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22,702 | Group-sparse block PCA and explained variance | stat.ML | The paper addresses the simultneous determination of goup-sparse loadings by
block optimization, and the correlated problem of defining explained variance
for a set of non orthogonal components. We give in both cases a comprehensive
mathematical presentation of the problem, which leads to propose i) a new
formulation/a... | computer science |
22,703 | Vertex Nomination Via Local Neighborhood Matching | stat.ML | Consider two networks on overlapping, non-identical vertex sets. Given
vertices of interest in the first network, we seek to identify the
corresponding vertices, if any exist, in the second network. While in
moderately sized networks graph matching methods can be applied directly to
recover the missing correspondences,... | computer science |
22,704 | Experimental Design for Non-Parametric Correction of Misspecified
Dynamical Models | stat.ML | We consider a class of misspecified dynamical models where the governing term
is only approximately known. Under the assumption that observations of the
system's evolution are accessible for various initial conditions, our goal is
to infer a non-parametric correction to the misspecified driving term such as
to faithful... | computer science |
22,705 | Mass Volume Curves and Anomaly Ranking | stat.ML | This paper aims at formulating the issue of ranking multivariate unlabeled
observations depending on their degree of abnormality as an unsupervised
statistical learning task. In the 1-d situation, this problem is usually
tackled by means of tail estimation techniques: univariate observations are
viewed as all the more ... | computer science |
22,706 | Linear Regression with Shuffled Labels | stat.ML | Is it possible to perform linear regression on datasets whose labels are
shuffled with respect to the inputs? We explore this question by proposing
several estimators that recover the weights of a noisy linear model from labels
that are shuffled by an unknown permutation. We show that the analog of the
classical least-... | computer science |
22,707 | Semiparametric spectral modeling of the Drosophila connectome | stat.ML | We present semiparametric spectral modeling of the complete larval Drosophila
mushroom body connectome. Motivated by a thorough exploratory data analysis of
the network via Gaussian mixture modeling (GMM) in the adjacency spectral
embedding (ASE) representation space, we introduce the latent structure model
(LSM) for n... | computer science |
22,708 | SILVar: Single Index Latent Variable Models | stat.ML | A semi-parametric, non-linear regression model in the presence of latent
variables is introduced. These latent variables can correspond to unmodeled
phenomena or unmeasured agents in a complex networked system. This new
formulation allows joint estimation of certain non-linearities in the system,
the direct interaction... | computer science |
22,709 | Influence Function and Robust Variant of Kernel Canonical Correlation
Analysis | stat.ML | Many unsupervised kernel methods rely on the estimation of the kernel
covariance operator (kernel CO) or kernel cross-covariance operator (kernel
CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even
when bounded positive definite kernels are used. To the best of our knowledge,
there are few well... | computer science |
22,710 | ShortFuse: Biomedical Time Series Representations in the Presence of
Structured Information | stat.ML | In healthcare applications, temporal variables that encode movement, health
status and longitudinal patient evolution are often accompanied by rich
structured information such as demographics, diagnostics and medical exam data.
However, current methods do not jointly optimize over structured covariates and
time series ... | computer science |
22,711 | Learning task structure via sparsity grouped multitask learning | stat.ML | Sparse mapping has been a key methodology in many high-dimensional scientific
problems. When multiple tasks share the set of relevant features, learning them
jointly in a group drastically improves the quality of relevant feature
selection. However, in practice this technique is used limitedly since such
grouping infor... | computer science |
22,712 | Convex Coupled Matrix and Tensor Completion | stat.ML | We propose a set of convex low rank inducing norms for a coupled matrices and
tensors (hereafter coupled tensors), which shares information between matrices
and tensors through common modes. More specifically, we propose a mixture of
the overlapped trace norm and the latent norms with the matrix trace norm, and
then, w... | computer science |
22,713 | Unimodal probability distributions for deep ordinal classification | stat.ML | Probability distributions produced by the cross-entropy loss for ordinal
classification problems can possess undesired properties. We propose a
straightforward technique to constrain discrete ordinal probability
distributions to be unimodal via the use of the Poisson and binomial
probability distributions. We evaluate ... | computer science |
22,714 | Probabilistic Matrix Factorization for Automated Machine Learning | stat.ML | In order to achieve state-of-the-art performance, modern machine learning
techniques require careful data pre-processing and hyperparameter tuning.
