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23,002 | Development of ICA and IVA Algorithms with Application to Medical Image
Analysis | stat.ML | Independent component analysis (ICA) is a widely used BSS method that can
uniquely achieve source recovery, subject to only scaling and permutation
ambiguities, through the assumption of statistical independence on the part of
the latent sources. Independent vector analysis (IVA) extends the applicability
of ICA by joi... | computer science |
23,003 | Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense
Stationary Ergodic Processes | stat.ML | We introduce a new unsupervised learning problem: clustering wide-sense
stationary ergodic stochastic processes. A covariance-based dissimilarity
measure and consistent algorithms are designed for clustering offline and
online data settings, respectively. We also suggest a formal criterion on the
efficiency of dissimil... | computer science |
23,004 | Solving for multi-class using orthogonal coding matrices | stat.ML | Probability estimates are desirable in statistical classification both for
gauging the accuracy of a classification result and for calibration. Here we
describe a method of solving for the conditional probabilities in multi-class
classification using orthogonal error correcting codes. The method is tested on
six differ... | computer science |
23,005 | Gradient descent revisited via an adaptive online learning rate | stat.ML | Any gradient descent optimization requires to choose a learning rate. With
deeper and deeper models, tuning that learning rate can easily become tedious
and does not necessarily lead to an ideal convergence. We propose a variation
of the gradient descent algorithm in the which the learning rate is not fixed.
Instead, w... | computer science |
23,006 | Adaptive Scan Gibbs Sampler for Large Scale Inference Problems | stat.ML | For large scale on-line inference problems the update strategy is critical
for performance. We derive an adaptive scan Gibbs sampler that optimizes the
update frequency by selecting an optimum mini-batch size. We demonstrate
performance of our adaptive batch-size Gibbs sampler by comparing it against
the collapsed Gibb... | computer science |
23,007 | Bayesian Nonparametric Modeling of Driver Behavior using HDP Split-Merge
Sampling Algorithm | stat.ML | Modern vehicles are equipped with increasingly complex sensors. These sensors
generate large volumes of data that provide opportunities for modeling and
analysis. Here, we are interested in exploiting this data to learn aspects of
behaviors and the road network associated with individual drivers. Our dataset
is collect... | computer science |
23,008 | Marketing Analytics: Methods, Practice, Implementation, and Links to
Other Fields | stat.ML | Marketing analytics is a diverse field, with both academic researchers and
practitioners coming from a range of backgrounds including marketing,
operations research, statistics, and computer science. This paper provides an
integrative review at the boundary of these three areas. The topics of
visualization, segmentatio... | computer science |
23,009 | Approximate Vanishing Ideal via Data Knotting | stat.ML | The vanishing ideal is a set of polynomials that takes zero value on the
given data points. Originally proposed in computer algebra, the vanishing ideal
has been recently exploited for extracting the nonlinear structures of data in
many applications. To avoid overfitting to noisy data, the polynomials are
often designe... | computer science |
23,010 | Tournament Leave-pair-out Cross-validation for Receiver Operating
Characteristic (ROC) Analysis | stat.ML | Receiver operating characteristic (ROC) analysis is widely used for
evaluating diagnostic systems. Recent studies have shown that estimating an
area under ROC curve (AUC) with standard cross-validation methods suffers from
a large bias. The leave-pair-out (LPO) cross-validation has been shown to
correct this bias. Howe... | computer science |
23,011 | Information Directed Sampling and Bandits with Heteroscedastic Noise | stat.ML | In the stochastic bandit problem, the goal is to maximize an unknown function
via a sequence of noisy function evaluations. Typically, the observation noise
is assumed to be independent of the evaluation point and satisfies a tail bound
taken uniformly on the domain. In this work, we consider the setting of
heterosceda... | computer science |
23,012 | Transformation Autoregressive Networks | stat.ML | The fundamental task of general density estimation $p(x)$ has been of keen
interest to machine learning. In this work, we attempt to systematically
characterize methods for density estimation. Broadly speaking, most of the
existing methods can be categorized into either using: \textit{a})
