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10,801 | Hellinger Distance Trees for Imbalanced Streams | cs.LG | Classifiers trained on data sets possessing an imbalanced class distribution
are known to exhibit poor generalisation performance. This is known as the
imbalanced learning problem. The problem becomes particularly acute when we
consider incremental classifiers operating on imbalanced data streams,
especially when the l... | computer science |
10,802 | A Hybrid Monte Carlo Architecture for Parameter Optimization | stat.ML | Much recent research has been conducted in the area of Bayesian learning,
particularly with regard to the optimization of hyper-parameters via Gaussian
process regression. The methodologies rely chiefly on the method of maximizing
the expected improvement of a score function with respect to adjustments in the
hyper-par... | computer science |
10,803 | Sharp Finite-Time Iterated-Logarithm Martingale Concentration | math.PR | We give concentration bounds for martingales that are uniform over finite
times and extend classical Hoeffding and Bernstein inequalities. We also
demonstrate our concentration bounds to be optimal with a matching
anti-concentration inequality, proved using the same method. Together these
constitute a finite-time versi... | computer science |
10,804 | Policy Gradients for CVaR-Constrained MDPs | stat.ML | We study a risk-constrained version of the stochastic shortest path (SSP)
problem, where the risk measure considered is Conditional Value-at-Risk (CVaR).
We propose two algorithms that obtain a locally risk-optimal policy by
employing four tools: stochastic approximation, mini batches, policy gradients
and importance s... | computer science |
10,805 | Accelerating Minibatch Stochastic Gradient Descent using Stratified
Sampling | stat.ML | Stochastic Gradient Descent (SGD) is a popular optimization method which has
been applied to many important machine learning tasks such as Support Vector
Machines and Deep Neural Networks. In order to parallelize SGD, minibatch
training is often employed. The standard approach is to uniformly sample a
minibatch at each... | computer science |
10,806 | Efficient Implementations of the Generalized Lasso Dual Path Algorithm | stat.CO | We consider efficient implementations of the generalized lasso dual path
algorithm of Tibshirani and Taylor (2011). We first describe a generic approach
that covers any penalty matrix D and any (full column rank) matrix X of
predictor variables. We then describe fast implementations for the special
cases of trend filte... | computer science |
10,807 | On the Complexity of A/B Testing | math.ST | A/B testing refers to the task of determining the best option among two
alternatives that yield random outcomes. We provide distribution-dependent
lower bounds for the performance of A/B testing that improve over the results
currently available both in the fixed-confidence (or delta-PAC) and
fixed-budget settings. When... | computer science |
10,808 | Effects of Sampling Methods on Prediction Quality. The Case of
Classifying Land Cover Using Decision Trees | stat.ML | Clever sampling methods can be used to improve the handling of big data and
increase its usefulness. The subject of this study is remote sensing,
specifically airborne laser scanning point clouds representing different
classes of ground cover. The aim is to derive a supervised learning model for
the classification usin... | computer science |
10,809 | Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with
Non-stationary Rewards | cs.LG | In a multi-armed bandit (MAB) problem a gambler needs to choose at each round
of play one of K arms, each characterized by an unknown reward distribution.