Moreover, given the ever increasing number of machine learning models being
developed, model selection is becoming increasingly important. Automating the
selection and tuni... | computer science |
22,715 | Kernel clustering: density biases and solutions | stat.ML | Kernel methods are popular in clustering due to their generality and
discriminating power. However, we show that many kernel clustering criteria
have density biases theoretically explaining some practically significant
artifacts empirically observed in the past. For example, we provide conditions
and formally prove the... | computer science |
22,716 | Co-clustering through Optimal Transport | stat.ML | In this paper, we present a novel method for co-clustering, an unsupervised
learning approach that aims at discovering homogeneous groups of data instances
and features by grouping them simultaneously. The proposed method uses the
entropy regularized optimal transport between empirical measures defined on
data instance... | computer science |
22,717 | Learning Gaussian Graphical Models Using Discriminated Hub Graphical
Lasso | stat.ML | We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based
on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply
this new method in two situations: with known hubs and without known hubs. Then
we compare DHGL with HGL using several measures of performance. When some hubs
are k... | computer science |
22,718 | Linear Dimensionality Reduction in Linear Time:
Johnson-Lindenstrauss-type Guarantees for Random Subspace | stat.ML | We consider the problem of efficient randomized dimensionality reduction with
norm-preservation guarantees. Specifically we prove data-dependent
Johnson-Lindenstrauss-type geometry preservation guarantees for Ho's random
subspace method: When data satisfy a mild regularity condition -- the extent of
which can be estima... | computer science |
22,719 | Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence
Modeling | stat.ML | Analyzing the underlying structure of multiple time-sequences provides
insight into the understanding of social networks and human activities. In this
work, we present the Bayesian nonparametric Poisson process allocation
(BaNPPA), a generative model to automatically infer the number of latent
functions in temporal dat... | computer science |
22,720 | Scalable Variational Inference for Dynamical Systems | stat.ML | Gradient matching is a promising tool for learning parameters and state
dynamics of ordinary differential equations. It is a grid free inference
approach which for fully observable systems is at times competitive with
numerical integration. However for many real-world applications, only sparse
observations are availabl... | computer science |
22,721 | The Kernel Mixture Network: A Nonparametric Method for Conditional
Density Estimation of Continuous Random Variables | stat.ML | This paper introduces the kernel mixture network, a new method for
nonparametric estimation of conditional probability densities using neural
networks. We model arbitrarily complex conditional densities as linear
combinations of a family of kernel functions centered at a subset of training
points. The weights are deter... | computer science |
22,722 | Streaming Sparse Gaussian Process Approximations | stat.ML | Sparse pseudo-point approximations for Gaussian process (GP) models provide a
suite of methods that support deployment of GPs in the large data regime and
enable analytic intractabilities to be sidestepped. However, the field lacks a
principled method to handle streaming data in which both the posterior
distribution ov... | computer science |
22,723 | Data-driven Optimal Transport Cost Selection for Distributionally Robust
Optimizatio | stat.ML | Recently, (Blanchet, Kang, and Murhy 2016) showed that several machine
learning algorithms, such as square-root Lasso, Support Vector Machines, and
regularized logistic regression, among many others, can be represented exactly
as distributionally robust optimization (DRO) problems. The distributional
uncertainty is def... | computer science |
22,724 | Doubly Robust Data-Driven Distributionally Robust Optimization | stat.ML | Data-driven Distributionally Robust Optimization (DD-DRO) via optimal
transport has been shown to encompass a wide range of popular machine learning
algorithms. The distributional uncertainty size is often shown to correspond to
the regularization parameter. The type of regularization (e.g. the norm used to
regularize)... | computer science |
22,725 | Accelerated Inference for Latent Variable Models | stat.ML | Inference of latent feature models in the Bayesian nonparametric setting is
generally difficult, especially in high dimensional settings, because it
usually requires proposing features from some prior distribution. In special
cases, where the integration is tractable, we could sample feature assignments
according to a ... | computer science |
22,726 | Data-adaptive Active Sampling for Efficient Graph-Cognizant
Classification | stat.ML | The present work deals with active sampling of graph nodes representing
training data for binary classification. The graph may be given or constructed
using similarity measures among nodal features. Leveraging the graph for
classification builds on the premise that labels across neighboring nodes are
correlated accordi... | computer science |
22,727 | Structured Bayesian Pruning via Log-Normal Multiplicative Noise | stat.ML | Dropout-based regularization methods can be regarded as injecting random
