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22,602 | Uniform Inference for High-dimensional Quantile Regression: Linear
Functionals and Regression Rank Scores | stat.ML | Hypothesis tests in models whose dimension far exceeds the sample size can be
formulated much like the classical studentized tests only after the initial
bias of estimation is removed successfully. The theory of debiased estimators
can be developed in the context of quantile regression models for a fixed
quantile value... | computer science |
22,603 | A Continuum of Optimal Primal-Dual Algorithms for Convex Composite
Minimization Problems with Applications to Structured Sparsity | stat.ML | Many statistical learning problems can be posed as minimization of a sum of
two convex functions, one typically a composition of non-smooth and linear
functions. Examples include regression under structured sparsity assumptions.
Popular algorithms for solving such problems, e.g., ADMM, often involve
non-trivial optimiz... | computer science |
22,604 | Column normalization of a random measurement matrix | stat.ML | In this note we answer a question of G. Lecu\'{e}, by showing that column
normalization of a random matrix with iid entries need not lead to good sparse
recovery properties, even if the generating random variable has a reasonable
moment growth. Specifically, for every $2 \leq p \leq c_1\log d$ we construct a
random vec... | computer science |
22,605 | A Unified Framework for Low-Rank plus Sparse Matrix Recovery | stat.ML | We propose a unified framework to solve general low-rank plus sparse matrix
recovery problems based on matrix factorization, which covers a broad family of
objective functions satisfying the restricted strong convexity and smoothness
conditions. Based on projected gradient descent and the double thresholding
operator, ... | computer science |
22,606 | A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big
Data | stat.ML | Partial Least Squares (PLS) methods have been heavily exploited to analyse
the association between two blocs of data. These powerful approaches can be
applied to data sets where the number of variables is greater than the number
of observations and in presence of high collinearity between variables.
Different sparse ve... | computer science |
22,607 | Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for
Incomplete Data | stat.ML | In this paper, we propose PCKID, a novel, robust, kernel function for
spectral clustering, specifically designed to handle incomplete data. By
combining posterior distributions of Gaussian Mixture Models for incomplete
data on different scales, we are able to learn a kernel for incomplete data
that does not depend on a... | computer science |
22,608 | Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm | stat.ML | Learning rates for regularized least-squares algorithms are in most cases
expressed with respect to the excess risk, or equivalently, the $L_2$-norm. For
some applications, however, guarantees with respect to stronger norms such as
the $L_\infty$-norm, are desirable. We address this problem by establishing
learning rat... | computer science |
22,609 | Deep Nonparametric Estimation of Discrete Conditional Distributions via
Smoothed Dyadic Partitioning | stat.ML | We present an approach to deep estimation of discrete conditional probability
distributions. Such models have several applications, including generative
modeling of audio, image, and video data. Our approach combines two main
techniques: dyadic partitioning and graph-based smoothing of the discrete
space. By recursivel... | computer science |
22,610 | GapTV: Accurate and Interpretable Low-Dimensional Regression and
Classification | stat.ML | We consider the problem of estimating a regression function in the common
situation where the number of features is small, where interpretability of the
model is a high priority, and where simple linear or additive models fail to
provide adequate performance. To address this problem, we present GapTV, an
approach that ... | computer science |
22,611 | Microwave breast cancer detection using Empirical Mode Decomposition
features | stat.ML | Microwave-based breast cancer detection has been proposed as a complementary
approach to compensate for some drawbacks of existing breast cancer detection
techniques. Among the existing microwave breast cancer detection methods,
machine learning-type algorithms have recently become more popular. These focus
on detectin... | computer science |
22,612 | Bayesian inference on random simple graphs with power law degree
distributions | stat.ML | We present a model for random simple graphs with a degree distribution that
obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge
probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor
(BFRY) random variables, which have been recently utilized in Bayesian
statistics for the c... | computer science |
22,613 | An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov
Network Estimation | stat.ML | The pseudo-likelihood method is one of the most popular algorithms for
learning sparse binary pairwise Markov networks. In this paper, we formulate
the $L_1$ regularized pseudo-likelihood problem as a sparse multiple logistic
regression problem. In this way, many insights and optimization procedures for
sparse logistic... | computer science |
22,614 | A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable
Couplings | stat.ML | We introduce a novel kernel that models input-dependent couplings across
