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22,502 | Removal of Batch Effects using Distribution-Matching Residual Networks | stat.ML | Sources of variability in experimentally derived data include measurement
error in addition to the physical phenomena of interest. This measurement error
is a combination of systematic components, originating from the measuring
instrument, and random measurement errors. Several novel biological
technologies, such as ma... | computer science |
22,503 | Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model | stat.ML | In this paper, we consider clustering data that is assumed to come from one
of finitely many pointed convex polyhedral cones. This model is referred to as
the Union of Polyhedral Cones (UOPC) model. Similar to the Union of Subspaces
(UOS) model where each data from each subspace is generated from a (unknown)
basis, in ... | computer science |
22,504 | Communication-efficient Distributed Sparse Linear Discriminant Analysis | stat.ML | We propose a communication-efficient distributed estimation method for sparse
linear discriminant analysis (LDA) in the high dimensional regime. Our method
distributes the data of size $N$ into $m$ machines, and estimates a local
sparse LDA estimator on each machine using the data subset of size $N/m$. After
the distri... | computer science |
22,505 | Estimation of low rank density matrices by Pauli measurements | stat.ML | Density matrices are positively semi-definite Hermitian matrices with unit
trace that describe the states of quantum systems. Many quantum systems of
physical interest can be represented as high-dimensional low rank density
matrices. A popular problem in {\it quantum state tomography} (QST) is to
estimate the unknown l... | computer science |
22,506 | Spatio-temporal Gaussian processes modeling of dynamical systems in
systems biology | stat.ML | Quantitative modeling of post-transcriptional regulation process is a
challenging problem in systems biology. A mechanical model of the regulatory
process needs to be able to describe the available spatio-temporal protein
concentration and mRNA expression data and recover the continuous
spatio-temporal fields. Rigorous... | computer science |
22,507 | Black-box Importance Sampling | stat.ML | Importance sampling is widely used in machine learning and statistics, but
its power is limited by the restriction of using simple proposals for which the
importance weights can be tractably calculated. We address this problem by
studying black-box importance sampling methods that calculate importance
weights for sampl... | computer science |
22,508 | A Unified Computational and Statistical Framework for Nonconvex Low-Rank
Matrix Estimation | stat.ML | We propose a unified framework for estimating low-rank matrices through
nonconvex optimization based on gradient descent algorithm. Our framework is
quite general and can be applied to both noisy and noiseless observations. In
the general case with noisy observations, we show that our algorithm is
guaranteed to linearl... | computer science |
22,509 | AutoGP: Exploring the Capabilities and Limitations of Gaussian Process
Models | stat.ML | We investigate the capabilities and limitations of Gaussian process models by
jointly exploring three complementary directions: (i) scalable and
statistically efficient inference; (ii) flexible kernels; and (iii) objective
functions for hyperparameter learning alternative to the marginal likelihood.
Our approach outper... | computer science |
22,510 | Consistent Kernel Mean Estimation for Functions of Random Variables | stat.ML | We provide a theoretical foundation for non-parametric estimation of
functions of random variables using kernel mean embeddings. We show that for
any continuous function $f$, consistent estimators of the mean embedding of a
random variable $X$ lead to consistent estimators of the mean embedding of
$f(X)$. For Mat\'ern ... | computer science |
22,511 | Clustering by connection center evolution | stat.ML | The determination of cluster centers generally depends on the scale that we
use to analyze the data to be clustered. Inappropriate scale usually leads to
unreasonable cluster centers and thus unreasonable results. In this study, we
first consider the similarity of elements in the data as the connectivity of
nodes in an... | computer science |
22,512 | Robust and Parallel Bayesian Model Selection | stat.ML | Effective and accurate model selection is an important problem in modern data
analysis. One of the major challenges is the computational burden required to
handle large data sets that cannot be stored or processed on one machine.
Another challenge one may encounter is the presence of outliers and
contaminations that da... | computer science |
22,513 | Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI
Analysis | stat.ML | Independent component analysis (ICA) is a powerful method for blind source
separation based on the assumption that sources are statistically independent.