noise with pre-defined magnitude to different parts of the neural network
during training. It was recently shown that Bayesian dropout procedure not only
improves generalization but also leads to extremely sparse neural architectures
by automatica... | computer science |
22,728 | Accelerated Hierarchical Density Clustering | stat.ML | We present an accelerated algorithm for hierarchical density based
clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a
significant qualitative improvement over the popular DBSCAN algorithm. The
accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while
supporting variable ... | computer science |
22,729 | Calibrating Black Box Classification Models through the Thresholding
Method | stat.ML | In high-dimensional classification settings, we wish to seek a balance
between high power and ensuring control over a desired loss function. In many
settings, the points most likely to be misclassified are those who lie near the
decision boundary of the given classification method. Often, these
uninformative points sho... | computer science |
22,730 | Honey Bee Dance Modeling in Real-time using Machine Learning | stat.ML | The waggle dance that honeybees perform is an astonishing way of
communicating the location of food source. After over 60 years of its
discovery, researchers still use manual labeling by watching hours of dance
videos to detect different transitions between dance components thus extracting
information regarding the dis... | computer science |
22,731 | Improved Algorithms for Matrix Recovery from Rank-One Projections | stat.ML | We consider the problem of estimation of a low-rank matrix from a limited
number of noisy rank-one projections. In particular, we propose two fast,
non-convex \emph{proper} algorithms for matrix recovery and support them with
rigorous theoretical analysis. We show that the proposed algorithms enjoy
linear convergence a... | computer science |
22,732 | Union of Intersections (UoI) for Interpretable Data Driven Discovery and
Prediction | stat.ML | The increasing size and complexity of scientific data could dramatically
enhance discovery and prediction for basic scientific applications. Realizing
this potential, however, requires novel statistical analysis methods that are
both interpretable and predictive. We introduce Union of Intersections (UoI), a
flexible, m... | computer science |
22,733 | Improved Clustering with Augmented k-means | stat.ML | Identifying a set of homogeneous clusters in a heterogeneous dataset is one
of the most important classes of problems in statistical modeling. In the realm
of unsupervised partitional clustering, k-means is a very important algorithm
for this. In this technical report, we develop a new k-means variant called
Augmented ... | computer science |
22,734 | Guide Actor-Critic for Continuous Control | stat.ML | Actor-critic methods solve reinforcement learning problems by updating a
parameterized policy known as an actor in a direction that increases an
estimate of the expected return known as a critic. However, existing
actor-critic methods only use values or gradients of the critic to update the
policy parameter. In this pa... | computer science |
22,735 | From optimal transport to generative modeling: the VEGAN cookbook | stat.ML | We study unsupervised generative modeling in terms of the optimal transport
(OT) problem between true (but unknown) data distribution $P_X$ and the latent
variable model distribution $P_G$. We show that the OT problem can be
equivalently written in terms of probabilistic encoders, which are constrained
to match the pos... | computer science |
22,736 | ReFACTor: Practical Low-Rank Matrix Estimation Under Column-Sparsity | stat.ML | Various problems in data analysis and statistical genetics call for recovery
of a column-sparse, low-rank matrix from noisy observations. We propose
ReFACTor, a simple variation of the classical Truncated Singular Value
Decomposition (TSVD) algorithm. In contrast to previous sparse principal
component analysis (PCA) al... | computer science |
22,737 | An Asynchronous Distributed Framework for Large-scale Learning Based on
Parameter Exchanges | stat.ML | In many distributed learning problems, the heterogeneous loading of computing
machines may harm the overall performance of synchronous strategies. In this
paper, we propose an effective asynchronous distributed framework for the
minimization of a sum of smooth functions, where each machine performs
iterations in parall... | computer science |
22,738 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational
Learning | stat.ML | Deep generative models provide powerful tools for distributions over
complicated manifolds, such as those of natural images. But many of these
methods, including generative adversarial networks (GANs), can be difficult to
train, in part because they are prone to mode collapse, which means that they
characterize only a ... | computer science |
22,739 | Concrete Dropout | stat.ML | Dropout is used as a practical tool to obtain uncertainty estimates in large
vision models and reinforcement learning (RL) tasks. But to obtain
well-calibrated uncertainty estimates, a grid-search over the dropout
probabilities is necessary - a prohibitive operation with large models, and an
impossible one with RL. We ... | computer science |
22,740 | Real Time Image Saliency for Black Box Classifiers | stat.ML | In this work we develop a fast saliency detection method that can be applied
to any differentiable image classifier. We train a masking model to manipulate
