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22,802 | Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
Gaussian Process Hybrid Deep Networks | stat.ML | Deep neural networks (DNNs) have excellent representative power and are state
of the art classifiers on many tasks. However, they often do not capture their
own uncertainties well making them less robust in the real world as they
overconfidently extrapolate and do not notice domain shift. Gaussian processes
(GPs) with ... | computer science |
22,803 | Block modelling in dynamic networks with non-homogeneous Poisson
processes and exact ICL | stat.ML | We develop a model in which interactions between nodes of a dynamic network
are counted by non homogeneous Poisson processes. In a block modelling
perspective, nodes belong to hidden clusters (whose number is unknown) and the
intensity functions of the counting processes only depend on the clusters of
nodes. In order t... | computer science |
22,804 | An Interactive Greedy Approach to Group Sparsity in High Dimension | stat.ML | Sparsity learning with known grouping structures has received considerable
attention due to wide modern applications in high-dimensional data analysis.
Although advantages of using group information have been well-studied by
shrinkage-based approaches, benefits of group sparsity have not been
well-documented for greedy... | computer science |
22,805 | Tick: a Python library for statistical learning, with a particular
emphasis on time-dependent modelling | stat.ML | Tick is a statistical learning library for Python~3, with a particular
emphasis on time-dependent models, such as point processes, and tools for
generalized linear models and survival analysis. The core of the library is an
optimization module providing model computational classes, solvers and proximal
operators for re... | computer science |
22,806 | Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck
process | stat.ML | Given the observation of a high-dimensional Ornstein-Uhlenbeck (OU) process
in continuous time, we proceed to the inference of the drift parameter under a
row-sparsity assumption. Towards that aim, we consider the negative
log-likelihood of the process, penalized by an $\ell^1$-penalization (Lasso and
Adaptive Lasso). ... | computer science |
22,807 | A Cluster Fusion Penalty for Grouping Response Variables in Multivariate
Regression Models | stat.ML | We propose a method for estimating coefficients in multivariate regression
when there is a clustering structure to the response variables. The proposed
method includes a fusion penalty, to shrink the difference in fitted values
from responses in the same cluster, and an L1 penalty for simultaneous variable
selection an... | computer science |
22,808 | Automation of Feature Engineering for IoT Analytics | stat.ML | This paper presents an approach for automation of interpretable feature
selection for Internet Of Things Analytics (IoTA) using machine learning (ML)
techniques. Authors have conducted a survey over different people involved in
different IoTA based application development tasks. The survey reveals that
feature selectio... | computer science |
22,809 | Distributionally Ambiguous Optimization Techniques in Batch Bayesian
Optimization | stat.ML | We propose a novel, theoretically-grounded, acquisition function for batch
Bayesian optimization informed by insights from distributionally ambiguous
optimization. Our acquisition function is a lower bound on the well-known
Expected Improvement function -- which requires a multi-dimensional Gaussian
Expectation over a ... | computer science |
22,810 | Kernel Method for Detecting Higher Order Interactions in multi-view
Data: An Application to Imaging, Genetics, and Epigenetics | stat.ML | In this study, we tested the interaction effect of multimodal datasets using
a novel method called the kernel method for detecting higher order interactions
among biologically relevant mulit-view data. Using a semiparametric method on a
reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects
linear mo... | computer science |
22,811 | On the Performance of Forecasting Models in the Presence of Input
Uncertainty | stat.ML | Nowadays, with the unprecedented penetration of renewable distributed energy
resources (DERs), the necessity of an efficient energy forecasting model is
more demanding than before. Generally, forecasting models are trained using
observed weather data while the trained models are applied for energy
forecasting using for... | computer science |
22,812 | An optimal unrestricted learning procedure | stat.ML | We study learning problems in the general setup, for arbitrary classes of
functions $F$, distributions $X$ and targets $Y$. Because proper learning
procedures, i.e., procedures that are only allowed to select functions in $F$,
tend to perform poorly unless the problem satisfies some additional structural
property (e.g.... | computer science |
22,813 | PAC-Bayes and Domain Adaptation | stat.ML | We provide two main contributions in PAC-Bayesian theory for domain
adaptation where the objective is to learn, from a source distribution, a
well-performing majority vote on a different, but related, target distribution.