multiple latent processes. The pairwise joint kernel measures covariance along
inputs and across different latent signals in a mutually-dependent fashion. A
latent correlation Gaussian process (LCGP) model combines these non-stationary
latent comp... | computer science |
22,615 | Embarrassingly parallel inference for Gaussian processes | stat.ML | Training Gaussian process-based models typically involves an $ O(N^3)$
computational bottleneck. Popular methods for overcoming this matrix inversion
problem cannot adequately model all types of latent functions, and are often
not parallelizable. We present an embarrassingly parallel method that takes
advantage of inve... | computer science |
22,616 | Semi-supervised Learning based on Distributionally Robust Optimization | stat.ML | We propose a novel method for semi-supervised learning (SSL) based on
data-driven distributionally robust optimization (DRO) using optimal transport
metrics. Our proposed method enhances generalization error by using the
unlabeled data to restrict the support of the worst case distribution in our
DRO formulation. We en... | computer science |
22,617 | The Second Order Linear Model | stat.ML | We study a fundamental class of regression models called the second order
linear model (SLM). The SLM extends the linear model to high order functional
space and has attracted considerable research interest recently. Yet how to
efficiently learn the SLM under full generality using nonconvex solver still
remains an open... | computer science |
22,618 | Linearly constrained Gaussian processes | stat.ML | We consider a modification of the covariance function in Gaussian processes
to correctly account for known linear constraints. By modelling the target
function as a transformation of an underlying function, the constraints are
explicitly incorporated in the model such that they are guaranteed to be
fulfilled by any sam... | computer science |
22,619 | Gauging Variational Inference | stat.ML | Computing partition function is the most important statistical inference task
arising in applications of Graphical Models (GM). Since it is computationally
intractable, approximate methods have been used to resolve the issue in
practice, where mean-field (MF) and belief propagation (BP) are arguably the
most popular an... | computer science |
22,620 | An unsupervised bayesian approach for the joint reconstruction and
classification of cutaneous reflectance confocal microscopy images | stat.ML | This paper studies a new Bayesian algorithm for the joint reconstruction and
classification of reflectance confocal microscopy (RCM) images, with
application to the identification of human skin lentigo. The proposed Bayesian
approach takes advantage of the distribution of the multiplicative speckle
noise affecting the ... | computer science |
22,621 | Autoencoding Variational Inference For Topic Models | stat.ML | Topic models are one of the most popular methods for learning representations
of text, but a major challenge is that any change to the topic model requires
mathematically deriving a new inference algorithm. A promising approach to
address this problem is autoencoding variational Bayes (AEVB), but it has
proven diffi- c... | computer science |
22,622 | A Statistical Machine Learning Approach to Yield Curve Forecasting | stat.ML | Yield curve forecasting is an important problem in finance. In this work we
explore the use of Gaussian Processes in conjunction with a dynamic modeling
strategy, much like the Kalman Filter, to model the yield curve. Gaussian
Processes have been successfully applied to model functional data in a variety
of application... | computer science |
22,623 | Soft-DTW: a Differentiable Loss Function for Time-Series | stat.ML | We propose in this paper a differentiable learning loss between time series,
building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the
Euclidean distance, DTW can compare time series of variable size and is robust
to shifts or dilatations across the time dimension. To compute DTW, one
typically so... | computer science |
22,624 | Forward and Reverse Gradient-Based Hyperparameter Optimization | stat.ML | We study two procedures (reverse-mode and forward-mode) for computing the
gradient of the validation error with respect to the hyperparameters of any
iterative learning algorithm such as stochastic gradient descent. These
procedures mirror two methods of computing gradients for recurrent neural
networks and have differ... | computer science |
22,625 | Computational Eco-Systems for Handwritten Digits Recognition | stat.ML | Inspired by the importance of diversity in biological system, we built an
heterogeneous system that could achieve this goal. Our architecture could be
summarized in two basic steps. First, we generate a diverse set of
classification hypothesis using both Convolutional Neural Networks, currently
the state-of-the-art tec... | computer science |
22,626 | Grammar Variational Autoencoder | stat.ML | Deep generative models have been wildly successful at learning coherent
latent representations for continuous data such as video and audio. However,
generative modeling of discrete data such as arithmetic expressions and
molecular structures still poses significant challenges. Crucially,
state-of-the-art methods often ... | computer science |
22,627 | Probabilistic Reduced-Order Modeling for Stochastic Partial Differential
Equations | stat.ML | We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the
coefficients (e.g. material parameters) exhibit random, fine scale variability.