the scores of the classifier by masking salient parts of the input image. Our
model generalises well to unseen images and requires a single forward pass to
perform ... | computer science |
22,741 | 3D Convolutional Neural Networks for Brain Tumor Segmentation: A
Comparison of Multi-resolution Architectures | stat.ML | This paper analyzes the use of 3D Convolutional Neural Networks for brain
tumor segmentation in MR images. We address the problem using three different
architectures that combine fine and coarse features to obtain the final
segmentation. We compare three different networks that use multi-resolution
features in terms of... | computer science |
22,742 | Community Detection with Graph Neural Networks | stat.ML | We study data-driven methods for community detection in graphs. This
estimation problem is typically formulated in terms of the spectrum of certain
operators, as well as via posterior inference under certain probabilistic
graphical models. Focusing on random graph families such as the Stochastic
Block Model, recent res... | computer science |
22,743 | An experimental study of graph-based semi-supervised classification with
additional node information | stat.ML | The volume of data generated by internet and social networks is increasing
every day, and there is a clear need for efficient ways of extracting useful
information from them. As those data can take different forms, it is important
to use all the available data representations for prediction.
In this paper, we focus o... | computer science |
22,744 | Boundary Crossing Probabilities for General Exponential Families | stat.ML | We consider parametric exponential families of dimension $K$ on the real
line. We study a variant of \textit{boundary crossing probabilities} coming
from the multi-armed bandit literature, in the case when the real-valued
distributions form an exponential family of dimension $K$. Formally, our result
is a concentration... | computer science |
22,745 | Doubly Stochastic Variational Inference for Deep Gaussian Processes | stat.ML | Gaussian processes (GPs) are a good choice for function approximation as they
are flexible, robust to over-fitting, and provide well-calibrated predictive
uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of
GPs, but inference in these models has proved challenging. Existing approaches
to infe... | computer science |
22,746 | Expectation Propagation for t-Exponential Family Using Q-Algebra | stat.ML | Exponential family distributions are highly useful in machine learning since
their calculation can be performed efficiently through natural parameters. The
exponential family has recently been extended to the t-exponential family,
which contains Student-t distributions as family members and thus allows us to
handle noi... | computer science |
22,747 | Convergence of Langevin MCMC in KL-divergence | stat.ML | Langevin diffusion is a commonly used tool for sampling from a given
distribution. In this work, we establish that when the target density $p^*$ is
such that $\log p^*$ is $L$ smooth and $m$ strongly convex, discrete Langevin
diffusion produces a distribution $p$ with $KL(p||p^*)\leq \epsilon$ in
$\tilde{O}(\frac{d}{\e... | computer science |
22,748 | Non-parametric estimation of Jensen-Shannon Divergence in Generative
Adversarial Network training | stat.ML | Generative Adversarial Networks (GANs) have become a widely popular framework
for generative modelling of high-dimensional datasets. However their training
is well-known to be difficult. This work presents a rigorous statistical
analysis of GANs providing straight-forward explanations for common training
pathologies su... | computer science |
22,749 | Predictive State Recurrent Neural Networks | stat.ML | We present a new model, Predictive State Recurrent Neural Networks (PSRNNs),
for filtering and prediction in dynamical systems. PSRNNs draw on insights from
both Recurrent Neural Networks (RNNs) and Predictive State Representations
(PSRs), and inherit advantages from both types of models. Like many successful
RNN archi... | computer science |
22,750 | Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and
High Frequency Trading | stat.ML | Deep learning applies hierarchical layers of hidden variables to construct
nonlinear high dimensional predictors. Our goal is to develop and train deep
learning architectures for spatio-temporal modeling. Training a deep
architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO)
for parameter regul... | computer science |
22,751 | Auto-Encoding Sequential Monte Carlo | stat.ML | We introduce AESMC: a method for using deep neural networks for simultaneous
model learning and inference amortization in a broad family of structured
probabilistic models. Starting with an unlabeled dataset and a partially
specified underlying generative model, AESMC refines the generative model and
learns efficient p... | computer science |
22,752 | Fair Inference On Outcomes | stat.ML | In this paper, we consider the problem of fair statistical inference
involving outcome variables. Examples include classification and regression
problems, and estimating treatment effects in randomized trials or
observational data. The issue of fairness arises in such problems where some
covariates or treatments are "s... | computer science |
22,753 | Model Selection in Bayesian Neural Networks via Horseshoe Priors | stat.ML | Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
In this work, we apply a horseshoe prior over node pre-activations of a