autoregressive models to estim... | computer science |
23,013 | Probabilistic Recurrent State-Space Models | stat.ML | State-space models (SSMs) are a highly expressive model class for learning
patterns in time series data and for system identification. Deterministic
versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex
time series data. Fully probabilistic SSMs, however, are often found hard to
train, even for ... | computer science |
23,014 | Nonparametric Quantile-Based Causal Discovery | stat.ML | Telling cause from effect using observational data is a challenging problem,
especially in the bivariate case. Contemporary methods often assume an
independence between the cause and the generating mechanism of the effect given
the cause. From this postulate, they derive asymmetries to uncover causal
relationships. In ... | computer science |
23,015 | Composite Gaussian Processes: Scalable Computation and Performance
Analysis | stat.ML | Gaussian process (GP) models provide a powerful tool for prediction but are
computationally prohibitive using large data sets. In such scenarios, one has
to resort to approximate methods. We derive an approximation based on a
composite likelihood approach using a general belief updating framework, which
leads to a recu... | computer science |
23,016 | Greedy Active Learning Algorithm for Logistic Regression Models | stat.ML | We study a logistic model-based active learning procedure for binary
classification problems, in which we adopt a batch subject selection strategy
with a modified sequential experimental design method. Moreover, accompanying
the proposed subject selection scheme, we simultaneously conduct a greedy
variable selection pr... | computer science |
23,017 | An Instability in Variational Inference for Topic Models | stat.ML | Topic models are Bayesian models that are frequently used to capture the
latent structure of certain corpora of documents or images. Each data element
in such a corpus (for instance each item in a collection of scientific
articles) is regarded as a convex combination of a small number of vectors
corresponding to `topic... | computer science |
23,018 | Comparison of computer systems and ranking criteria for automatic
melanoma detection in dermoscopic images | stat.ML | Melanoma is the deadliest form of skin cancer. Computer systems can assist in
melanoma detection, but are not widespread in clinical practice. In 2016, an
open challenge in classification of dermoscopic images of skin lesions was
announced. A training set of 900 images with corresponding class labels and
semi-automatic... | computer science |
23,019 | Information Assisted Dictionary Learning for fMRI data analysis | stat.ML | Extracting information from functional magnetic resonance images (fMRI) has
been a major area of research for many years, but is still demanding more
accurate techniques. Nowadays, we have a plenty of available information about
the brain-behavior that can be used to develop more precise methods. Thus, this
paper prese... | computer science |
23,020 | Multi-View Bayesian Correlated Component Analysis | stat.ML | Correlated component analysis as proposed by Dmochowski et al. (2012) is a
tool for investigating brain process similarity in the responses to multiple
views of a given stimulus. Correlated components are identified under the
assumption that the involved spatial networks are identical. Here we propose a
hierarchical pr... | computer science |
23,021 | Multi-set Canonical Correlation Analysis simply explained | stat.ML | There are a multitude of methods to perform multi-set correlated component
analysis (MCCA), including some that require iterative solutions. The methods
differ on the criterion they optimize and the constraints placed on the
solutions. This note focuses perhaps on the simplest version, which can be
solved in a single s... | computer science |
23,022 | Region Detection in Markov Random Fields: Gaussian Case | stat.ML | In this work we consider the problem of model selection in Gaussian Markov
fields in the sample deficient scenario. The benchmark information-theoretic
results in the case of d-regular graphs require the number of samples to be at
least proportional to the logarithm of the number of vertices to allow
consistent graph r... | computer science |
23,023 | Gaussian Process Classification with Privileged Information by
Soft-to-Hard Labeling Transfer | stat.ML | Learning using privileged information is an attractive problem setting that
helps many learning scenarios in the real world. A state-of-the-art method of
Gaussian process classification (GPC) with privileged information is GPC+,
which incorporates privileged information into a noise term of the likelihood.