The direct solution to such problems requires grids that are small enough to
resolve this fine scale variability which unavoidably requires the repeated
solution of... | computer science |
22,628 | Max-value Entropy Search for Efficient Bayesian Optimization | stat.ML | Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a
compelling information-theoretic motivation, and maximize the information
gained about the $\arg\max$ of the unknown function; yet, both are plagued by
the expensive computatio... | computer science |
22,629 | Batched High-dimensional Bayesian Optimization via Structural Kernel
Learning | stat.ML | Optimization of high-dimensional black-box functions is an extremely
challenging problem. While Bayesian optimization has emerged as a popular
approach for optimizing black-box functions, its applicability has been limited
to low-dimensional problems due to its computational and statistical challenges
arising from high... | computer science |
22,630 | Global optimization of Lipschitz functions | stat.ML | The goal of the paper is to design sequential strategies which lead to
efficient optimization of an unknown function under the only assumption that it
has a finite Lipschitz constant. We first identify sufficient conditions for
the consistency of generic sequential algorithms and formulate the expected
minimax rate for... | computer science |
22,631 | Polynomial Time Algorithms for Dual Volume Sampling | stat.ML | We study dual volume sampling, a method for selecting k columns from an n x m
short and wide matrix (n <= k <= m) such that the probability of selection is
proportional to the volume spanned by the rows of the induced submatrix. This
method was proposed by Avron and Boutsidis (2013), who showed it to be a
promising met... | computer science |
22,632 | Trimmed Density Ratio Estimation | stat.ML | Density ratio estimation is a vital tool in both machine learning and
statistical community. However, due to the unbounded nature of density ratio,
the estimation procedure can be vulnerable to corrupted data points, which
often pushes the estimated ratio toward infinity. In this paper, we present a
robust estimator wh... | computer science |
22,633 | mlrMBO: A Modular Framework for Model-Based Optimization of Expensive
Black-Box Functions | stat.ML | We present mlrMBO, a flexible and comprehensive R toolbox for model-based
optimization (MBO), also known as Bayesian optimization, which addresses the
problem of expensive black-box optimization by approximating the given
objective function through a surrogate regression model. It is designed for
both single- and multi... | computer science |
22,634 | Parallel Markov Chain Monte Carlo for the Indian Buffet Process | stat.ML | Indian Buffet Process based models are an elegant way for discovering
underlying features within a data set, but inference in such models can be
slow. Inferring underlying features using Markov chain Monte Carlo either
relies on an uncollapsed representation, which leads to poor mixing, or on a
collapsed representation... | computer science |
22,635 | Density Level Set Estimation on Manifolds with DBSCAN | stat.ML | We show that DBSCAN can estimate the connected components of the
$\lambda$-density level set $\{ x : f(x) \ge \lambda\}$ given $n$ i.i.d.
samples from an unknown density $f$. We characterize the regularity of the
level set boundaries using parameter $\beta > 0$ and analyze the estimation
error under the Hausdorff metri... | computer science |
22,636 | DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw
Single-Channel EEG | stat.ML | The present study proposes a deep learning model, named DeepSleepNet, for
automatic sleep stage scoring based on raw single-channel EEG. Most of the
existing methods rely on hand-engineered features which require prior knowledge
of sleep analysis. Only a few of them encode the temporal information such as
transition ru... | computer science |
22,637 | Practical Bayesian Optimization for Variable Cost Objectives | stat.ML | We propose a novel Bayesian Optimization approach for black-box functions
with an environmental variable whose value determines the tradeoff between
evaluation cost and the fidelity of the evaluations. Further, we use a novel
approach to sampling support points, allowing faster construction of the
acquisition function.... | computer science |
22,638 | Multivariate Gaussian and Student$-t$ Process Regression for
Multi-output Prediction | stat.ML | Gaussian process for vector-valued function model has been shown to be a
useful method for multi-output prediction. The existing method for this model
is to re-formulate the matrix-variate Gaussian distribution as a multivariate
normal distribution. Although it is effective in many cases, re-formulation is
not always w... | computer science |
22,639 | Conditional Time Series Forecasting with Convolutional Neural Networks | stat.ML | We present a method for conditional time series forecasting based on the