Firstly, we propose an improvement of the previous approach we proposed in
Germain et al. (2013), ... | computer science |
22,814 | Improving Output Uncertainty Estimation and Generalization in Deep
Learning via Neural Network Gaussian Processes | stat.ML | We propose a simple method that combines neural networks and Gaussian
processes. The proposed method can estimate the uncertainty of outputs and
flexibly adjust target functions where training data exist, which are
advantages of Gaussian processes. The proposed method can also achieve high
generalization performance fo... | computer science |
22,815 | RKL: a general, invariant Bayes solution for Neyman-Scott | stat.ML | Neyman-Scott is a classic example of an estimation problem with a
partially-consistent posterior, for which standard estimation methods tend to
produce inconsistent results. Past attempts to create consistent estimators for
Neyman-Scott have led to ad-hoc solutions, to estimators that do not satisfy
representation inva... | computer science |
22,816 | Prolongation of SMAP to Spatio-temporally Seamless Coverage of
Continental US Using a Deep Learning Neural Network | stat.ML | The Soil Moisture Active Passive (SMAP) mission has delivered valuable
sensing of surface soil moisture since 2015. However, it has a short time span
and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
learning neural network, Long Short-Term Memory (LSTM), we created a system
that predicts SM... | computer science |
22,817 | Learning to Draw Samples with Amortized Stein Variational Gradient
Descent | stat.ML | We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient direction (Liu & Wang, 2016)
that maxima... | computer science |
22,818 | Graphical posterior predictive classifier: Bayesian model averaging with
particle Gibbs | stat.ML | In this study, we present a multi-class graphical Bayesian predictive
classifier that incorporates the uncertainty in the model selection into the
standard Bayesian formalism. For each class, the dependence structure
underlying the observed features is represented by a set of decomposable
Gaussian graphical models. Emp... | computer science |
22,819 | A signature-based machine learning model for bipolar disorder and
borderline personality disorder | stat.ML | Mobile technologies offer opportunities for higher resolution monitoring of
health conditions. This opportunity seems of particular promise in psychiatry
where diagnoses often rely on retrospective and subjective recall of mood
states. However, getting actionable information from these rather complex time
series is cha... | computer science |
22,820 | Health Analytics: a systematic review of approaches to detect phenotype
cohorts using electronic health records | stat.ML | The paper presents a systematic review of state-of-the-art approaches to
identify patient cohorts using electronic health records. It gives a
comprehensive overview of the most commonly de-tected phenotypes and its
underlying data sets. Special attention is given to preprocessing of in-put
data and the different modeli... | computer science |
22,821 | Dynamic Clustering Algorithms via Small-Variance Analysis of Markov
Chain Mixture Models | stat.ML | Bayesian nonparametrics are a class of probabilistic models in which the
model size is inferred from data. A recently developed methodology in this
field is small-variance asymptotic analysis, a mathematical technique for
deriving learning algorithms that capture much of the flexibility of Bayesian
nonparametric infere... | computer science |
22,822 | Signal and Noise Statistics Oblivious Sparse Reconstruction using
OMP/OLS | stat.ML | Orthogonal matching pursuit (OMP) and orthogonal least squares (OLS) are
widely used for sparse signal reconstruction in under-determined linear
regression problems. The performance of these compressed sensing (CS)
algorithms depends crucially on the \textit{a priori} knowledge of either the
sparsity of the signal ($k_... | computer science |
22,823 | Variational Recursive Dual Filtering | stat.ML | State space models provide an interpretable framework for complex time series
by combining an intuitive dynamical system model with a probabilistic
observation model. We developed a flexible online learning framework for latent
nonlinear state dynamics and filtered latent states. Our method utilizes the
stochastic grad... | computer science |
22,824 | A generalized multivariate Student-t mixture model for Bayesian
classification and clustering of radar waveforms | stat.ML | In this paper, a generalized multivariate Student-t mixture model is
developed for classification and clustering of Low Probability of Intercept
radar waveforms. A Low Probability of Intercept radar signal is characterized
by a pulse compression waveform which is either frequency-modulated or
phase-modulated. The propo... | computer science |
22,825 | Consistent Nonparametric Different-Feature Selection via the Sparsest
$k$-Subgraph Problem | stat.ML | Two-sample feature selection is the problem of finding features that describe
a difference between two probability distributions, which is a ubiquitous
problem in both scientific and engineering studies. However, existing methods
have limited applicability because of their restrictive assumptions on data
distributoins ... | computer science |
22,826 | Transfer Learning with Label Noise | stat.ML | Transfer learning aims to improve learning in the target domain with limited
training data by borrowing knowledge from a related but different source domain
with sufficient labeled data. To reduce the distribution shift between source