A drawback o... | computer science |
23,024 | Safe Triplet Screening for Distance Metric Learning | stat.ML | We study safe screening for metric learning. Distance metric learning can
optimize a metric over a set of triplets, each one of which is defined by a
pair of same class instances and an instance in a different class. However, the
number of possible triplets is quite huge even for a small dataset. Our safe
triplet scree... | computer science |
23,025 | Deep Neural Networks Learn Non-Smooth Functions Effectively | stat.ML | We theoretically discuss why deep neural networks (DNNs) performs better than
other models in some cases by investigating statistical properties of DNNs for
non-smooth functions. While DNNs have empirically shown higher performance than
other standard methods, understanding its mechanism is still a challenging
problem.... | computer science |
23,026 | A probabilistic framework for multi-view feature learning with
many-to-many associations via neural networks | stat.ML | A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is
proposed for multi-view feature learning with many-to-many associations. PMvGE
is a probabilistic model for predicting new associations via graph embedding of
the nodes of data vectors with links of their associations.Multi-view data
vectors with ma... | computer science |
23,027 | Variable Selection and Task Grouping for Multi-Task Learning | stat.ML | We consider multi-task learning, which simultaneously learns related
prediction tasks, to improve generalization performance. We factorize a
coefficient matrix as the product of two matrices based on a low-rank
assumption. These matrices have sparsities to simultaneously perform variable
selection and learn and overlap... | computer science |
23,028 | Superposition-Assisted Stochastic Optimization for Hawkes Processes | stat.ML | We consider the learning of multi-agent Hawkes processes, a model containing
multiple Hawkes processes with shared endogenous impact functions and different
exogenous intensities. In the framework of stochastic maximum likelihood
estimation, we explore the associated risk bound. Further, we consider the
superposition o... | computer science |
23,029 | State Space Gaussian Processes with Non-Gaussian Likelihood | stat.ML | We provide a comprehensive overview and tooling for GP modeling with
non-Gaussian likelihoods using state space methods. The state space formulation
allows for solving one-dimensional GP models in $\mathcal{O}(n)$ time and
memory complexity. While existing literature has focused on the connection
between GP regression ... | computer science |
23,030 | Conditional Density Estimation with Bayesian Normalising Flows | stat.ML | Modeling complex conditional distributions is critical in a variety of
settings. Despite a long tradition of research into conditional density
estimation, current methods employ either simple parametric forms or are
difficult to learn in practice. This paper employs normalising flows as a
flexible likelihood model and ... | computer science |
23,031 | Vertex nomination: The canonical sampling and the extended spectral
nomination schemes | stat.ML | Suppose that one particular block in a stochastic block model is deemed
"interesting," but block labels are only observed for a few of the vertices.
Utilizing a graph realized from the model, the vertex nomination task is to
order the vertices with unobserved block labels into a "nomination list" with
the goal of havin... | computer science |
23,032 | Nonnegative PARAFAC2: a flexible coupling approach | stat.ML | Modeling variability in tensor decomposition methods is one of the challenges
of source separation. One possible solution to account for variations from one
data set to another, jointly analysed, is to resort to the PARAFAC2 model.
However, so far imposing constraints on the mode with variability has not been
possible.... | computer science |
23,033 | Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian
Optimisation | stat.ML | The paper presents a novel approach to direct covariance function learning
for Bayesian optimisation, with particular emphasis on experimental design
problems where an existing corpus of condensed knowledge is present. The method
presented borrows techniques from reproducing kernel Banach space theory
(specifically m-k... | computer science |
23,034 | High Dimensional Bayesian Optimization Using Dropout | stat.ML | Scaling Bayesian optimization to high dimensions is challenging task as the
global optimization of high-dimensional acquisition function can be expensive
and often infeasible. Existing methods depend either on limited active
variables or the additive form of the objective function. We propose a new
method for high-dime... | computer science |
23,035 | History PCA: A New Algorithm for Streaming PCA | stat.ML | In this paper we propose a new algorithm for streaming principal component
analysis. With limited memory, small devices cannot store all the samples in
the high-dimensional regime. Streaming principal component analysis aims to