recent deep convolutional WaveNet architecture. The proposed network contains
stacks of dilated convolutions that allow it to access a broad range of history
when forecasting; multiple convolutional filters are applied in parallel to
separate time... | computer science |
22,640 | A statistical model for aggregating judgments by incorporating peer
predictions | stat.ML | We propose a probabilistic model to aggregate the answers of respondents
answering multiple-choice questions. The model does not assume that everyone
has access to the same information, and so does not assume that the consensus
answer is correct. Instead, it infers the most probable world state, even if
only a minority... | computer science |
22,641 | A Random Finite Set Model for Data Clustering | stat.ML | The goal of data clustering is to partition data points into groups to
minimize a given objective function. While most existing clustering algorithms
treat each data point as vector, in many applications each datum is not a
vector but a point pattern or a set of points. Moreover, many existing
clustering methods requir... | computer science |
22,642 | Optimization for L1-Norm Error Fitting via Data Aggregation | stat.ML | We propose a data aggregation-based algorithm with monotonic convergence to a
global optimum for a generalized version of the L1-norm error fitting model
with an assumption of the fitting function. Any L1-norm model can be solved
optimally using the proposed algorithm if it follows the form of the L1-norm
error fitting... | computer science |
22,643 | Adaptivity to Noise Parameters in Nonparametric Active Learning | stat.ML | This work addresses various open questions in the theory of active learning
for nonparametric classification. Our contributions are both statistical and
algorithmic: -We establish new minimax-rates for active learning under common
\textit{noise conditions}. These rates display interesting transitions -- due
to the inte... | computer science |
22,644 | On Consistency of Graph-based Semi-supervised Learning | stat.ML | Graph-based semi-supervised learning is one of the most popular methods in
machine learning. Some of its theoretical properties such as bounds for the
generalization error and the convergence of the graph Laplacian regularizer
have been studied in computer science and statistics literatures. However, a
fundamental stat... | computer science |
22,645 | Multi-fidelity Bayesian Optimisation with Continuous Approximations | stat.ML | Bandit methods for black-box optimisation, such as Bayesian optimisation, are
used in a variety of applications including hyper-parameter tuning and
experiment design. Recently, \emph{multi-fidelity} methods have garnered
considerable attention since function evaluations have become increasingly
expensive in such appli... | computer science |
22,646 | Practical Coreset Constructions for Machine Learning | stat.ML | We investigate coresets - succinct, small summaries of large data sets - so
that solutions found on the summary are provably competitive with solution
found on the full data set. We provide an overview over the state-of-the-art in
coreset construction for machine learning. In Section 2, we present both the
intuition be... | computer science |
22,647 | Universal Consistency and Robustness of Localized Support Vector
Machines | stat.ML | The massive amount of available data potentially used to discover patters in
machine learning is a challenge for kernel based algorithms with respect to
runtime and storage capacities. Local approaches might help to relieve these
issues. From a statistical point of view local approaches allow additionally to
deal with ... | computer science |
22,648 | Testing and Learning on Distributions with Symmetric Noise Invariance | stat.ML | Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD),
the resulting distance between distributions, are useful tools for fully
nonparametric two-sample testing and learning on distributions. However, it is
rarely that all possible differences between samples are of interest --
discovered difference... | computer science |
22,649 | Distribution of Gaussian Process Arc Lengths | stat.ML | We present the first treatment of the arc length of the Gaussian Process (GP)
with more than a single output dimension. GPs are commonly used for tasks such
as trajectory modelling, where path length is a crucial quantity of interest.
Previously, only paths in one dimension have been considered, with no
theoretical con... | computer science |
22,650 | Robustness of Maximum Correntropy Estimation Against Large Outliers | stat.ML | The maximum correntropy criterion (MCC) has recently been successfully
applied in robust regression, classification and adaptive filtering, where the
correntropy is maximized instead of minimizing the well-known mean square error
(MSE) to improve the robustness with respect to outliers (or impulsive noises).