Though ICA has proven useful and has been employed in many applications,
complete statistical independence can be too restrictive an assumption in
practice. Additiona... | computer science |
22,514 | Revisiting Classifier Two-Sample Tests | stat.ML | The goal of two-sample tests is to assess whether two samples, $S_P \sim P^n$
and $S_Q \sim Q^m$, are drawn from the same distribution. Perhaps intriguingly,
one relatively unexplored method to build two-sample tests is the use of binary
classifiers. In particular, construct a dataset by pairing the $n$ examples in
$S_... | computer science |
22,515 | On the Convergence of Stochastic Gradient MCMC Algorithms with
High-Order Integrators | stat.ML | Recent advances in Bayesian learning with large-scale data have witnessed
emergence of stochastic gradient MCMC algorithms (SG-MCMC), such as stochastic
gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian MCMC
(SGHMC), and the stochastic gradient thermostat. While finite-time convergence
properties of th... | computer science |
22,516 | Dictionary Learning Strategies for Compressed Fiber Sensing Using a
Probabilistic Sparse Model | stat.ML | We present a sparse estimation and dictionary learning framework for
compressed fiber sensing based on a probabilistic hierarchical sparse model. To
handle severe dictionary coherence, selective shrinkage is achieved using a
Weibull prior, which can be related to non-convex optimization with $p$-norm
constraints for $0... | computer science |
22,517 | Mean-Field Variational Inference for Gradient Matching with Gaussian
Processes | stat.ML | Gradient matching with Gaussian processes is a promising tool for learning
parameters of ordinary differential equations (ODE's). The essence of gradient
matching is to model the prior over state variables as a Gaussian process which
implies that the joint distribution given the ODE's and GP kernels is also
Gaussian di... | computer science |
22,518 | Independent Component Analysis by Entropy Maximization with Kernels | stat.ML | Independent component analysis (ICA) is the most popular method for blind
source separation (BSS) with a diverse set of applications, such as biomedical
signal processing, video and image analysis, and communications. Maximum
likelihood (ML), an optimal theoretical framework for ICA, requires knowledge
of the true unde... | computer science |
22,519 | Inertial Regularization and Selection (IRS): Sequential Regression in
High-Dimension and Sparsity | stat.ML | In this paper, we develop a new sequential regression modeling approach for
data streams. Data streams are commonly found around us, e.g in a retail
enterprise sales data is continuously collected every day. A demand forecasting
model is an important outcome from the data that needs to be continuously
updated with the ... | computer science |
22,520 | Bayesian Nonparametric Modeling of Heterogeneous Groups of Censored Data | stat.ML | Datasets containing large samples of time-to-event data arising from several
small heterogeneous groups are commonly encountered in statistics. This
presents problems as they cannot be pooled directly due to their heterogeneity
or analyzed individually because of their small sample size. Bayesian
nonparametric modellin... | computer science |
22,521 | C-mix: a high dimensional mixture model for censored durations, with
applications to genetic data | stat.ML | We introduce a mixture model for censored durations (C-mix), and develop
maximum likelihood inference for the joint estimation of the time distributions
and latent regression parameters of the model. We consider a high-dimensional
setting, with datasets containing a large number of biomedical covariates. We
therefore p... | computer science |
22,522 | Parallelizable sparse inverse formulation Gaussian processes (SpInGP) | stat.ML | We propose a parallelizable sparse inverse formulation Gaussian process
(SpInGP) for temporal models. It uses a sparse precision GP formulation and
sparse matrix routines to speed up the computations. Due to the state-space
formulation used in the algorithm, the time complexity of the basic SpInGP is
linear, and becaus... | computer science |
22,523 | Gaussian Process Kernels for Popular State-Space Time Series Models | stat.ML | In this paper we investigate a link between state- space models and Gaussian
Processes (GP) for time series modeling and forecasting. In particular, several
widely used state- space models are transformed into continuous time form and
corresponding Gaussian Process kernels are derived. Experimen- tal results
demonstrat... | computer science |
22,524 | Tensor Decompositions for Identifying Directed Graph Topologies and
Tracking Dynamic Networks | stat.ML | Directed networks are pervasive both in nature and engineered systems, often
underlying the complex behavior observed in biological systems, microblogs and
social interactions over the web, as well as global financial markets. Since
their structures are often unobservable, in order to facilitate network
analytics, one ... | computer science |
22,525 | Recurrent switching linear dynamical systems | stat.ML | Many natural systems, such as neurons firing in the brain or basketball teams
traversing a court, give rise to time series data with complex, nonlinear
dynamics. We can gain insight into these systems by decomposing the data into
segments that are each explained by simpler dynamic units. Building on
switching linear dy... | computer science |
22,526 | Poisson intensity estimation with reproducing kernels | stat.ML | Despite the fundamental nature of the inhomogeneous Poisson process in the
theory and application of stochastic processes, and its attractive
generalizations (e.g. Cox process), few tractable nonparametric modeling
approaches of intensity functions exist, especially when observed points lie in
a high-dimensional space.... | computer science |
22,527 | Statistical Inference for Model Parameters in Stochastic Gradient
Descent | stat.ML | The stochastic gradient descent (SGD) algorithm has been widely used in
statistical estimation for large-scale data due to its computational and memory
efficiency. While most existing work focuses on the convergence of the
objective function or the error of the obtained solution, we investigate the
problem of statistic... | computer science |
22,528 | GPflow: A Gaussian process library using TensorFlow | stat.ML | GPflow is a Gaussian process library that uses TensorFlow for its core
computations and Python for its front end. The distinguishing features of
GPflow are that it uses variational inference as the primary approximation
method, provides concise code through the use of automatic differentiation, has
been engineered with... | computer science |
22,529 | Sparse Signal Subspace Decomposition Based on Adaptive Over-complete
Dictionary | stat.ML | This paper proposes a subspace decomposition method based on an over-complete
dictionary in sparse representation, called "Sparse Signal Subspace
Decomposition" (or 3SD) method. This method makes use of a novel criterion
based on the occurrence frequency of atoms of the dictionary over the data set.