find the $k$-dimensional subspace which can explain the most variation of the
$d$-dimensiona... | computer science |
23,036 | ICA based on Split Generalized Gaussian | stat.ML | Independent Component Analysis (ICA) - one of the basic tools in data
analysis - aims to find a coordinate system in which the components of the data
are independent. Most popular ICA methods use kurtosis as a metric of
non-Gaussianity to maximize, such as FastICA and JADE. However, their
assumption of fourth-order mom... | computer science |
23,037 | DeepMatch: Balancing Deep Covariate Representations for Causal Inference
Using Adversarial Training | stat.ML | We study optimal covariate balance for causal inferences from observational
data when rich covariates and complex relationships necessitate flexible
modeling with neural networks. Standard approaches such as propensity weighting
and matching/balancing fail in such settings due to miscalibrated propensity
nets and inapp... | computer science |
23,038 | Constraining the Dynamics of Deep Probabilistic Models | stat.ML | We introduce a novel generative formulation of deep probabilistic models
implementing "soft" constraints on the dynamics of the functions they can
model. In particular we develop a flexible methodological framework where the
modeled functions and derivatives of a given order are subject to inequality or
equality constr... | computer science |
23,039 | Neural Granger Causality for Nonlinear Time Series | stat.ML | While most classical approaches to Granger causality detection assume linear
dynamics, many interactions in applied domains, like neuroscience and genomics,
are inherently nonlinear. In these cases, using linear models may lead to
inconsistent estimation of Granger causal interactions. We propose a class of
nonlinear m... | computer science |
23,040 | Post Selection Inference with Incomplete Maximum Mean Discrepancy
Estimator | stat.ML | Measuring divergence between two distributions is essential in machine
learning and statistics and has various applications including binary
classification, change point detection, and two-sample test. Furthermore, in
the era of big data, designing divergence measure that is interpretable and can
handle high-dimensiona... | computer science |
23,041 | Out-of-sample extension of graph adjacency spectral embedding | stat.ML | Many popular dimensionality reduction procedures have out-of-sample
extensions, which allow a practitioner to apply a learned embedding to
observations not seen in the initial training sample. In this work, we consider
the problem of obtaining an out-of-sample extension for the adjacency spectral
embedding, a procedure... | computer science |
23,042 | Bayesian Uncertainty Estimation for Batch Normalized Deep Networks | stat.ML | Deep neural networks have led to a series of breakthroughs, dramatically
improving the state-of-the-art in many domains. The techniques driving these
advances, however, lack a formal method to account for model uncertainty. While
the Bayesian approach to learning provides a solid theoretical framework to
handle uncerta... | computer science |
23,043 | Recovery of simultaneous low rank and two-way sparse coefficient
matrices, a nonconvex approach | stat.ML | We study the problem of recovery of matrices that are simultaneously low rank
and row and/or column sparse. Such matrices appear in recent applications in
cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics.
We propose a GDT (Gradient Descent with hard Thresholding) algorithm to
efficiently r... | computer science |
23,044 | Structured Uncertainty Prediction Networks | stat.ML | This paper is the first work to propose a network to predict a structured
uncertainty distribution for a reconstructed image. Our novel model learns to
predict a full Gaussian covariance matrix for each reconstruction, which
permits efficient sampling and likelihood evaluation. We demonstrate that our
model can accurat... | computer science |
23,045 | High-Quality Prediction Intervals for Deep Learning: A
Distribution-Free, Ensembled Approach | stat.ML | Deep neural networks are a powerful technique for learning complex functions
from data. However, their appeal in real-world applications can be hindered by
an inability to quantify the uncertainty of predictions. In this paper, the
generation of prediction intervals (PI) for quantifying uncertainty in
regression tasks ... | computer science |
23,046 | The Gaussian Process Autoregressive Regression Model (GPAR) | stat.ML | Multi-output regression models must exploit dependencies between outputs to
maximise predictive performance. The application of Gaussian processes (GPs) to
this setting typically yields models that are computationally demanding and
have limited representational power. We present the Gaussian Process
Autoregressive Regr... | computer science |
23,047 | Dual Extrapolation for Faster Lasso Solvers | stat.ML | Convex sparsity-inducing regularizations are ubiquitous in high-dimension
machine learning, but their non-differentiability requires the use of iterative
solvers. To accelerate such solvers, state-of-the-art approaches consist in
