Indian buffet process and are parameterized by Gibbs-type random measures. Two
existing classes are contained as special cases: the original two-parameter
Indian buffet process, corresponding to the Dirichlet process, and the stable
(or three-param... | computer science |
22,296 | Inference in topic models: sparsity and trade-off | stat.ML | Topic models are popular for modeling discrete data (e.g., texts, images,
videos, links), and provide an efficient way to discover hidden
structures/semantics in massive data. One of the core problems in this field is
the posterior inference for individual data instances. This problem is
particularly important in strea... | computer science |
22,297 | Guaranteed inference in topic models | stat.ML | One of the core problems in statistical models is the estimation of a
posterior distribution. For topic models, the problem of posterior inference
for individual texts is particularly important, especially when dealing with
data streams, but is often intractable in the worst case. As a consequence,
existing methods for... | computer science |
22,298 | Cross-Validated Variable Selection in Tree-Based Methods Improves
Predictive Performance | stat.ML | Recursive partitioning approaches producing tree-like models are a long
standing staple of predictive modeling, in the last decade mostly as
``sub-learners'' within state of the art ensemble methods like Boosting and
Random Forest. However, a fundamental flaw in the partitioning (or splitting)
rule of commonly used tre... | computer science |
22,299 | Relative Density and Exact Recovery in Heterogeneous Stochastic Block
Models | stat.ML | The Stochastic Block Model (SBM) is a widely used random graph model for
networks with communities. Despite the recent burst of interest in recovering
communities in the SBM from statistical and computational points of view, there
are still gaps in understanding the fundamental information theoretic and
computational l... | computer science |
22,300 | Learning a Hybrid Architecture for Sequence Regression and Annotation | stat.ML | When learning a hidden Markov model (HMM), sequen- tial observations can
often be complemented by real-valued summary response variables generated from
the path of hid- den states. Such settings arise in numerous domains, includ-
ing many applications in biology, like motif discovery and genome annotation.
In this pape... | computer science |
22,301 | A Theoretically Grounded Application of Dropout in Recurrent Neural
Networks | stat.ML | Recurrent neural networks (RNNs) stand at the forefront of many recent
developments in deep learning. Yet a major difficulty with these models is
their tendency to overfit, with dropout shown to fail when applied to recurrent
layers. Recent results at the intersection of Bayesian modelling and deep
learning offer a Bay... | computer science |
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