This criterion, wel... | computer science |
22,530 | Rapid Posterior Exploration in Bayesian Non-negative Matrix
Factorization | stat.ML | Non-negative Matrix Factorization (NMF) is a popular tool for data
exploration. Bayesian NMF promises to also characterize uncertainty in the
factorization. Unfortunately, current inference approaches such as MCMC mix
slowly and tend to get stuck on single modes. We introduce a novel approach
using rapidly-exploring ra... | computer science |
22,531 | Geometric Dirichlet Means algorithm for topic inference | stat.ML | We propose a geometric algorithm for topic learning and inference that is
built on the convex geometry of topics arising from the Latent Dirichlet
Allocation (LDA) model and its nonparametric extensions. To this end we study
the optimization of a geometric loss function, which is a surrogate to the
LDA's likelihood. Ou... | computer science |
22,532 | A general multiblock method for structured variable selection | stat.ML | Regularised canonical correlation analysis was recently extended to more than
two sets of variables by the multiblock method Regularised generalised
canonical correlation analysis (RGCCA). Further, Sparse GCCA (SGCCA) was
proposed to address the issue of variable selection. However, for technical
reasons, the variable ... | computer science |
22,533 | Super-resolution estimation of cyclic arrival rates | stat.ML | Exploiting the fact that most arrival processes exhibit cyclic behaviour, we
propose a simple procedure for estimating the intensity of a nonhomogeneous
Poisson process. The estimator is the super-resolution analogue to Shao 2010
and Shao & Lii 2011, which is a sum of $p$ sinusoids where $p$ and the
frequency, amplitud... | computer science |
22,534 | Exploring and measuring non-linear correlations: Copulas, Lightspeed
Transportation and Clustering | stat.ML | We propose a methodology to explore and measure the pairwise correlations
that exist between variables in a dataset. The methodology leverages copulas
for encoding dependence between two variables, state-of-the-art optimal
transport for providing a relevant geometry to the copulas, and clustering for
summarizing the ma... | computer science |
22,535 | Analysis of Nonstationary Time Series Using Locally Coupled Gaussian
Processes | stat.ML | The analysis of nonstationary time series is of great importance in many
scientific fields such as physics and neuroscience. In recent years, Gaussian
process regression has attracted substantial attention as a robust and powerful
method for analyzing time series. In this paper, we introduce a new framework
for analyzi... | computer science |
22,536 | Function Driven Diffusion for Personalized Counterfactual Inference | stat.ML | We consider the problem of constructing diffusion operators high dimensional
data $X$ to address counterfactual functions $F$, such as individualized
treatment effectiveness. We propose and construct a new diffusion metric $K_F$
that captures both the local geometry of $X$ and the directions of variance of
$F$. The res... | computer science |
22,537 | Causal Compression | stat.ML | We propose a new method of discovering causal relationships in temporal data
based on the notion of causal compression. To this end, we adopt the Pearlian
graph setting and the directed information as an information theoretic tool for
quantifying causality. We introduce chain rule for directed information and use
it to... | computer science |
22,538 | Sensitivity Maps of the Hilbert-Schmidt Independence Criterion | stat.ML | Kernel dependence measures yield accurate estimates of nonlinear relations
between random variables, and they are also endorsed with solid theoretical
properties and convergence rates. Besides, the empirical estimates are easy to
compute in closed form just involving linear algebra operations. However, they
are hampere... | computer science |
22,539 | Learning Methods for Dynamic Topic Modeling in Automated Behaviour
Analysis | stat.ML | Semi-supervised and unsupervised systems provide operators with invaluable
support and can tremendously reduce the operators load. In the light of the
necessity to process large volumes of video data and provide autonomous
decisions, this work proposes new learning algorithms for activity analysis in