and target domains, recent methods have focused on exploring invariant
representation... | computer science |
22,827 | Anomaly Detection by Robust Statistics | stat.ML | Real data often contain anomalous cases, also known as outliers. These may
spoil the resulting analysis but they may also contain valuable information. In
either case, the ability to detect such anomalies is essential. A useful tool
for this purpose is robust statistics, which aims to detect the outliers by
first fitti... | computer science |
22,828 | Application of Support Vector Machine Modeling and Graph Theory Metrics
for Disease Classification | stat.ML | Disease classification is a crucial element of biomedical research. Recent
studies have demonstrated that machine learning techniques, such as Support
Vector Machine (SVM) modeling, produce similar or improved predictive
capabilities in comparison to the traditional method of Logistic Regression. In
addition, it has be... | computer science |
22,829 | On Tensor Train Rank Minimization: Statistical Efficiency and Scalable
Algorithm | stat.ML | Tensor train (TT) decomposition provides a space-efficient representation for
higher-order tensors. Despite its advantage, we face two crucial limitations
when we apply the TT decomposition to machine learning problems: the lack of
statistical theory and of scalable algorithms. In this paper, we address the
limitations... | computer science |
22,830 | Application of machine learning for hematological diagnosis | stat.ML | Quick and accurate medical diagnosis is crucial for the successful treatment
of a disease. Using machine learning algorithms, we have built two models to
predict a hematologic disease, based on laboratory blood test results. In one
predictive model, we used all available blood test parameters and in the other
a reduced... | computer science |
22,831 | Detecting early signs of depressive and manic episodes in patients with
bipolar disorder using the signature-based model | stat.ML | Recurrent major mood episodes and subsyndromal mood instability cause
substantial disability in patients with bipolar disorder. Early identification
of mood episodes enabling timely mood stabilisation is an important clinical
goal. Recent technological advances allow the prospective reporting of mood in
real time enabl... | computer science |
22,832 | Learning Model Reparametrizations: Implicit Variational Inference by
Fitting MCMC distributions | stat.ML | We introduce a new algorithm for approximate inference that combines
reparametrization, Markov chain Monte Carlo and variational methods. We
construct a very flexible implicit variational distribution synthesized by an
arbitrary Markov chain Monte Carlo operation and a deterministic transformation
that can be optimized... | computer science |
22,833 | Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing | 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 Maximum
Variance Total V... | computer science |
22,834 | KNN Ensembles for Tweedie Regression: The Power of Multiscale
Neighborhoods | stat.ML | Very few K-nearest-neighbor (KNN) ensembles exist, despite the efficacy of
this approach in regression, classification, and outlier detection. Those that
do exist focus on bagging features, rather than varying k or bagging
observations; it is unknown whether varying k or bagging observations can
improve prediction. Giv... | computer science |
22,835 | Multiresolution Kernel Approximation for Gaussian Process Regression | stat.ML | Gaussian process regression generally does not scale to beyond a few
thousands data points without applying some sort of kernel approximation
method. Most approximations focus on the high eigenvalue part of the spectrum
of the kernel matrix, $K$, which leads to bad performance when the length scale
of the kernel is sma... | computer science |
22,836 | Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix
Factorization Framework via Facet Enumeration and Convex Optimization | stat.ML | Consider a structured matrix factorization model where one factor is
restricted to have its columns lying in the unit simplex. This
simplex-structured matrix factorization (SSMF) model and the associated
factorization techniques have spurred much interest in research topics over
different areas, such as hyperspectral u... | computer science |
22,837 | Demixing Structured Superposition Signals from Periodic and Aperiodic
Nonlinear Observations | stat.ML | We consider the demixing problem of two (or more) structured high-dimensional
vectors from a limited number of nonlinear observations where this nonlinearity
is due to either a periodic or an aperiodic function. We study certain families
of structured superposition models, and propose a method which provably
recovers t... | computer science |
22,838 | Mahalanonbis Distance Informed by Clustering | stat.ML | A fundamental question in data analysis, machine learning and signal
processing is how to compare between data points. The choice of the distance
metric is specifically challenging for high-dimensional data sets, where the
problem of meaningfulness is more prominent (e.g. the Euclidean distance
between images). In this... | computer science |
22,839 | Towards life cycle identification of malaria parasites using machine
learning and Riemannian geometry | stat.ML | Malaria is a serious infectious disease that is responsible for over half
million deaths yearly worldwide. The major cause of these mortalities is late
or inaccurate diagnosis. Manual microscopy is currently considered as the