Bayesian neur... | computer science |
22,754 | Large Linear Multi-output Gaussian Process Learning | stat.ML | Gaussian processes (GPs), or distributions over arbitrary functions in a
continuous domain, can be generalized to the multi-output case: a linear model
of coregionalization (LMC) is one approach. LMCs estimate and exploit
correlations across the multiple outputs. While model estimation can be
performed efficiently for ... | computer science |
22,755 | Dynamics Based Features For Graph Classification | stat.ML | Numerous social, medical, engineering and biological challenges can be framed
as graph-based learning tasks. Here, we propose a new feature based approach to
network classification. We show how dynamics on a network can be useful to
reveal patterns about the organization of the components of the underlying
graph where ... | computer science |
22,756 | Identification of Gaussian Process State Space Models | stat.ML | The Gaussian process state space model (GPSSM) is a non-linear dynamical
system, where unknown transition and/or measurement mappings are described by
GPs. Most research in GPSSMs has focussed on the state estimation problem,
i.e., computing a posterior of the latent state given the model. However, the
key challenge in... | computer science |
22,757 | Learning Graphs with Monotone Topology Properties and Multiple Connected
Components | stat.ML | Recent papers have formulated the problem of learning graphs from data as an
inverse covariance estimation with graph Laplacian constraints. While such
problems are convex, existing methods cannot guarantee that solutions will have
specific graph topology properties (e.g., being $k$-partite), which are
desirable for so... | computer science |
22,758 | Efficient learning with robust gradient descent | stat.ML | Minimizing the empirical risk is a popular training strategy, but for
learning tasks where the data may be noisy or heavy-tailed, one may require
many observations in order to generalize well. To achieve better performance
under less stringent requirements, we introduce a procedure which constructs a
robust approximati... | computer science |
22,759 | Learning Generative Models with Sinkhorn Divergences | stat.ML | The ability to compare two degenerate probability distributions (i.e. two
probability distributions supported on two distinct low-dimensional manifolds
living in a much higher-dimensional space) is a crucial problem arising in the
estimation of generative models for high-dimensional observations such as those
arising i... | computer science |
22,760 | Selective Inference for Change Point Detection in Multi-dimensional
Sequences | stat.ML | We study the problem of detecting change points (CPs) that are characterized
by a subset of dimensions in a multi-dimensional sequence. A method for
detecting those CPs can be formulated as a two-stage method: one for selecting
relevant dimensions, and another for selecting CPs. It has been difficult to
properly contro... | computer science |
22,761 | Understanding the Learned Iterative Soft Thresholding Algorithm with
matrix factorization | stat.ML | Sparse coding is a core building block in many data analysis and machine
learning pipelines. Typically it is solved by relying on generic optimization
techniques, such as the Iterative Soft Thresholding Algorithm and its
accelerated version (ISTA, FISTA). These methods are optimal in the class of
first-order methods fo... | computer science |
22,762 | Batched Large-scale Bayesian Optimization in High-dimensional Spaces | stat.ML | Bayesian optimization (BO) has become an effective approach for black-box
function optimization problems when function evaluations are expensive and the
optimum can be achieved within a relatively small number of queries. However,
many cases, such as the ones with high-dimensional inputs, may require a much
larger numb... | computer science |
22,763 | GAN and VAE from an Optimal Transport Point of View | stat.ML | This short article revisits some of the ideas introduced in arXiv:1701.07875
and arXiv:1705.07642 in a simple setup. This sheds some lights on the
connexions between Variational Autoencoders (VAE), Generative Adversarial
Networks (GAN) and Minimum Kantorovitch Estimators (MKE). | computer science |
22,764 | Parallel and Distributed Thompson Sampling for Large-scale Accelerated
Exploration of Chemical Space | stat.ML | Chemical space is so large that brute force searches for new interesting
molecules are infeasible. High-throughput virtual screening via computer
cluster simulations can speed up the discovery process by collecting very large
amounts of data in parallel, e.g., up to hundreds or thousands of parallel
measurements. Bayes... | computer science |
22,765 | Shape Parameter Estimation | stat.ML | Performance of machine learning approaches depends strongly on the choice of
misfit penalty, and correct choice of penalty parameters, such as the threshold
of the Huber function. These parameters are typically chosen using expert
knowledge, cross-validation, or black-box optimization, which are time
consuming for larg... | computer science |
22,766 | Improving Variational Auto-Encoders using convex combination linear
Inverse Autoregressive Flow | stat.ML | In this paper, we propose a new volume-preserving flow and show that it
performs similarly to the linear general normalizing flow. The idea is to
enrich a linear Inverse Autoregressive Flow by introducing multiple
lower-triangular matrices with ones on the diagonal and combining them using a