video. The activit... | computer science |
22,540 | Cross-validation based Nonlinear Shrinkage | stat.ML | Many machine learning algorithms require precise estimates of covariance
matrices. The sample covariance matrix performs poorly in high-dimensional
settings, which has stimulated the development of alternative methods, the
majority based on factor models and shrinkage. Recent work of Ledoit and Wolf
has extended the sh... | computer science |
22,541 | Gaussian Processes for Survival Analysis | stat.ML | We introduce a semi-parametric Bayesian model for survival analysis. The
model is centred on a parametric baseline hazard, and uses a Gaussian process
to model variations away from it nonparametrically, as well as dependence on
covariates. As opposed to many other methods in survival analysis, our
framework does not im... | computer science |
22,542 | Tensor Decomposition via Variational Auto-Encoder | stat.ML | Tensor decomposition is an important technique for capturing the high-order
interactions among multiway data. Multi-linear tensor composition methods, such
as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the
complex interactions among objects are multi-linear, and are thus insufficient
to repres... | computer science |
22,543 | Spectral community detection in heterogeneous large networks | stat.ML | In this article, we study spectral methods for community detection based on $
\alpha$-parametrized normalized modularity matrix hereafter called $ {\bf
L}_\alpha $ in heterogeneous graph models. We show, in a regime where community
detection is not asymptotically trivial, that $ {\bf L}_\alpha $ can be well
approximate... | computer science |
22,544 | Optimal rates for the regularized learning algorithms under general
source condition | stat.ML | We consider the learning algorithms under general source condition with the
polynomial decay of the eigenvalues of the integral operator in vector-valued
function setting. We discuss the upper convergence rates of Tikhonov
regularizer under general source condition corresponding to increasing monotone
index function. T... | computer science |
22,545 | A Bayesian optimization approach to find Nash equilibria | stat.ML | Game theory finds nowadays a broad range of applications in engineering and
machine learning. However, in a derivative-free, expensive black-box context,
very few algorithmic solutions are available to find game equilibria. Here, we
propose a novel Gaussian-process based approach for solving games in this
context. We f... | computer science |
22,546 | Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning | stat.ML | The vision for precision medicine is to use individual patient
characteristics to inform a personalized treatment plan that leads to the best
healthcare possible for each patient. Mobile technologies have an important
role to play in this vision as they offer a means to monitor a patient's health
status in real-time an... | computer science |
22,547 | Kernel regression, minimax rates and effective dimensionality: beyond
the regular case | stat.ML | We investigate if kernel regularization methods can achieve minimax
convergence rates over a source condition regularity assumption for the target
function. These questions have been considered in past literature, but only
under specific assumptions about the decay, typically polynomial, of the
spectrum of the the kern... | computer science |
22,548 | Error Metrics for Learning Reliable Manifolds from Streaming Data | stat.ML | Spectral dimensionality reduction is frequently used to identify
low-dimensional structure in high-dimensional data. However, learning
manifolds, especially from the streaming data, is computationally and memory
expensive. In this paper, we argue that a stable manifold can be learned using
only a fraction of the stream... | computer science |
22,549 | Joint mean and covariance estimation with unreplicated matrix-variate
data | stat.ML | It has been proposed that complex populations, such as those that arise in
genomics studies, may exhibit dependencies among observations as well as among
variables. This gives rise to the challenging problem of analyzing unreplicated
high-dimensional data with unknown mean and dependence structures.