dominant diagnostic method for malaria. However, it is time consuming and prone
to human error... | computer science |
22,840 | Auxiliary Variables for Multi-Dirichlet Priors | stat.ML | Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper... | computer science |
22,841 | Comprehensive Feature-Based Landscape Analysis of Continuous and
Constrained Optimization Problems Using the R-Package flacco | stat.ML | Choosing the best-performing optimizer(s) out of a portfolio of optimization
algorithms is usually a difficult and complex task. It gets even worse, if the
underlying functions are unknown, i.e., so-called Black-Box problems, and
function evaluations are considered to be expensive. In the case of continuous
single-obje... | computer science |
22,842 | A debiased distributed estimation for sparse partially linear models in
diverging dimensions | stat.ML | We consider a distributed estimation of the double-penalized least squares
approach for high dimensional partial linear models, where the sample with a
total of $N$ data points is randomly distributed among $m$ machines and the
parameters of interest are calculated by merging their $m$ individual
estimators. This paper... | computer science |
22,843 | Hierarchical Multinomial-Dirichlet model for the estimation of
conditional probability tables | stat.ML | We present a novel approach for estimating conditional probability tables,
based on a joint, rather than independent, estimate of the conditional
distributions belonging to the same table. We derive exact analytical
expressions for the estimators and we analyse their properties both
analytically and via simulation. We ... | computer science |
22,844 | An Ensemble Classifier for Predicting the Onset of Type II Diabetes | stat.ML | Prediction of disease onset from patient survey and lifestyle data is quickly
becoming an important tool for diagnosing a disease before it progresses. In
this study, data from the National Health and Nutrition Examination Survey
(NHANES) questionnaire is used to predict the onset of type II diabetes. An
ensemble model... | computer science |
22,845 | Logistic Regression as Soft Perceptron Learning | stat.ML | We comment on the fact that gradient ascent for logistic regression has a
connection with the perceptron learning algorithm. Logistic learning is the
"soft" variant of perceptron learning. | computer science |
22,846 | Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions | stat.ML | Graphical Lasso (GL) is a popular method for learning the structure of an
undirected graphical model, which is based on an $l_1$ regularization
technique. The first goal of this work is to study the behavior of the optimal
solution of GL as a function of its regularization coefficient. We show that if
the number of sam... | computer science |
22,847 | Sparse Regularization in Marketing and Economics | stat.ML | Sparse alpha-norm regularization has many data-rich applications in Marketing
and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps
to a sparse solution. This feature is attractive for ultra high-dimensional
problems that occur in demand estimation and forecasting. The alpha-norm
objective is ... | computer science |
22,848 | Statistical Inference for Machine Learning Inverse Probability Weighting
with Survival Outcomes | stat.ML | We present an inverse probability weighted estimator for survival analysis
under informative right censoring. Our estimator has the novel property that it
converges to a normal variable at $n^{1/2}$ rate for a large class of censoring
probability estimators, including many data-adaptive (e.g., machine learning)
predict... | computer science |
22,849 | Estimation of interventional effects of features on prediction | stat.ML | The interpretability of prediction mechanisms with respect to the underlying
prediction problem is often unclear. While several studies have focused on
developing prediction models with meaningful parameters, the causal
relationships between the predictors and the actual prediction have not been
considered. Here, we co... | computer science |
22,850 | Extending the small-ball method | stat.ML | The small-ball method was introduced as a way of obtaining a high
probability, isomorphic lower bound on the quadratic empirical process, under
weak assumptions on the indexing class. The key assumption was that class
members satisfy a uniform small-ball estimate, that is, $Pr(|f| \geq
\kappa\|f\|_{L_2}) \geq \delta$ f... | computer science |
22,851 | Continuous-Time Flows for Efficient Inference and Density Estimation | stat.ML | Two fundamental problems in unsupervised learning are efficient inference for
latent-variable models and robust density estimation based on large amounts of
unlabeled data. Algorithms for the two tasks, such as normalizing flows and
generative adversarial networks (GANs), are often developed independently. In
this pape... | computer science |
22,852 | A Convergence Analysis for A Class of Practical Variance-Reduction
Stochastic Gradient MCMC | stat.ML | Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as
a flexible family of scalable Bayesian sampling algorithms. However, there has
been little theoretical analysis of the impact of minibatch size to the
algorithm's convergence rate. In this paper, we prove that under a limited
computational bud... | computer science |
22,853 | Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data | stat.ML | Classifying samples into categories becomes intractable when a single sample
can have millions to billions of features, such as in genetics or imaging data.