Considerab... | computer science |
22,651 | Reducing Crowdsourcing to Graphon Estimation, Statistically | stat.ML | Inferring the correct answers to binary tasks based on multiple noisy answers
in an unsupervised manner has emerged as the canonical question for micro-task
crowdsourcing or more generally aggregating opinions. In graphon estimation,
one is interested in estimating edge intensities or probabilities between nodes
using ... | computer science |
22,652 | Training Gaussian Mixture Models at Scale via Coresets | stat.ML | How can we train a statistical mixture model on a massive data set? In this
work we show how to construct coresets for mixtures of Gaussians. A coreset is
a weighted subset of the data, which guarantees that models fitting the coreset
also provide a good fit for the original data set. We show that, perhaps
surprisingly... | computer science |
22,653 | The Dependence of Machine Learning on Electronic Medical Record Quality | stat.ML | There is growing interest in applying machine learning methods to Electronic
Medical Records (EMR). Across different institutions, however, EMR quality can
vary widely. This work investigated the impact of this disparity on the
performance of three advanced machine learning algorithms: logistic regression,
multilayer p... | computer science |
22,654 | Binarsity: a penalization for one-hot encoded features | stat.ML | This paper deals with the problem of large-scale linear supervised learning
in settings where a large number of continuous features are available. We
propose to combine the well-known trick of one-hot encoding of continuous
features with a new penalization called binarsity. In each group of binary
features coming from ... | computer science |
22,655 | Learning to Predict: A Fast Re-constructive Method to Generate
Multimodal Embeddings | stat.ML | Integrating visual and linguistic information into a single multimodal
representation is an unsolved problem with wide-reaching applications to both
natural language processing and computer vision. In this paper, we present a
simple method to build multimodal representations by learning a
language-to-vision mapping and... | computer science |
22,656 | A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis | stat.ML | Existing strategies for finite-armed stochastic bandits mostly depend on a
parameter of scale that must be known in advance. Sometimes this is in the form
of a bound on the payoffs, or the knowledge of a variance or subgaussian
parameter. The notable exceptions are the analysis of Gaussian bandits with
unknown mean and... | computer science |
22,657 | Thompson Sampling for Linear-Quadratic Control Problems | stat.ML | We consider the exploration-exploitation tradeoff in linear quadratic (LQ)
control problems, where the state dynamics is linear and the cost function is
quadratic in states and controls. We analyze the regret of Thompson sampling
(TS) (a.k.a. posterior-sampling for reinforcement learning) in the frequentist
setting, i.... | computer science |
22,658 | Multilabel Classification with R Package mlr | stat.ML | We implemented several multilabel classification algorithms in the machine
learning package mlr. The implemented methods are binary relevance, classifier
chains, nested stacking, dependent binary relevance and stacking, which can be
used with any base learner that is accessible in mlr. Moreover, there is access
to the ... | computer science |
22,659 | Sparse Multi-Output Gaussian Processes for Medical Time Series
Prediction | stat.ML | In real-time monitoring of hospital patients, high-quality inference of
patients' health status using all information available from clinical
covariates and lab tests are essential to enable successful medical
interventions and improve patient outcomes. In this work, we develop and
explore a Bayesian nonparametric mode... | computer science |
22,660 | PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for
Low-Dose 3D CT Image Reconstruction | stat.ML | The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure while maintaining high image
quality is an important area of research in low-dose CT (LDCT) imaging. We
propose a new penalized weighted least squares (PWLS) reconstruction method
that exploits ... | computer science |
22,661 | Fairness in Criminal Justice Risk Assessments: The State of the Art | stat.ML | Objectives: Discussions of fairness in criminal justice risk assessments
typically lack conceptual precision. Rhetoric too often substitutes for careful
analysis. In this paper, we seek to clarify the tradeoffs between different
kinds of fairness and between fairness and accuracy.
Methods: We draw on the existing lit... | computer science |
22,662 | Discovering Explainable Latent Covariance Structure for Multiple Time
Series | stat.ML | Analyzing time series data is important to predict future events and changes
in finance, manufacturing, and administrative decisions. Gaussian processes
(GPs) solve regression and classification problems by choosing appropriate
kernels capturing covariance structure of data. In time series analysis, GP
based regression... | computer science |
22,663 | Algebraic Variety Models for High-Rank Matrix Completion | stat.ML | We consider a generalization of low-rank matrix completion to the case where
the data belongs to an algebraic variety, i.e. each data point is a solution to
a system of polynomial equations. In this case the original matrix is possibly
high-rank, but it becomes low-rank after mapping each column to a higher
dimensional... | computer science |
22,664 | Gradient-based Regularization Parameter Selection for Problems with
Non-smooth Penalty Functions | stat.ML | In high-dimensional and/or non-parametric regression problems, regularization
(or penalization) is used to control model complexity and induce desired
structure. Each penalty has a weight parameter that indicates how strongly the
structure corresponding to that penalty should be enforced. Typically the
parameters are c... | computer science |
22,665 | Improving Spectral Clustering using the Asymptotic Value of the
Normalised Cut | stat.ML | Spectral clustering is a popular and versatile clustering method based on a
relaxation of the normalised graph cut objective. Despite its popularity,
however, there is no single agreed upon method for tuning the important scaling
parameter, nor for determining automatically the number of clusters to extract.