convex combination. In the ... | computer science |
22,767 | Outlier Detection Using Distributionally Robust Optimization under the
Wasserstein Metric | stat.ML | We present a Distributionally Robust Optimization (DRO) approach to outlier
detection in a linear regression setting, where the closeness of probability
distributions is measured using the Wasserstein metric. Training samples
contaminated with outliers skew the regression plane computed by least squares
and thus impede... | computer science |
22,768 | Consistency Results for Stationary Autoregressive Processes with
Constrained Coefficients | stat.ML | We consider stationary autoregressive processes with coefficients restricted
to an ellipsoid, which includes autoregressive processes with absolutely
summable coefficients. We provide consistency results under different norms for
the estimation of such processes using constrained and penalized estimators. As
an applica... | computer science |
22,769 | Time Series Using Exponential Smoothing Cells | stat.ML | Time series analysis is used to understand and predict dynamic processes,
including evolving demands in business, weather, markets, and biological
rhythms. Exponential smoothing is used in all these domains to obtain simple
interpretable models of time series and to forecast future values. Despite its
popularity, expon... | computer science |
22,770 | An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic
Riemannian Optimization | stat.ML | We consider maximum likelihood estimation for Gaussian Mixture Models (Gmms).
This task is almost invariably solved (in theory and practice) via the
Expectation Maximization (EM) algorithm. EM owes its success to various
factors, of which is its ability to fulfill positive definiteness constraints
in closed form is of ... | computer science |
22,771 | Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate
Monitoring from Ballistocardiograms | stat.ML | A multiple instance dictionary learning approach, Dictionary Learning using
Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat
heart rate estimation and to characterize heartbeat signatures from
ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI
estimates a "heartbea... | computer science |
22,772 | Practical Gauss-Newton Optimisation for Deep Learning | stat.ML | We present an efficient block-diagonal ap- proximation to the Gauss-Newton
matrix for feedforward neural networks. Our result- ing algorithm is
competitive against state- of-the-art first order optimisation methods, with
sometimes significant improvement in optimisation performance. Unlike
first-order methods, for whic... | computer science |
22,773 | Dealing with Integer-valued Variables in Bayesian Optimization with
Gaussian Processes | stat.ML | Bayesian optimization (BO) methods are useful for optimizing functions that
are expensive to evaluate, lack an analytical expression and whose evaluations
can be contaminated by noise. These methods rely on a probabilistic model of
the objective function, typically a Gaussian process (GP), upon which an
acquisition fun... | computer science |
22,774 | General Latent Feature Models for Heterogeneous Datasets | stat.ML | Latent feature modeling allows capturing the latent structure responsible for
generating the observed properties of a set of objects. It is often used to
make predictions either for new values of interest or missing information in
the original data, as well as to perform data exploratory analysis. However,
although the... | computer science |
22,775 | Stochastic Gradient MCMC Methods for Hidden Markov Models | stat.ML | Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling
Bayesian inference to large datasets under an assumption of i.i.d data. We
instead develop an SG-MCMC algorithm to learn the parameters of hidden Markov
models (HMMs) for time-dependent data. There are two challenges to applying
SG-MCMC in this... | computer science |
22,776 | Deep Generative Models for Relational Data with Side Information | stat.ML | We present a probabilistic framework for overlapping community discovery and
link prediction for relational data, given as a graph. The proposed framework
has: (1) a deep architecture which enables us to infer multiple layers of
latent features/communities for each node, providing superior link prediction
performance o... | computer science |
22,777 | Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification | stat.ML | The two-sample hypothesis testing problem is studied for the challenging
scenario of high dimensional data sets with small sample sizes. We show that
the two-sample hypothesis testing problem can be posed as a one-class set
classification problem. In the set classification problem the goal is to
classify a set of data ... | computer science |
22,778 | A Comparison of Resampling and Recursive Partitioning Methods in Random
Forest for Estimating the Asymptotic Variance Using the Infinitesimal
Jackknife | stat.ML | The infinitesimal jackknife (IJ) has recently been applied to the random
forest to estimate its prediction variance. These theorems were verified under
a traditional random forest framework which uses classification and regression
trees (CART) and bootstrap resampling. However, random forests using
conditional inferenc... | computer science |
22,779 | Infinite Mixture Model of Markov Chains | stat.ML | We propose a Bayesian nonparametric mixture model for prediction- and
information extraction tasks with an efficient inference scheme. It models
categorical-valued time series that exhibit dynamics from multiple underlying