reducing the size of the optimization problem at hand. In the context of
regression, this ... | computer science |
23,048 | Subspace-Induced Gaussian Processes | stat.ML | We present a new Gaussian process (GP) regression model where the covariance
kernel is indexed or parameterized by a sufficient dimension reduction subspace
of a reproducing kernel Hilbert space. The covariance kernel will be low-rank
while capturing the statistical dependency of the response to the covariates,
this af... | computer science |
23,049 | A Generative Deep Recurrent Model for Exchangeable Data | stat.ML | We present a novel model architecture which leverages deep learning tools to
perform exact Bayesian inference on sets of high dimensional, complex
observations. Our model is provably exchangeable, meaning that the joint
distribution over observations is invariant under permutation: this property
lies at the heart of Ba... | computer science |
23,050 | Path-Specific Counterfactual Fairness | stat.ML | We consider the problem of learning fair decision systems in complex
scenarios in which a sensitive attribute might affect the decision along both
fair and unfair pathways. We introduce a causal approach to disregard effects
along unfair pathways that simplifies and generalizes previous literature. Our
method corrects ... | computer science |
23,051 | An Analysis of Categorical Distributional Reinforcement Learning | stat.ML | Distributional approaches to value-based reinforcement learning model the
entire distribution of returns, rather than just their expected values, and
have recently been shown to yield state-of-the-art empirical performance. This
was demonstrated by the recently proposed C51 algorithm, based on categorical
distributiona... | computer science |
23,052 | Learning Causally-Generated Stationary Time Series | stat.ML | We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly
nonparametric model for causal, spectrally complex dynamical phenomena. The
CGPCM is a generative model in which white noise is passed through a causal,
nonparametric-window moving-average filter, a construction that we show to be
equivalent to ... | computer science |
23,053 | Kernel Recursive ABC: Point Estimation with Intractable Likelihood | stat.ML | We propose a novel approach to parameter estimation for simulator-based
statistical models with intractable likelihoods. The proposed method is
recursive application of kernel ABC and kernel herding to the same observed
data. We provide a theoretical explanation regarding why this approach works,
showing (for the popul... | computer science |
23,054 | Exact Sampling of Determinantal Point Processes without
Eigendecomposition | stat.ML | Determinantal point processes (DPPs) enable the modelling of repulsion: they
provide diverse sets of points. This repulsion is encoded in a kernel K that we
can see as a matrix storing the similarity between points. The usual algorithm
to sample DPPs is exact but it uses the spectral decomposition of K, a
computation t... | computer science |
23,055 | Learning Weighted Representations for Generalization Across Designs | stat.ML | Predictive models that generalize well under distributional shift are often
desirable and sometimes crucial to building robust and reliable machine
learning applications. We focus on distributional shift that arises in causal
inference from observational data and in unsupervised domain adaptation. We
pose both of these... | computer science |
23,056 | Conditionally Independent Multiresolution Gaussian Processes | stat.ML | We propose a multiresolution Gaussian process (GP) model which assumes
conditional independence among GPs across resolutions. We characterize each GP
using a particular representation of the Karhunen-Lo\`eve expansion where each
basis vector of the representation consists of an axis and a scale factor,
referred to as t... | computer science |
23,057 | Tunability: Importance of Hyperparameters of Machine Learning Algorithms | stat.ML | Modern machine learning algorithms for classification or regression such as
gradient boosting, random forest and neural networks involve a number of
parameters that have to be fixed before running them. Such parameters are
commonly denoted as hyperparameters in machine learning, a terminology we also
adopt here. The te... | computer science |
23,058 | Learning Binary Latent Variable Models: A Tensor Eigenpair Approach | stat.ML | Latent variable models with hidden binary units appear in various
applications. Learning such models, in particular in the presence of noise, is
a challenging computational problem. In this paper we propose a novel spectral
approach to this problem, based on the eigenvectors of both the second order
moment matrix and t... | computer science |
23,059 | Application of Rényi and Tsallis Entropies to Topic Modeling
Optimization | stat.ML | This is full length article (draft version) where problem number of topics in
Topic Modeling is discussed. We proposed idea that Renyi and Tsallis entropy
can be used for identification of optimal number in large textual collections.