Principal Components Analysis (PCA) is widely used to identify a
low-dimensional representation of such features for further analysis. However,
PCA ignores class l... | computer science |
22,854 | Deep and Confident Prediction for Time Series at Uber | stat.ML | Reliable uncertainty estimation for time series prediction is critical in
many fields, including physics, biology, and manufacturing. At Uber,
probabilistic time series forecasting is used for robust prediction of number
of trips during special events, driver incentive allocation, as well as
real-time anomaly detection... | computer science |
22,855 | Entropic Determinants | stat.ML | The ability of many powerful machine learning algorithms to deal with large
data sets without compromise is often hampered by computationally expensive
linear algebra tasks, of which calculating the log determinant is a canonical
example. In this paper we demonstrate the optimality of Maximum Entropy methods
in approxi... | computer science |
22,856 | Roll-back Hamiltonian Monte Carlo | stat.ML | We propose a new framework for Hamiltonian Monte Carlo (HMC) on truncated
probability distributions with smooth underlying density functions. Traditional
HMC requires computing the gradient of potential function associated with the
target distribution, and therefore does not perform its full power on truncated
distribu... | computer science |
22,857 | Discovering Potential Correlations via Hypercontractivity | stat.ML | Discovering a correlation from one variable to another variable is of
fundamental scientific and practical interest. While existing correlation
measures are suitable for discovering average correlation, they fail to
discover hidden or potential correlations. To bridge this gap, (i) we postulate
a set of natural axioms ... | computer science |
22,858 | Weighted Orthogonal Components Regression Analysis | stat.ML | In the multiple linear regression setting, we propose a general framework,
termed weighted orthogonal components regression (WOCR), which encompasses many
known methods as special cases, including ridge regression and principal
components regression. WOCR makes use of the monotonicity inherent in
orthogonal components ... | computer science |
22,859 | Random Forests of Interaction Trees for Estimating Individualized
Treatment Effects in Randomized Trials | stat.ML | Assessing heterogeneous treatment effects has become a growing interest in
advancing precision medicine. Individualized treatment effects (ITE) play a
critical role in such an endeavor. Concerning experimental data collected from
randomized trials, we put forward a method, termed random forests of
interaction trees (RF... | computer science |
22,860 | Dependence Modeling in Ultra High Dimensions with Vine Copulas and the
Graphical Lasso | stat.ML | To model high dimensional data, Gaussian methods are widely used since they
remain tractable and yield parsimonious models by imposing strong assumptions
on the data. Vine copulas are more flexible by combining arbitrary marginal
distributions and (conditional) bivariate copulas. Yet, this adaptability is
accompanied b... | computer science |
22,861 | Optimal Learning for Sequential Decision Making for Expensive Cost
Functions with Stochastic Binary Feedbacks | stat.ML | We consider the problem of sequentially making decisions that are rewarded by
"successes" and "failures" which can be predicted through an unknown
relationship that depends on a partially controllable vector of attributes for
each instance. The learner takes an active role in selecting samples from the
instance pool. T... | computer science |
22,862 | Mixtures and products in two graphical models | stat.ML | We compare two statistical models of three binary random variables. One is a
mixture model and the other is a product of mixtures model called a restricted
Boltzmann machine. Although the two models we study look different from their
parametrizations, we show that they represent the same set of distributions on
the int... | computer science |
22,863 | Learning Functional Causal Models with Generative Neural Networks | stat.ML | We introduce a new approach to functional causal modeling from observational
data. The approach, called Causal Generative Neural Networks (CGNN), leverages
the power of neural networks to learn a generative model of the joint
distribution of the observed variables, by minimizing the Maximum Mean
Discrepancy between gen... | computer science |