Popular he... | computer science |
22,666 | Optimal Policies for Observing Time Series and Related Restless Bandit
Problems | stat.ML | The trade-off between the cost of acquiring and processing data, and
uncertainty due to a lack of data is fundamental in machine learning. A basic
instance of this trade-off is the problem of deciding when to make noisy and
costly observations of a discrete-time Gaussian random walk, so as to minimise
the posterior var... | computer science |
22,667 | Intraoperative margin assessment of human breast tissue in optical
coherence tomography images using deep neural networks | stat.ML | Objective: In this work, we perform margin assessment of human breast tissue
from optical coherence tomography (OCT) images using deep neural networks
(DNNs). This work simulates an intraoperative setting for breast cancer
lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art
methods (Weight Decay an... | computer science |
22,668 | The Risk of Machine Learning | stat.ML | Many applied settings in empirical economics involve simultaneous estimation
of a large number of parameters. In particular, applied economists are often
interested in estimating the effects of many-valued treatments (like teacher
effects or location effects), treatment effects for many groups, and prediction
models wi... | computer science |
22,669 | Prediction of infectious disease epidemics via weighted density
ensembles | stat.ML | Accurate and reliable predictions of infectious disease dynamics can be
valuable to public health organizations that plan interventions to decrease or
prevent disease transmission. A great variety of models have been developed for
this task, using different model structures, covariates, and targets for
prediction. Expe... | computer science |
22,670 | Exploiting gradients and Hessians in Bayesian optimization and Bayesian
quadrature | stat.ML | An exciting branch of machine learning research focuses on methods for
learning, optimizing, and integrating unknown functions that are difficult or
costly to evaluate. A popular Bayesian approach to this problem uses a Gaussian
process (GP) to construct a posterior distribution over the function of
interest given a se... | computer science |
22,671 | Dictionary-based Tensor Canonical Polyadic Decomposition | stat.ML | To ensure interpretability of extracted sources in tensor decomposition, we
introduce in this paper a dictionary-based tensor canonical polyadic
decomposition which enforces one factor to belong exactly to a known
dictionary. A new formulation of sparse coding is proposed which enables high
dimensional tensors dictiona... | computer science |
22,672 | On the construction of probabilistic Newton-type algorithms | stat.ML | It has recently been shown that many of the existing quasi-Newton algorithms
can be formulated as learning algorithms, capable of learning local models of
the cost functions. Importantly, this understanding allows us to safely start
assembling probabilistic Newton-type algorithms, applicable in situations where
we only... | computer science |
22,673 | Detecting confounding in multivariate linear models via spectral
analysis | stat.ML | We study a model where one target variable Y is correlated with a vector
X:=(X_1,...,X_d) of predictor variables being potential causes of Y. We
describe a method that infers to what extent the statistical dependences
between X and Y are due to the influence of X on Y and to what extent due to a
hidden common cause (co... | computer science |
22,674 | Massive Data Clustering in Moderate Dimensions from the Dual Spaces of
Observation and Attribute Data Clouds | stat.ML | Cluster analysis of very high dimensional data can benefit from the
properties of such high dimensionality. Informally expressed, in this work, our
focus is on the analogous situation when the dimensionality is moderate to
small, relative to a massively sized set of observations. Mathematically
expressed, these are the... | computer science |
22,675 | Angle-Based Joint and Individual Variation Explained | stat.ML | Integrative analysis of disparate data blocks measured on a common set of
experimental subjects is a major challenge in modern data analysis. This data
structure naturally motivates the simultaneous exploration of the joint and
individual variation within each data block resulting in new insights. For
instance, there i... | computer science |
22,676 | When is Network Lasso Accurate? | stat.ML | The "least absolute shrinkage and selection operator" (Lasso) method has been
adapted recently for networkstructured datasets. In particular, this network
Lasso method allows to learn graph signals from a small number of noisy signal
samples by using the total variation of a graph signal for regularization.