Matrix-variate appr... | computer science |
22,550 | Improved Particle Filters for Vehicle Localisation | stat.ML | The ability to track a moving vehicle is of crucial importance in numerous
applications. The task has often been approached by the importance sampling
technique of particle filters due to its ability to model non-linear and
non-Gaussian dynamics, of which a vehicle travelling on a road network is a
good example. Partic... | computer science |
22,551 | ROS Regression: Integrating Regularization and Optimal Scaling
Regression | stat.ML | In this paper we combine two important extensions of ordinary least squares
regression: regularization and optimal scaling. Optimal scaling (sometimes also
called optimal scoring) has originally been developed for categorical data, and
the process finds quantifications for the categories that are optimal for the
regres... | computer science |
22,552 | Finding Alternate Features in Lasso | stat.ML | We propose a method for finding alternate features missing in the Lasso
optimal solution. In ordinary Lasso problem, one global optimum is obtained and
the resulting features are interpreted as task-relevant features. However, this
can overlook possibly relevant features not selected by the Lasso. With the
proposed met... | computer science |
22,553 | Variational Fourier features for Gaussian processes | stat.ML | This work brings together two powerful concepts in Gaussian processes: the
variational approach to sparse approximation and the spectral representation of
Gaussian processes. This gives rise to an approximation that inherits the
benefits of the variational approach but with the representational power and
computational ... | computer science |
22,554 | MDL-motivated compression of GLM ensembles increases interpretability
and retains predictive power | stat.ML | Over the years, ensemble methods have become a staple of machine learning.
Similarly, generalized linear models (GLMs) have become very popular for a wide
variety of statistical inference tasks. The former have been shown to enhance
out- of-sample predictive power and the latter possess easy interpretability.
Recently,... | computer science |
22,555 | Time Series Structure Discovery via Probabilistic Program Synthesis | stat.ML | There is a widespread need for techniques that can discover structure from
time series data. Recently introduced techniques such as Automatic Bayesian
Covariance Discovery (ABCD) provide a way to find structure within a single
time series by searching through a space of covariance kernels that is
generated using a simp... | computer science |
22,556 | Optimal Learning for Stochastic Optimization with Nonlinear Parametric
Belief Models | stat.ML | We consider the problem of estimating the expected value of information (the
knowledge gradient) for Bayesian learning problems where the belief model is
nonlinear in the parameters. Our goal is to maximize some metric, while
simultaneously learning the unknown parameters of the nonlinear belief model,
by guiding a seq... | computer science |
22,557 | Poisson Random Fields for Dynamic Feature Models | stat.ML | We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic
model for time-dependent data assumed to have been generated by an unknown
number of latent features. This model is suitable as a prior in Bayesian
nonparametric feature allocation models in which the features underlying the
observed data exhi... | computer science |
22,558 | Learning Cost-Effective and Interpretable Regimes for Treatment
Recommendation | stat.ML | Decision makers, such as doctors and judges, make crucial decisions such as
recommending treatments to patients, and granting bails to defendants on a
daily basis. Such decisions typically involve weighting the potential benefits
of taking an action against the costs involved. In this work, we aim to
automate this task... | computer science |
22,559 | Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for
Complex Systems | stat.ML | This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine
Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016 | computer science |
22,560 | Probabilistic map-matching using particle filters | stat.ML | Increasing availability of vehicle GPS data has created potentially
transformative opportunities for traffic management, route planning and other
location-based services. Critical to the utility of the data is their accuracy.
Map-matching is the process of improving the accuracy by aligning GPS data with
the road netwo... | computer science |
22,561 | Complex-valued Gaussian Process Regression for Time Series Analysis | stat.ML | The construction of synthetic complex-valued signals from real-valued
observations is an important step in many time series analysis techniques. The
most widely used approach is based on the Hilbert transform, which maps the
real-valued signal into its quadrature component. In this paper, we define a
probabilistic gene... | computer science |
22,562 | Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor
Regression | stat.ML | In this paper, we consider the problem of learning high-dimensional tensor
regression problems with low-rank structure. One of the core challenges
associated with learning high-dimensional models is computation since the
underlying optimization problems are often non-convex. While convex relaxations
could lead to polyn... | computer science |
22,563 | Towards multiple kernel principal component analysis for integrative
analysis of tumor samples | stat.ML | Personalized treatment of patients based on tissue-specific cancer subtypes
has strongly increased the efficacy of the chosen therapies. Even though the
amount of data measured for cancer patients has increased over the last years,
most cancer subtypes are still diagnosed based on individual data sources (e.g.
gene exp... | computer science |
22,564 | Stochastic Variance-reduced Gradient Descent for Low-rank Matrix
Recovery from Linear Measurements | stat.ML | We study the problem of estimating low-rank matrices from linear measurements
(a.k.a., matrix sensing) through nonconvex optimization. We propose an
efficient stochastic variance reduced gradient descent algorithm to solve a
nonconvex optimization problem of matrix sensing. Our algorithm is applicable
to both noisy and... | computer science |
22,565 | Optimal Low-Rank Dynamic Mode Decomposition | stat.ML | Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing
the dynamics of non-linear systems from experimental datasets. Recently,
several attempts have extended DMD to the context of low-rank approximations.