We also report results of numerical experiments of Semantic stability for 4
topic mode... | computer science |
23,060 | Computational Optimal Transport | stat.ML | Optimal Transport (OT) is a mathematical gem at the interface between
probability, analysis and optimization. The goal of that theory is to define
geometric tools that are useful to compare probability distributions. Earlier
contributions originated from Monge's work in the 18th century, to be later
rediscovered under ... | computer science |
23,061 | Kernel Embedding Approaches to Orbit Determination of Spacecraft
Clusters | stat.ML | This paper presents a novel formulation and solution of orbit determination
over finite time horizons as a learning problem. We present an approach to
orbit determination under very broad conditions that are satisfied for n-body
problems. These weak conditions allow us to perform orbit determination with
noisy and high... | computer science |
23,062 | On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding | stat.ML | We develop theory for nonlinear dimensionality reduction (NLDR). A number of
NLDR methods have been developed, but there is limited understanding of how
these methods work and the relationships between them. There is limited basis
for using existing NLDR theory for deriving new algorithms. We provide a novel
framework ... | computer science |
23,063 | Bayesian Optimization for Dynamic Problems | stat.ML | We propose practical extensions to Bayesian optimization for solving dynamic
problems. We model dynamic objective functions using spatiotemporal Gaussian
process priors which capture all the instances of the functions over time. Our
extensions to Bayesian optimization use the information learnt from this model
to guide... | computer science |
23,064 | Provably robust estimation of modulo 1 samples of a smooth function with
applications to phase unwrapping | stat.ML | Consider an unknown smooth function $f: [0,1]^d \rightarrow \mathbb{R}$, and
say we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) +
\eta_i)\mod 1$, for $x_i \in [0,1]^d$, where $\eta_i$ denotes the noise. Given
the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust
estimates of the cl... | computer science |
23,065 | Nonparametric Risk Assessment and Density Estimation for Persistence
Landscapes | stat.ML | This paper presents approximate confidence intervals for each function of
parameters in a Banach space based on a bootstrap algorithm. We apply kernel
density approach to estimate the persistence landscape. In addition, we
evaluate the quality distribution function estimator of random variables using
integrated mean sq... | computer science |
23,066 | Variance Networks: When Expectation Does Not Meet Your Expectations | stat.ML | In this paper, we propose variance networks, a new model that stores the
learned information in the variances of the network weights. Surprisingly, no
information gets stored in the expectations of the weights, therefore if we
replace these weights with their expectations, we would obtain a random guess
quality predict... | computer science |
23,067 | Empirical bounds for functions with weak interactions | stat.ML | We provide sharp empirical estimates of expectation, variance and normal
approximation for a class of statistics whose variation in any argument does
not change too much when another argument is modified. Examples of such weak
interactions are furnished by U- and V-statistics, Lipschitz L-statistics and
various error f... | computer science |
23,068 | Variational Inference for Gaussian Process with Panel Count Data | stat.ML | We present the first framework for Gaussian-process-modulated Poisson
processes when the temporal data appear in the form of panel counts. Panel
count data frequently arise when experimental subjects are observed only at
discrete time points and only the numbers of occurrences of the events between
subsequent observati... | computer science |
23,069 | Learning unknown ODE models with Gaussian processes | stat.ML | In conventional ODE modelling coefficients of an equation driving the system
state forward in time are estimated. However, for many complex systems it is
practically impossible to determine the equations or interactions governing the
underlying dynamics. In these settings, parametric ODE model cannot be
formulated. Her... | computer science |
23,070 | Scalable Algorithms for Learning High-Dimensional Linear Mixed Models | stat.ML | Linear mixed models (LMMs) are used extensively to model dependecies of
observations in linear regression and are used extensively in many application
areas. Parameter estimation for LMMs can be computationally prohibitive on big
data. State-of-the-art learning algorithms require computational complexity
which depends ... | computer science |
23,071 | Simulation and Calibration of a Fully Bayesian Marked Multidimensional
Hawkes Process with Dissimilar Decays | stat.ML | We propose a simulation method for multidimensional Hawkes processes based on
superposition theory of point processes. This formulation allows us to design
efficient simulations for Hawkes processes with differing exponentially
decaying intensities. We demonstrate that inter-arrival times can be decomposed
into simpler... | computer science |
23,072 | Optimal Transport for Multi-source Domain Adaptation under Target Shift | stat.ML | In this paper, we propose to tackle the problem of reducing discrepancies
between multiple domains referred to as multi-source domain adaptation and
consider it under the target shift assumption: in all domains we aim to solve a
classification problem with the same output classes, but with labels'
proportions differing... | computer science |
23,073 | SAM: Structural Agnostic Model, Causal Discovery and Penalized