While effic... | computer science |
22,677 | Locally-adapted convolution-based super-resolution of
irregularly-sampled ocean remote sensing data | stat.ML | Super-resolution is a classical problem in image processing, with numerous
applications to remote sensing image enhancement. Here, we address the
super-resolution of irregularly-sampled remote sensing images. Using an optimal
interpolation as the low-resolution reconstruction, we explore locally-adapted
multimodal conv... | computer science |
22,678 | A Brief Introduction to the Temporal Group LASSO and its Potential
Applications in Healthcare | stat.ML | The Temporal Group LASSO is an example of a multi-task, regularized
regression approach for the prediction of response variables that vary over
time. The aim of this work is to introduce the reader to the concepts behind
the Temporal Group LASSO and its related methods, as well as to the type of
potential applications ... | computer science |
22,679 | Interactive Graphics for Visually Diagnosing Forest Classifiers in R | stat.ML | This paper describes structuring data and constructing plots to explore
forest classification models interactively. A forest classifier is an example
of an ensemble, produced by bagging multiple trees. The process of bagging and
combining results from multiple trees, produces numerous diagnostics which,
with interactiv... | computer science |
22,680 | Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic
Factor | stat.ML | In this paper we study the problem of noisy tensor completion for tensors
that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with
one of the factors being sparse. We present general theoretical error bounds
for an estimate obtained by using a complexity-regularized maximum likelihood
principle and ... | computer science |
22,681 | Strictly Proper Kernel Scoring Rules and Divergences with an Application
to Kernel Two-Sample Hypothesis Testing | stat.ML | We study strictly proper scoring rules in the Reproducing Kernel Hilbert
Space. We propose a general Kernel Scoring rule and associated Kernel
Divergence. We consider conditions under which the Kernel Score is strictly
proper. We then demonstrate that the Kernel Score includes the Maximum Mean
Discrepancy as a special ... | computer science |
22,682 | Integral Transforms from Finite Data: An Application of Gaussian Process
Regression to Fourier Analysis | stat.ML | Computing accurate estimates of the Fourier transform of analog signals from
discrete data points is important in many fields of science and engineering.
The conventional approach of performing the discrete Fourier transform of the
data implicitly assumes periodicity and bandlimitedness of the signal. In this
paper, we... | computer science |
22,683 | Reinterpreting Importance-Weighted Autoencoders | stat.ML | The standard interpretation of importance-weighted autoencoders is that they
maximize a tighter lower bound on the marginal likelihood than the standard
evidence lower bound. We give an alternate interpretation of this procedure:
that it optimizes the standard variational lower bound, but using a more
complex distribut... | computer science |
22,684 | Preferential Bayesian Optimization | stat.ML | Bayesian optimization (BO) has emerged during the last few years as an
effective approach to optimizing black-box functions where direct queries of
the objective are expensive. In this paper we consider the case where direct
access to the function is not possible, but information about user preferences
is. Such scenari... | computer science |
22,685 | Infinite Sparse Structured Factor Analysis | stat.ML | Matrix factorisation methods decompose multivariate observations as linear
combinations of latent feature vectors. The Indian Buffet Process (IBP)
provides a way to model the number of latent features required for a good
approximation in terms of regularised reconstruction error. Previous work has
focussed on latent fe... | computer science |
22,686 | Projection Free Rank-Drop Steps | stat.ML | The Frank-Wolfe (FW) algorithm has been widely used in solving nuclear norm
constrained problems, since it does not require projections. However, FW often
yields high rank intermediate iterates, which can be very expensive in time and
space costs for large problems. To address this issue, we propose a rank-drop
method ... | computer science |
22,687 | k-Means is a Variational EM Approximation of Gaussian Mixture Models | stat.ML | We show that k-means (Lloyd's algorithm) is equivalent to a variational EM
approximation of a Gaussian Mixture Model (GMM) with isotropic Gaussians. The
k-means algorithm is obtained if truncated posteriors are used as variational
distributions. In contrast to the standard way to relate k-means and GMMs, we
show that i... | computer science |
22,688 | Boosting with Structural Sparsity: A Differential Inclusion Approach | stat.ML | Boosting as gradient descent algorithms is one popular method in machine
learning. In this paper a novel Boosting-type algorithm is proposed based on
restricted gradient descent with structural sparsity control whose underlying
dynamics are governed by differential inclusions. In particular, we present an
iterative reg... | computer science |
22,689 | Accelerated Distributed Dual Averaging over Evolving Networks of Growing
Connectivity | stat.ML | We consider the problem of accelerating distributed optimization in
multi-agent networks by sequentially adding edges. Specifically, we extend the
distributed dual averaging (DDA) subgradient algorithm to evolving networks of
growing connectivity and analyze the corresponding improvement in convergence
rate. It is know... | computer science |
22,690 | Stein Variational Adaptive Importance Sampling | stat.ML | We propose a novel adaptive importance sampling algorithm which incorporates
Stein variational gradient decent algorithm (SVGD) with importance sampling
(IS). Our algorithm leverages the nonparametric transforms in SVGD to
iteratively decrease the KL divergence between our importance proposal and the
target distributio... | computer science |
22,691 | Importance Sampled Stochastic Optimization for Variational Inference | stat.ML | Variational inference approximates the posterior distribution of a
probabilistic model with a parameterized density by maximizing a lower bound
for the model evidence. Modern solutions fit a flexible approximation with
stochastic gradient descent, using Monte Carlo approximation for the gradients.