This extension is of particular interest for reduced-order modeling in various
applicative ... | computer science |
22,566 | NIPS 2016 Workshop on Representation Learning in Artificial and
Biological Neural Networks (MLINI 2016) | stat.ML | This workshop explores the interface between cognitive neuroscience and
recent advances in AI fields that aim to reproduce human performance such as
natural language processing and computer vision, and specifically deep learning
approaches to such problems.
When studying the cognitive capabilities of the brain, scien... | computer science |
22,567 | Learning Sparse Structural Changes in High-dimensional Markov Networks:
A Review on Methodologies and Theories | stat.ML | Recent years have seen an increasing popularity of learning the sparse
\emph{changes} in Markov Networks. Changes in the structure of Markov Networks
reflect alternations of interactions between random variables under different
regimes and provide insights into the underlying system. While each individual
network struc... | computer science |
22,568 | Optimal statistical decision for Gaussian graphical model selection | stat.ML | Gaussian graphical model is a graphical representation of the dependence
structure for a Gaussian random vector. It is recognized as a powerful tool in
different applied fields such as bioinformatics, error-control codes, speech
language, information retrieval and others. Gaussian graphical model selection
is a statist... | computer science |
22,569 | A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank
Matrix Recovery | stat.ML | We propose a generic framework based on a new stochastic variance-reduced
gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
Starting from an appropriate initial estimator, our proposed algorithm performs
projected gradient descent based on a novel semi-stochastic gradient
specifically desi... | computer science |
22,570 | A Large Dimensional Analysis of Least Squares Support Vector Machines | stat.ML | In this article, a large dimensional performance analysis of kernel least
squares support vector machines (LS-SVMs) is provided under the assumption of a
two-class Gaussian mixture model for the input data. Building upon recent
random matrix advances, when both the dimension of data $p$ and their number
$n$ grow large ... | computer science |
22,571 | What Can I Do Now? Guiding Users in a World of Automated Decisions | stat.ML | More and more processes governing our lives use in some part an automatic
decision step, where -- based on a feature vector derived from an applicant --
an algorithm has the decision power over the final outcome. Here we present a
simple idea which gives some of the power back to the applicant by providing
her with alt... | computer science |
22,572 | Sparse Kernel Canonical Correlation Analysis via $\ell_1$-regularization | stat.ML | Canonical correlation analysis (CCA) is a multivariate statistical technique
for finding the linear relationship between two sets of variables. The kernel
generalization of CCA named kernel CCA has been proposed to find nonlinear
relations between datasets. Despite their wide usage, they have one common
limitation that... | computer science |
22,573 | Datenqualität in Regressionsproblemen | stat.ML | Regression models are increasingly built using datasets which do not follow a
design of experiment. Instead, the data is e.g. gathered by an automated
monitoring of a technical system. As a consequence, already the input data
represents phenomena of the system and violates statistical assumptions of
distributions. The ... | computer science |
22,574 | Multi-view Regularized Gaussian Processes | stat.ML | Gaussian processes (GPs) have been proven to be powerful tools in various
areas of machine learning. However, there are very few applications of GPs in
the scenario of multi-view learning. In this paper, we present a new GP model
for multi-view learning. Unlike existing methods, it combines multiple views by
regularizi... | computer science |
22,575 | Random Forest Missing Data Algorithms | stat.ML | Random forest (RF) missing data algorithms are an attractive approach for
dealing with missing data. They have the desirable properties of being able to
handle mixed types of missing data, they are adaptive to interactions and
nonlinearity, and they have the potential to scale to big data settings.