Adversarial Learning | stat.ML | We present the Structural Agnostic Model (SAM), a framework to estimate
end-to-end non-acyclic causal graphs from observational data. In a nutshell,
SAM implements an adversarial game in which a separate model generates each
variable, given real values from all others. In tandem, a discriminator
attempts to distinguish... | computer science |
23,074 | Variational zero-inflated Gaussian processes with sparse kernels | stat.ML | Zero-inflated datasets, which have an excess of zero outputs, are commonly
encountered in problems such as climate or rare event modelling. Conventional
machine learning approaches tend to overestimate the non-zeros leading to poor
performance. We propose a novel model family of zero-inflated Gaussian
processes (ZiGP) ... | computer science |
23,075 | Bucket Renormalization for Approximate Inference | stat.ML | Probabilistic graphical models are a key tool in machine learning
applications. Computing the partition function, i.e., normalizing constant, is
a fundamental task of statistical inference but it is generally computationally
intractable, leading to extensive study of approximation methods. Iterative
variational methods... | computer science |
23,076 | Uplift Modeling from Separate Labels | stat.ML | Uplift modeling is aimed at estimating the incremental impact of an action on
an individual's behavior, which is useful in various application domains such
as targeted marketing (advertisement campaigns) and personalized medicine
(medical treatments). Conventional methods of uplift modeling require every
instance to be... | computer science |
23,077 | Variational Message Passing with Structured Inference Networks | stat.ML | Recent efforts on combining deep models with probabilistic graphical models
are promising in providing flexible models that are also easy to interpret. We
propose a variational message-passing algorithm for variational inference in
such models. We make three contributions. First, we propose structured
inference network... | computer science |
23,078 | Estimation of lactate threshold with machine learning techniques in
recreational runners | stat.ML | Lactate threshold is considered an essential parameter when assessing
performance of elite and recreational runners and prescribing training
intensities in endurance sports. However, the measurement of blood lactate
concentration requires expensive equipment and the extraction of blood samples,
which are inconvenient f... | computer science |
23,079 | Modelling sparsity, heterogeneity, reciprocity and community structure
in temporal interaction data | stat.ML | We propose a novel class of network models for temporal dyadic interaction
data. Our goal is to capture a number of important features often observed in
social interactions: sparsity, degree heterogeneity, community structure and
reciprocity. We propose a family of models based on self-exciting Hawkes point
processes i... | computer science |
23,080 | Gaussian Processes indexed on the symmetric group: prediction and
learning | stat.ML | In the framework of the supervised learning of a real function defined on a
space X , the so called Kriging method stands on a real Gaussian field defined
on X. The Euclidean case is well known and has been widely studied. In this
paper, we explore the less classical case where X is the non commutative finite
group of ... | computer science |
23,081 | A particle-based variational approach to Bayesian Non-negative Matrix
Factorization | stat.ML | Bayesian Non-negative Matrix Factorization (NMF) is a promising approach for
understanding uncertainty and structure in matrix data. However, a large volume
of applied work optimizes traditional non-Bayesian NMF objectives that fail to
provide a principled understanding of the non-identifiability inherent in NMF--
an i... | computer science |
23,082 | A Linear Shift Invariant Multiscale Transform | cs.CV | This paper presents a multiscale decomposition algorithm. Unlike standard
wavelet transforms, the proposed operator is both linear and shift invariant.
The central idea is to obtain shift invariance by averaging the aligned wavelet
transform projections over all circular shifts of the signal. It is shown how
the same t... | computer science |
23,083 | General Theory of Image Normalization | cs.CV | We give a systematic, abstract formulation of the image normalization method
as applied to a general group of image transformations, and then illustrate the
abstract analysis by applying it to the hierarchy of viewing transformations of
a planar object. | computer science |
23,084 | A Differential Invariant for Zooming | cs.CV | This paper presents an invariant under scaling and linear brightness change.
The invariant is based on differentials and therefore is a local feature.
Rotationally invariant 2-d differential Gaussian operators up to third order
are proposed for the implementation of the invariant. The performance is
analyzed by simulat... | computer science |
23,085 | A Parallel Algorithm for Dilated Contour Extraction from Bilevel Images | cs.CV | We describe a simple, but efficient algorithm for the generation of dilated
contours from bilevel images. The initial part of the contour extraction is
explained to be a good candidate for parallel computer code generation. The
remainder of the algorithm is of linear nature. | computer science |
23,086 | Image Compression with Iterated Function Systems, Finite Automata and
Zerotrees: Grand Unification | cs.CV | Fractal image compression, Culik's image compression and zerotree prediction
coding of wavelet image decomposition coefficients succeed only because typical
images being compressed possess a significant degree of self-similarity.