This enables variatio... | computer science |
22,692 | Noise-Tolerant Interactive Learning from Pairwise Comparisons | stat.ML | We study the problem of interactively learning a binary classifier using
noisy labeling and pairwise comparison oracles, where the comparison oracle
answers which one in the given two instances is more likely to be positive.
Learning from such oracles has multiple applications where obtaining direct
labels is harder bu... | computer science |
22,693 | Asynchronous Distributed Variational Gaussian Processes for Regression | stat.ML | Gaussian processes (GPs) are powerful non-parametric function estimators.
However, their applications are largely limited by the expensive computational
cost of the inference procedures. Existing stochastic or distributed
synchronous variational inferences, although have alleviated this issue by
scaling up GPs to milli... | computer science |
22,694 | Consistency of community detection in multi-layer networks using
spectral and matrix factorization methods | stat.ML | We consider the problem of estimating a consensus community structure by
combining information from multiple layers of a multi-layer network or multiple
snapshots of a time-varying network. Numerous methods have been proposed in the
literature for the more general problem of multi-view clustering in the past
decade bas... | computer science |
22,695 | Stein Variational Gradient Descent as Gradient Flow | stat.ML | Stein variational gradient descent (SVGD) is a deterministic sampling
algorithm that iteratively transports a set of particles to approximate given
distributions, based on an efficient gradient-based update that guarantees to
optimally decrease the KL divergence within a function space. This paper
develops the first th... | computer science |
22,696 | A relevance-scalability-interpretability tradeoff with temporally
evolving user personas | stat.ML | The current work characterizes the users of a VoD streaming space through
user-personas based on a tenure timeline and temporal behavioral features in
the absence of explicit user profiles. A combination of tenure timeline and
temporal characteristics caters to business needs of understanding the
evolution and phases o... | computer science |
22,697 | Estimating the coefficients of a mixture of two linear regressions by
expectation maximization | stat.ML | We give convergence guarantees for estimating the coefficients of a symmetric
mixture of two linear regressions by expectation maximization (EM). In
particular, we show that convergence of the empirical iterates is guaranteed
provided the algorithm is initialized in an unbounded cone. That is, if the
initializer has a ... | computer science |
22,698 | Structured Sparse Modelling with Hierarchical GP | stat.ML | In this paper a new Bayesian model for sparse linear regression with a
spatio-temporal structure is proposed. It incorporates the structural
assumptions based on a hierarchical Gaussian process prior for spike and slab
coefficients. We design an inference algorithm based on Expectation Propagation
and evaluate the mode... | computer science |
22,699 | Prediction of Daytime Hypoglycemic Events Using Continuous Glucose
Monitoring Data and Classification Technique | stat.ML | Daytime hypoglycemia should be accurately predicted to achieve normoglycemia
and to avoid disastrous situations. Hypoglycemia, an abnormally low blood
glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia.
Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In
this paper, ... | computer science |
22,700 | Ensemble Sales Forecasting Study in Semiconductor Industry | stat.ML | Sales forecasting plays a prominent role in business planning and business
strategy. The value and importance of advance information is a cornerstone of
planning activity, and a well-set forecast goal can guide sale-force more
efficiently. In this paper CPU sales forecasting of Intel Corporation, a
multinational semico... | computer science |
22,701 | Stochastic Divergence Minimization for Biterm Topic Model | stat.ML | As the emergence and the thriving development of social networks, a huge
number of short texts are accumulated and need to be processed. Inferring
latent topics of collected short texts is useful for understanding its hidden
structure and predicting new contents. Unlike conventional topic models such as
latent Dirichle... | computer science |
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