22,864 | Constrained Bayesian Optimization for Automatic Chemical Design | stat.ML | Automatic Chemical Design leverages recent advances in deep generative
modelling to provide a framework for performing continuous optimization of
molecular properties. Although the provision of a continuous representation for
prospective lead drug candidates has opened the door to hitherto inaccessible
tools of mathema... | computer science |
22,865 | The generalised random dot product graph | stat.ML | This paper introduces a latent position network model, called the generalised
random dot product graph, comprising as special cases the stochastic
blockmodel, mixed membership stochastic blockmodel, and random dot product
graph. In this model, nodes are represented as random vectors on
$\mathbb{R}^d$, and the probabili... | computer science |
22,866 | Multivariate Gaussian Network Structure Learning | stat.ML | We consider a graphical model where a multivariate normal vector is
associated with each node of the underlying graph and estimate the graphical
structure. We minimize a loss function obtained by regressing the vector at
each node on those at the remaining ones under a group penalty. We show that
the proposed estimator... | computer science |
22,867 | Bayesian nonparametric Principal Component Analysis | stat.ML | Principal component analysis (PCA) is very popular to perform dimension
reduction. The selection of the number of significant components is essential
but often based on some practical heuristics depending on the application. Only
few works have proposed a probabilistic approach able to infer the number of
significant c... | computer science |
22,868 | Learning Low-Dimensional Metrics | stat.ML | This paper investigates the theoretical foundations of metric learning,
focused on three key questions that are not fully addressed in prior work: 1)
we consider learning general low-dimensional (low-rank) metrics as well as
sparse metrics; 2) we develop upper and lower (minimax)bounds on the
generalization error; 3) w... | computer science |
22,869 | A Summary Of The Kernel Matrix, And How To Learn It Effectively Using
Semidefinite Programming | stat.ML | Kernel-based learning algorithms are widely used in machine learning for
problems that make use of the similarity between object pairs. Such algorithms
first embed all data points into an alternative space, where the inner product
between object pairs specifies their distance in the embedding space. Applying
kernel met... | computer science |
22,870 | An Expectation Conditional Maximization approach for Gaussian graphical
models | stat.ML | Bayesian graphical models are a useful tool for understanding dependence
relationships among many variables, particularly in situations with external
prior information. In high-dimensional settings, the space of possible graphs
becomes enormous, rendering even state-of-the-art Bayesian stochastic search
computationally... | computer science |
22,871 | Lazy stochastic principal component analysis | stat.ML | Stochastic principal component analysis (SPCA) has become a popular
dimensionality reduction strategy for large, high-dimensional datasets. We
derive a simplified algorithm, called Lazy SPCA, which has reduced
computational complexity and is better suited for large-scale distributed
computation. We prove that SPCA and ... | computer science |
22,872 | Weather Forecasting Error in Solar Energy Forecasting | stat.ML | As renewable distributed energy resources (DERs) penetrate the power grid at
an accelerating speed, it is essential for operators to have accurate solar
photovoltaic (PV) energy forecasting for efficient operations and planning.
Generally, observed weather data are applied in the solar PV generation
forecasting model w... | computer science |
22,873 | On the Model Shrinkage Effect of Gamma Process Edge Partition Models | stat.ML | The edge partition model (EPM) is a fundamental Bayesian nonparametric model
for extracting an overlapping structure from binary matrix. The EPM adopts a
gamma process ($\Gamma$P) prior to automatically shrink the number of active
atoms. However, we empirically found that the model shrinkage of the EPM does
not typical... | computer science |
22,874 | Telling Cause from Effect using MDL-based Local and Global Regression | stat.ML | We consider the fundamental problem of inferring the causal direction between
two univariate numeric random variables $X$ and $Y$ from observational data.
The two-variable case is especially difficult to solve since it is not possible
to use standard conditional independence tests between the variables.