Currently there are ... | computer science |
22,576 | Estimating Individual Treatment Effect in Observational Data Using
Random Forest Methods | stat.ML | Estimation of individual treatment effect in observational data is
complicated due to the challenges of confounding and selection bias. A useful
inferential framework to address this is the counterfactual (potential
outcomes) model which takes the hypothetical stance of asking what if an
individual had received both tr... | computer science |
22,577 | Stability Enhanced Large-Margin Classifier Selection | stat.ML | Stability is an important aspect of a classification procedure because
unstable predictions can potentially reduce users' trust in a classification
system and also harm the reproducibility of scientific conclusions. The major
goal of our work is to introduce a novel concept of classification instability,
i.e., decision... | computer science |
22,578 | The Impact of Random Models on Clustering Similarity | stat.ML | Clustering is a central approach for unsupervised learning. After clustering
is applied, the most fundamental analysis is to quantitatively compare
clusterings. Such comparisons are crucial for the evaluation of clustering
methods as well as other tasks such as consensus clustering. It is often argued
that, in order to... | computer science |
22,579 | Iterative Thresholding for Demixing Structured Superpositions in High
Dimensions | stat.ML | We consider the demixing problem of two (or more) high-dimensional vectors
from nonlinear observations when the number of such observations is far less
than the ambient dimension of the underlying vectors. Specifically, we
demonstrate an algorithm that stably estimate the underlying components under
general \emph{struc... | computer science |
22,580 | Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps | stat.ML | Random sinusoidal features are a popular approach for speeding up
kernel-based inference in large datasets. Prior to the inference stage, the
approach suggests performing dimensionality reduction by first multiplying each
data vector by a random Gaussian matrix, and then computing an element-wise
sinusoid. Theoretical ... | computer science |
22,581 | Robust mixture modelling using sub-Gaussian stable distribution | stat.ML | Heavy-tailed distributions are widely used in robust mixture modelling due to
possessing thick tails. As a computationally tractable subclass of the stable
distributions, sub-Gaussian $\alpha$-stable distribution received much interest
in the literature. Here, we introduce a type of expectation maximization
algorithm t... | computer science |
22,582 | Subset Selection for Multiple Linear Regression via Optimization | stat.ML | Subset selection in multiple linear regression is to choose a subset of
candidate explanatory variables that tradeoff error and the number of variables
selected. We built mathematical programming models for subset selection and
compare the performance of an LP-based branch-and-bound algorithm with tailored
valid inequa... | computer science |
22,583 | Boosting hazard regression with time-varying covariates | stat.ML | Consider a left-truncated right-censored survival process whose evolution
depends on time-varying covariates. Given functional data samples from the
process, we propose a practical boosting procedure for estimating its
log-intensity function. Our method does not require any separability
assumptions like Cox proportiona... | computer science |
22,584 | Prototypal Analysis and Prototypal Regression | stat.ML | Prototypal analysis is introduced to overcome two shortcomings of archetypal
analysis: its sensitivity to outliers and its non-locality, which reduces its
applicability as a learning tool. Same as archetypal analysis, prototypal
analysis finds prototypes through convex combination of the data points and
approximates th... | computer science |
22,585 | Sharp Convergence Rates for Forward Regression in High-Dimensional
Sparse Linear Models | stat.ML | Forward regression is a statistical model selection and estimation procedure
which inductively selects covariates that add predictive power into a working
statistical regression model. Once a model is selected, unknown regression
parameters are estimated by least squares. This paper analyzes forward
regression in high-... | computer science |
22,586 | Energy Prediction using Spatiotemporal Pattern Networks | stat.ML | This paper presents a novel data-driven technique based on the spatiotemporal
pattern network (STPN) for energy/power prediction for complex dynamical
systems. Built on symbolic dynamic filtering, the STPN framework is used to
capture not only the individual system characteristics but also the pair-wise
causal dependen... | computer science |
22,587 | Query Efficient Posterior Estimation in Scientific Experiments via
Bayesian Active Learning | stat.ML | A common problem in disciplines of applied Statistics research such as
Astrostatistics is of estimating the posterior distribution of relevant
parameters. Typically, the likelihoods for such models are computed via
expensive experiments such as cosmological simulations of the universe. An
urgent challenge in these rese... | computer science |
22,588 | Shape-Based Approach to Household Load Curve Clustering and Prediction | stat.ML | Consumer Demand Response (DR) is an important research and industry problem,
which seeks to categorize, predict and modify consumer's energy consumption.