patterns (e.g. user behavior traces). We simplify the idea of capturing these
patterns by hierarc... | computer science |
22,780 | Interpretable Predictions of Tree-based Ensembles via Actionable Feature
Tweaking | stat.ML | Machine-learned models are often described as "black boxes". In many
real-world applications however, models may have to sacrifice predictive power
in favour of human-interpretability. When this is the case, feature engineering
becomes a crucial task, which requires significant and time-consuming human
effort. Whilst s... | computer science |
22,781 | An Unsupervised Method for Estimating the Global Horizontal Irradiance
from Photovoltaic Power Measurements | stat.ML | In this paper, we present a method to determine the global horizontal
irradiance (GHI) from the power measurements of one or more PV systems, located
in the same neighborhood. The method is completely unsupervised and is based on
a physical model of a PV plant. The precise assessment of solar irradiance is
pivotal for ... | computer science |
22,782 | Ensembles of Models and Metrics for Robust Ranking of Homologous
Proteins | stat.ML | An ensemble of models (EM), where each model is constructed on a diverse
subset of feature variables, is proposed to rank rare class items ahead of
majority class items in a highly unbalanced two class problem. The proposed
ensemble relies on an algorithm to group the feature variables into subsets
where the variables ... | computer science |
22,783 | Scalable Multi-Class Gaussian Process Classification using Expectation
Propagation | stat.ML | This paper describes an expectation propagation (EP) method for multi-class
classification with Gaussian processes that scales well to very large datasets.
In such a method the estimate of the log-marginal-likelihood involves a sum
across the data instances. This enables efficient training using stochastic
gradients an... | computer science |
22,784 | Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery | stat.ML | We adopt data structure in the form of cover trees and iteratively apply
approximate nearest neighbour (ANN) searches for fast compressed sensing
reconstruction of signals living on discrete smooth manifolds. Levering on the
recent stability results for the inexact Iterative Projected Gradient (IPG)
algorithm and by us... | computer science |
22,785 | Dr.VAE: Drug Response Variational Autoencoder | stat.ML | We present two deep generative models based on Variational Autoencoders to
improve the accuracy of drug response prediction. Our models, Perturbation
Variational Autoencoder and its semi-supervised extension, Drug Response
Variational Autoencoder (Dr.VAE), learn latent representation of the underlying
gene states befor... | computer science |
22,786 | YouTube-8M Video Understanding Challenge Approach and Applications | stat.ML | This paper introduces the YouTube-8M Video Understanding Challenge hosted as
a Kaggle competition and also describes my approach to experimenting with
various models. For each of my experiments, I provide the score result as well
as possible improvements to be made. Towards the end of the paper, I discuss
the various e... | computer science |
22,787 | Efficient Manifold and Subspace Approximations with Spherelets | stat.ML | Data lying in a high-dimensional ambient space are commonly thought to have a
much lower intrinsic dimension. In particular, the data may be concentrated
near a lower-dimensional subspace or manifold. There is an immense literature
focused on approximating the unknown subspace, and in exploiting such
approximations in ... | computer science |
22,788 | Uncertainty Decomposition in Bayesian Neural Networks with Latent
Variables | stat.ML | Bayesian neural networks (BNNs) with latent variables are probabilistic
models which can automatically identify complex stochastic patterns in the
data. We describe and study in these models a decomposition of predictive
uncertainty into its epistemic and aleatoric components. First, we show how
such a decomposition ar... | computer science |
22,789 | Two-Stage Hybrid Day-Ahead Solar Forecasting | stat.ML | Power supply from renewable resources is on a global rise where it is
forecasted that renewable generation will surpass other types of generation in
a foreseeable future. Increased generation from renewable resources, mainly
solar and wind, exposes the power grid to more vulnerabilities, conceivably due
to their variab... | computer science |
22,790 | Fast Algorithms for Learning Latent Variables in Graphical Models | stat.ML | We study the problem of learning latent variables in Gaussian graphical
models. Existing methods for this problem assume that the precision matrix of
the observed variables is the superposition of a sparse and a low-rank
component. In this paper, we focus on the estimation of the low-rank component,
which encodes the e... | computer science |
22,791 | Unsupervised Learning via Total Correlation Explanation | stat.ML | Learning by children and animals occurs effortlessly and largely without
obvious supervision. Successes in automating supervised learning have not
translated to the more ambiguous realm of unsupervised learning where goals and
labels are not provided. Barlow (1961) suggested that the signal that brains
leverage for uns... | computer science |
22,792 | Generalized notions of sparsity and restricted isometry property. Part
II: Applications | stat.ML | The restricted isometry property (RIP) is a universal tool for data recovery.