Besides the common concept, these methods turn out to be even more tightly
related, to the... | computer science |
23,087 | Differential Invariants under Gamma Correction | cs.CV | This paper presents invariants under gamma correction and similarity
transformations. The invariants are local features based on differentials which
are implemented using derivatives of the Gaussian. The use of the proposed
invariant representation is shown to yield improved correlation results in a
template matching s... | computer science |
23,088 | Assisted Video Sequences Indexing : Motion Analysis Based on Interest
Points | cs.CV | This work deals with content-based video indexing. Our viewpoint is
semi-automatic analysis of compressed video. We consider the possible
applications of motion analysis and moving object detection : assisting moving
object indexing, summarising videos, and allowing image and motion queries. We
propose an approach base... | computer science |
23,089 | Robustness of Regional Matching Scheme over Global Matching Scheme | cs.CV | The paper has established and verified the theory prevailing widely among
image and pattern recognition specialists that the bottom-up indirect regional
matching process is the more stable and the more robust than the global
matching process against concentrated types of noise represented by clutter,
outlier or occlusi... | computer science |
23,090 | Boosting the Differences: A fast Bayesian classifier neural network | cs.CV | A Bayesian classifier that up-weights the differences in the attribute values
is discussed. Using four popular datasets from the UCI repository, some
interesting features of the network are illustrated. The network is suitable
for classification problems. | computer science |
23,091 | Distorted English Alphabet Identification : An application of Difference
Boosting Algorithm | cs.CV | The difference-boosting algorithm is used on letters dataset from the UCI
repository to classify distorted raster images of English alphabets. In
contrast to rather complex networks, the difference-boosting is found to
produce comparable or better classification efficiency on this complex problem. | computer science |
23,092 | Geometric Morphology of Granular Materials | cs.CV | We present a new method to transform the spectral pixel information of a
micrograph into an affine geometric description, which allows us to analyze the
morphology of granular materials. We use spectral and pulse-coupled neural
network based segmentation techniques to generate blobs, and a newly developed
algorithm to ... | computer science |
23,093 | Probabilistic Search for Object Segmentation and Recognition | cs.CV | The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to b... | computer science |
23,094 | Least squares fitting of circles and lines | cs.CV | We study theoretical and computational aspects of the least squares fit (LSF)
of circles and circular arcs. First we discuss the existence and uniqueness of
LSF and various parametrization schemes. Then we evaluate several popular
circle fitting algorithms and propose a new one that surpasses the existing
methods in re... | computer science |
23,095 | Statistical efficiency of curve fitting algorithms | cs.CV | We study the problem of fitting parametrized curves to noisy data. Under
certain assumptions (known as Cartesian and radial functional models), we
derive asymptotic expressions for the bias and the covariance matrix of the
parameter estimates. We also extend Kanatani's version of the Cramer-Rao lower
bound, which he pr... | computer science |
23,096 | Flexible Camera Calibration Using a New Analytical Radial Undistortion
Formula with Application to Mobile Robot Localization | cs.CV | Most algorithms in 3D computer vision rely on the pinhole camera model
because of its simplicity, whereas virtually all imaging devices introduce
certain amount of nonlinear distortion, where the radial distortion is the most
severe part. Common approach to radial distortion is by the means of polynomial
approximation,... | computer science |
23,097 | A New Analytical Radial Distortion Model for Camera Calibration | cs.CV | Common approach to radial distortion is by the means of polynomial
approximation, which introduces distortion-specific parameters into the camera
model and requires estimation of these distortion parameters. The task of
estimating radial distortion is to find a radial distortion model that allows
easy undistortion as w... | computer science |
23,098 | Rational Radial Distortion Models with Analytical Undistortion Formulae | cs.CV | The common approach to radial distortion is by the means of polynomial
approximation, which introduces distortion-specific parameters into the camera
model and requires estimation of these distortion parameters. The task of
estimating radial distortion is to find a radial distortion model that allows
easy undistortion ... | computer science |
23,099 | An Analytical Piecewise Radial Distortion Model for Precision Camera
Calibration | cs.CV | The common approach to radial distortion is by the means of polynomial
approximation, which introduces distortion-specific parameters into the camera
model and requires estimation of these distortion parameters. The task of
estimating radial distortion is to find a radial distortion model that allows
easy undistortion ... | computer science |
23,100 | Camera Calibration: a USU Implementation | cs.CV | The task of camera calibration is to estimate the intrinsic and extrinsic
parameters of a camera model. Though there are some restricted techniques to
infer the 3-D information about the scene from uncalibrated cameras, effective
camera calibration procedures will open up the possibility of using a wide
range of existi... | computer science |
23,101 | A Family of Simplified Geometric Distortion Models for Camera
Calibration | cs.CV | The commonly used radial distortion model for camera calibration is in fact
an assumption or a restriction. In practice, camera distortion could happen in
a general geometrical manner that is not limited to the radial sense. This
paper proposes a simplified geometrical distortion modeling method by using two
different ... | computer science |
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