To tackle thi... | computer science |
22,875 | Adaptive Nonparametric Clustering | stat.ML | This paper presents a new approach to non-parametric cluster analysis called
Adaptive Weights Clustering (AWC). The idea is to identify the clustering
structure by checking at different points and for different scales on departure
from local homogeneity. The proposed procedure describes the clustering
structure in term... | computer science |
22,876 | Symbolic Analysis-based Reduced Order Markov Modeling of Time Series
Data | stat.ML | This paper presents a technique for reduced-order Markov modeling for compact
representation of time-series data. In this work, symbolic dynamics-based tools
have been used to infer an approximate generative Markov model. The time-series
data are first symbolized by partitioning the continuous measurement space of
the ... | computer science |
22,877 | Multi-way Interacting Regression via Factorization Machines | stat.ML | We propose a Bayesian regression method that accounts for multi-way
interactions of arbitrary orders among the predictor variables. Our model makes
use of a factorization mechanism for representing the regression coefficients
of interactions among the predictors, while the interaction selection is guided
by a prior dis... | computer science |
22,878 | Bayesian Multi Plate High Throughput Screening of Compounds | stat.ML | High throughput screening of compounds (chemicals) is an essential part of
drug discovery [7], involving thousands to millions of compounds, with the
purpose of identifying candidate hits. Most statistical tools, including the
industry standard B-score method, work on individual compound plates and do not
exploit cross... | computer science |
22,879 | Reconstruction from Periodic Nonlinearities, With Applications to HDR
Imaging | stat.ML | We consider the problem of reconstructing signals and images from periodic
nonlinearities. For such problems, we design a measurement scheme that supports
efficient reconstruction; moreover, our method can be adapted to extend to
compressive sensing-based signal and image acquisition systems. Our techniques
can be pote... | computer science |
22,880 | Testing for Feature Relevance: The HARVEST Algorithm | stat.ML | Feature selection with high-dimensional data and a very small proportion of
relevant features poses a severe challenge to standard statistical methods. We
have developed a new approach (HARVEST) that is straightforward to apply,
albeit somewhat computer-intensive. This algorithm can be used to pre-screen a
large number... | computer science |
22,881 | Learning Predictive Leading Indicators for Forecasting Time Series
Systems with Unknown Clusters of Forecast Tasks | stat.ML | We present a new method for forecasting systems of multiple interrelated time
series. The method learns the forecast models together with discovering leading
indicators from within the system that serve as good predictors improving the
forecast accuracy and a cluster structure of the predictive tasks around these.
The ... | computer science |
22,882 | Large-Scale Quadratically Constrained Quadratic Program via
Low-Discrepancy Sequences | stat.ML | We consider the problem of solving a large-scale Quadratically Constrained
Quadratic Program. Such problems occur naturally in many scientific and web
applications. Although there are efficient methods which tackle this problem,
they are mostly not scalable. In this paper, we develop a method that
transforms the quadra... | computer science |
22,883 | Decontamination of Mutual Contamination Models | stat.ML | Many machine learning problems can be characterized by mutual contamination
models. In these problems, one observes several random samples from different
convex combinations of a set of unknown base distributions and the goal is to
infer these base distributions. This paper considers the general setting where
the base ... | computer science |
22,884 | Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes | stat.ML | This work constructs a hypothesis test for detecting whether an
data-generating function $h: R^p \rightarrow R$ belongs to a specific
reproducing kernel Hilbert space $\mathcal{H}_0$ , where the structure of
$\mathcal{H}_0$ is only partially known. Utilizing the theory of reproducing
kernels, we reduce this hypothesis ... | computer science |
22,885 | Learning Registered Point Processes from Idiosyncratic Observations | stat.ML | A parametric point process model is developed, with modeling based on the
assumption that sequential observations often share latent phenomena, while
also possessing idiosyncratic effects. An alternating optimization method is
proposed to learn a "registered" point process that accounts for shared
structure, as well as... | computer science |
22,886 | Strengths and Weaknesses of Deep Learning Models for Face Recognition
Against Image Degradations | stat.ML | Deep convolutional neural networks (CNNs) based approaches are the
state-of-the-art in various computer vision tasks, including face recognition.