We explore the implication of the RIP in the framework of generalized sparsity
and group measurements introduced in the Part I paper. It turns out that for a
given measurement instrument the number of measurements for RIP can be improved
by o... | computer science |
22,793 | Bayesian Semisupervised Learning with Deep Generative Models | stat.ML | Neural network based generative models with discriminative components are a
powerful approach for semi-supervised learning. However, these techniques a)
cannot account for model uncertainty in the estimation of the model's
discriminative component and b) lack flexibility to capture complex stochastic
patterns in the la... | computer science |
22,794 | A Fixed-Point of View on Gradient Methods for Big Data | stat.ML | Interpreting gradient methods as fixed-point iterations, we provide a
detailed analysis of those methods for minimizing convex objective functions.
Due to their conceptual and algorithmic simplicity, gradient methods are widely
used in machine learning for massive data sets (big data). In particular,
stochastic gradien... | computer science |
22,795 | Towards Bursting Filter Bubble via Contextual Risks and Uncertainties | stat.ML | A rising topic in computational journalism is how to enhance the diversity in
news served to subscribers to foster exploration behavior in news reading.
Despite the success of preference learning in personalized news recommendation,
their over-exploitation causes filter bubble that isolates readers from
opposing viewpo... | computer science |
22,796 | Nuclear penalized multinomial regression with an application to
predicting at bat outcomes in baseball | stat.ML | We propose the nuclear norm penalty as an alternative to the ridge penalty
for regularized multinomial regression. This convex relaxation of reduced-rank
multinomial regression has the advantage of leveraging underlying structure
among the response categories to make better predictions. We apply our method,
nuclear pen... | computer science |
22,797 | Some methods for heterogeneous treatment effect estimation in
high-dimensions | stat.ML | When devising a course of treatment for a patient, doctors often have little
quantitative evidence on which to base their decisions, beyond their medical
education and published clinical trials. Stanford Health Care alone has
millions of electronic medical records (EMRs) that are only just recently being
leveraged to i... | computer science |
22,798 | Regression Phalanxes | stat.ML | Tomal et al. (2015) introduced the notion of "phalanxes" in the context of
rare-class detection in two-class classification problems. A phalanx is a
subset of features that work well for classification tasks. In this paper, we
propose a different class of phalanxes for application in regression settings.
We define a "R... | computer science |
22,799 | Mode-Seeking Clustering and Density Ridge Estimation via Direct
Estimation of Density-Derivative-Ratios | stat.ML | Modes and ridges of the probability density function behind observed data are
useful geometric features. Mode-seeking clustering assigns cluster labels by
associating data samples with the nearest modes, and estimation of density
ridges enables us to find lower-dimensional structures hidden in data. A key
technical cha... | computer science |
22,800 | Exhaustive search for sparse variable selection in linear regression | stat.ML | We propose a K-sparse exhaustive search (ES-K) method and a K-sparse
approximate exhaustive search method (AES-K) for selecting variables in linear
regression. With these methods, K-sparse combinations of variables are tested
exhaustively assuming that the optimal combination of explanatory variables is
K-sparse. By co... | computer science |
22,801 | Subspace Clustering with Missing and Corrupted Data | stat.ML | Given full or partial information about a collection of points that lie close
to a union of several subspaces, subspace clustering refers to the process of
clustering the points according to their subspace and identifying the
subspaces. One popular approach, sparse subspace clustering (SSC), represents
each sample as a... | computer science |
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