Unfortunately, traditional clustering methods have resulted in many hundreds of
clusters, with a given consumer often associated with several clusters, making
it diff... | computer science |
22,589 | Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time
Series Data | stat.ML | This paper proposes a hierarchical feature extractor for non-stationary
streaming time series based on the concept of switching observable Markov chain
models. The slow time-scale non-stationary behaviors are considered to be a
mixture of quasi-stationary fast time-scale segments that are exhibited by
complex dynamical... | computer science |
22,590 | Robust Clustering for Time Series Using Spectral Densities and
Functional Data Analysis | stat.ML | In this work a robust clustering algorithm for stationary time series is
proposed. The algorithm is based on the use of estimated spectral densities,
which are considered as functional data, as the basic characteristic of
stationary time series for clustering purposes. A robust algorithm for
functional data is then app... | computer science |
22,591 | Spectral Clustering via Graph Filtering: Consistency on the
High-Dimensional Stochastic Block Model | stat.ML | Spectral clustering is amongst the most popular methods for community
detection in graphs. A key step in spectral clustering algorithms is the
eigen-decomposition of the $n{\times}n$ graph Laplacian matrix to extract its
$k$ leading eigenvectors, where $k$ is the desired number of clusters among $n$
objects. This is pr... | computer science |
22,592 | An Efficient, Expressive and Local Minima-free Method for Learning
Controlled Dynamical Systems | stat.ML | We propose a framework for modeling and estimating the state of controlled
dynamical systems, where an agent can affect the system through actions and
receives partial observations. Based on this framework, we propose the
Predictive State Representation with Random Fourier Features (RFFPSR). A key
property in RFF-PSRs ... | computer science |
22,593 | metboost: Exploratory regression analysis with hierarchically clustered
data | stat.ML | As data collections become larger, exploratory regression analysis becomes
more important but more challenging. When observations are hierarchically
clustered the problem is even more challenging because model selection with
mixed effect models can produce misleading results when nonlinear effects are
not included into... | computer science |
22,594 | Intercomparison of Machine Learning Methods for Statistical Downscaling:
The Case of Daily and Extreme Precipitation | stat.ML | Statistical downscaling of global climate models (GCMs) allows researchers to
study local climate change effects decades into the future. A wide range of
statistical models have been applied to downscaling GCMs but recent advances in
machine learning have not been explored. In this paper, we compare four
fundamental st... | computer science |
22,595 | Sequential Dirichlet Process Mixtures of Multivariate Skew
t-distributions for Model-based Clustering of Flow Cytometry Data | stat.ML | Flow cytometry is a high-throughput technology used to quantify multiple
surface and intracellular markers at the level of a single cell. This enables
to identify cell sub-types, and to determine their relative proportions.
Improvements of this technology allow to describe millions of individual cells
from a blood samp... | computer science |
22,596 | Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High
Dimensional Surfaces: An application to high-throughput toxicity testing | stat.ML | Many modern data sets are sampled with error from complex high-dimensional
surfaces. Methods such as tensor product splines or Gaussian processes are
effective/well suited for characterizing a surface in two or three dimensions
but may suffer from difficulties when representing higher dimensional surfaces.
Motivated by... | computer science |
22,597 | Additive Models with Trend Filtering | stat.ML | We consider additive models built with trend filtering, i.e., additive models
whose components are each regularized by the (discrete) total variation of
their $(k+1)$st (discrete) derivative, for a chosen integer $k \geq 0$. This
results in $k$th degree piecewise polynomial components, (e.g., $k=0$ gives
piecewise cons... | computer science |
22,598 | Estimating Nonlinear Dynamics with the ConvNet Smoother | stat.ML | Estimating the state of a dynamical system from a series of noise-corrupted
observations is fundamental in many areas of science and engineering. The most
well-known method, the Kalman smoother (and the related Kalman filter), relies
on assumptions of linearity and Gaussianity that are rarely met in practice. In
this p... | computer science |
22,599 | Observable dictionary learning for high-dimensional statistical
inference | stat.ML | This paper introduces a method for efficiently inferring a high-dimensional
distributed quantity from a few observations. The quantity of interest (QoI) is
approximated in a basis (dictionary) learned from a training set. The
coefficients associated with the approximation of the QoI in the basis are
determined by minim... | computer science |
22,600 | SAGA and Restricted Strong Convexity | stat.ML | SAGA is a fast incremental gradient method on the finite sum problem and its
effectiveness has been tested on a vast of applications. In this paper, we
analyze SAGA on a class of non-strongly convex and non-convex statistical
problem such as Lasso, group Lasso, Logistic regression with $\ell_1$
regularization, linear r... | computer science |
22,601 | Exponentially vanishing sub-optimal local minima in multilayer neural
networks | stat.ML | Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska
et al. (2015)) suggest that local minima with high error are exponentially rare
in high dimensions. However, to prove low error guarantees for Multilayer
Neural Networks (MNNs), previous works so far required either a heavily
modified MNN mod... | computer science |
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