Considerable research effort is currently being directed towards further
improving deep CNNs by focusing on more powerful model architectures and better
learning techniques. ... | computer science |
22,887 | Differentially Private Database Release via Kernel Mean Embeddings | stat.ML | We lay theoretical foundations for new database release mechanisms that allow
third-parties to construct consistent estimators of population statistics,
while ensuring that the privacy of each individual contributing to the database
is protected. The proposed framework rests on two main ideas. First, releasing
(an esti... | computer science |
22,888 | Smooth Pinball Neural Network for Probabilistic Forecasting of Wind
Power | stat.ML | Uncertainty analysis in the form of probabilistic forecasting can
significantly improve decision making processes in the smart power grid for
better integrating renewable energy sources such as wind. Whereas point
forecasting provides a single expected value, probabilistic forecasts provide
more information in the form... | computer science |
22,889 | Multitask Learning using Task Clustering with Applications to Predictive
Modeling and GWAS of Plant Varieties | stat.ML | Inferring predictive maps between multiple input and multiple output
variables or tasks has innumerable applications in data science. Multi-task
learning attempts to learn the maps to several output tasks simultaneously with
information sharing between them. We propose a novel multi-task learning
framework for sparse l... | computer science |
22,890 | Forecasting Player Behavioral Data and Simulating in-Game Events | stat.ML | Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation opti... | computer science |
22,891 | Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model | stat.ML | The emergence of mobile games has caused a paradigm shift in the video-game
industry. Game developers now have at their disposal a plethora of information
on their players, and thus can take advantage of reliable models that can
accurately predict player behavior and scale to huge datasets. Churn
prediction, a challeng... | computer science |
22,892 | Churn Prediction in Mobile Social Games: Towards a Complete Assessment
Using Survival Ensembles | stat.ML | Reducing user attrition, i.e. churn, is a broad challenge faced by several
industries. In mobile social games, decreasing churn is decisive to increase
player retention and rise revenues. Churn prediction models allow to understand
player loyalty and to anticipate when they will stop playing a game. Thanks to
these pre... | computer science |
22,893 | Conic Scan-and-Cover algorithms for nonparametric topic modeling | stat.ML | We propose new algorithms for topic modeling when the number of topics is
unknown. Our approach relies on an analysis of the concentration of mass and
angular geometry of the topic simplex, a convex polytope constructed by taking
the convex hull of vertices representing the latent topics. Our algorithms are
shown in pr... | computer science |
22,894 | Lagged Exact Bayesian Online Changepoint Detection | stat.ML | Identifying changes in the generative process of sequential data, known as
changepoint detection, has become an increasingly important topic for a wide
variety of fields. A recently developed approach, which we call EXact Online
Bayesian Changepoint Detection (EXO), has shown reasonable results with
efficient computati... | computer science |
22,895 | Multilevel Modeling with Structured Penalties for Classification from
Imaging Genetics data | stat.ML | In this paper, we propose a framework for automatic classification of
patients from multimodal genetic and brain imaging data by optimally combining
them. Additive models with unadapted penalties (such as the classical group
lasso penalty or $L_1$-multiple kernel learning) treat all modalities in the
same manner and ca... | computer science |
22,896 | Learning Independent Features with Adversarial Nets for Non-linear ICA | stat.ML | Reliable measures of statistical dependence could be useful tools for
learning independent features and performing tasks like source separation using
Independent Component Analysis (ICA). Unfortunately, many of such measures,
like the mutual information, are hard to estimate and optimize directly. We
propose to learn i... | computer science |
22,897 | Unsupervised Real-Time Control through Variational Empowerment | stat.ML | We introduce a methodology for efficiently computing a lower bound to
empowerment, allowing it to be used as an unsupervised cost function for policy
learning in real-time control. Empowerment, being the channel capacity between
actions and states, maximises the influence of an agent on its near future. It
has been sho... | computer science |
22,898 | Benefits from Superposed Hawkes Processes | stat.ML | The superposition of temporal point processes has been studied for many
years, although the usefulness of such models for practical applications has
not be fully developed. We investigate superposed Hawkes process as an
important class of such models, with properties studied in the framework of
least squares estimation... | computer science |
22,899 | An Improved Modified Cholesky Decomposition Method for Inverse
Covariance Matrix Estimation | stat.ML | The modified Cholesky decomposition is commonly used for inverse covariance
matrix estimation given a specified order of random variables. However, the
order of variables is often not available or cannot be pre-determined. Hence,
we propose a novel estimator to address the variable order issue in the
modified Cholesky ... | computer science |
22,900 | Simultaneous Matrix Diagonalization for Structural Brain Networks
Classification | stat.ML | This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous app... | computer science |
22,901 | Fully adaptive algorithm for pure exploration in linear bandits | stat.ML | We propose the first fully-adaptive algorithm for pure exploration in linear
bandits---the task to find the arm with the largest expected reward, which
depends on an unknown parameter linearly. While existing methods partially or
entirely fix sequences of arm selections before observing rewards, our method
adaptively c... | computer science |
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