Reward realizations are only observed when an arm is selected, and the
gambler's objective is to maximize his cumulative expected earnings over some
given horizon of... | computer science |
10,810 | Methods and Models for Interpretable Linear Classification | stat.ME | We present an integer programming framework to build accurate and
interpretable discrete linear classification models. Unlike existing
approaches, our framework is designed to provide practitioners with the control
and flexibility they need to tailor accurate and interpretable models for a
domain of choice. To this end... | computer science |
10,811 | Identification of functionally related enzymes by learning-to-rank
methods | cs.LG | Enzyme sequences and structures are routinely used in the biological sciences
as queries to search for functionally related enzymes in online databases. To
this end, one usually departs from some notion of similarity, comparing two
enzymes by looking for correspondences in their sequences, structures or
surfaces. For a... | computer science |
10,812 | Compressive Sampling Using EM Algorithm | stat.ME | Conventional approaches of sampling signals follow the celebrated theorem of
Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and
Tao, is a new paradigm that goes against the conventional methods in data
acquisition and provides a way of recovering signals using fewer samples than
the traditiona... | computer science |
10,813 | Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search
(MIPS) | stat.ML | We present the first provably sublinear time algorithm for approximate
\emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first
hashing algorithm for searching with (un-normalized) inner product as the
underlying similarity measure. Finding hashing schemes for MIPS was considered
hard. We formally sho... | computer science |
10,814 | LASS: a simple assignment model with Laplacian smoothing | cs.LG | We consider the problem of learning soft assignments of $N$ items to $K$
categories given two sources of information: an item-category similarity
matrix, which encourages items to be assigned to categories they are similar to
(and to not be assigned to categories they are dissimilar to), and an item-item
similarity mat... | computer science |
10,815 | The role of dimensionality reduction in linear classification | cs.LG | Dimensionality reduction (DR) is often used as a preprocessing step in
classification, but usually one first fixes the DR mapping, possibly using
label information, and then learns a classifier (a filter approach). Best
performance would be obtained by optimizing the classification error jointly
over DR mapping and cla... | computer science |
10,816 | Differentially Private Empirical Risk Minimization: Efficient Algorithms
and Tight Error Bounds | cs.LG | In this paper, we initiate a systematic investigation of differentially
private algorithms for convex empirical risk minimization. Various
instantiations of this problem have been studied before. We provide new
algorithms and matching lower bounds for private ERM assuming only that each
data point's contribution to the... | computer science |
10,817 | Learning From Ordered Sets and Applications in Collaborative Ranking | cs.LG | Ranking over sets arise when users choose between groups of items. For
example, a group may be of those movies deemed $5$ stars to them, or a
customized tour package. It turns out, to model this data type properly, we
need to investigate the general combinatorics problem of partitioning a set and
ordering the subsets. ... | computer science |
10,818 | Thurstonian Boltzmann Machines: Learning from Multiple Inequalities | stat.ML | We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture
that can naturally incorporate a wide range of data inputs at the same time.
Our motivation rests in the Thurstonian view that many discrete data types can
be considered as being generated from a subset of underlying latent continuous
variables, ... | computer science |
10,819 | Conditional Restricted Boltzmann Machines for Cold Start Recommendations | cs.IR | Restricted Boltzman Machines (RBMs) have been successfully used in
recommender systems. However, as with most of other collaborative filtering
techniques, it cannot solve cold start problems for there is no rating for a
new item. In this paper, we first apply conditional RBM (CRBM) which could take
extra information in... | computer science |
10,820 | Sample Complexity Analysis for Learning Overcomplete Latent Variable
Models through Tensor Methods | cs.LG | We provide guarantees for learning latent variable models emphasizing on the
overcomplete regime, where the dimensionality of the latent space can exceed
the observed dimensionality. In particular, we consider multiview mixtures,
spherical Gaussian mixtures, ICA, and sparse coding models. We provide tight
concentration... | computer science |
10,821 | Estimating Maximally Probable Constrained Relations by Mathematical
Programming | cs.LG | Estimating a constrained relation is a fundamental problem in machine
learning. Special cases are classification (the problem of estimating a map
from a set of to-be-classified elements to a set of labels), clustering (the
problem of estimating an equivalence relation on a set) and ranking (the
problem of estimating a ... | computer science |
10,822 | Mixed-Variate Restricted Boltzmann Machines | stat.ML | Modern datasets are becoming heterogeneous. To this end, we present in this
paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling
variables of multiple types and modalities, including binary and continuous
responses, categorical options, multicategorical choices, ordinal assessment
and category... | computer science |
10,823 | MCMC for Hierarchical Semi-Markov Conditional Random Fields | stat.ML | Deep architecture such as hierarchical semi-Markov models is an important
class of models for nested sequential data. Current exact inference schemes
either cost cubic time in sequence length, or exponential time in model depth.
These costs are prohibitive for large-scale problems with arbitrary length and
depth. In th... | computer science |
10,824 | Non-Convex Rank Minimization via an Empirical Bayesian Approach | cs.LG | In many applications that require matrix solutions of minimal rank, the
underlying cost function is non-convex leading to an intractable, NP-hard
optimization problem. Consequently, the convex nuclear norm is frequently used
as a surrogate penalty term for matrix rank. The problem is that in many
practical scenarios th... | computer science |
10,825 | Guess Who Rated This Movie: Identifying Users Through Subspace
Clustering | cs.LG | It is often the case that, within an online recommender system, multiple
users share a common account. Can such shared accounts be identified solely on
the basis of the userprovided ratings? Once a shared account is identified, can
the different users sharing it be identified as well? Whenever such user
identification ... | computer science |
10,826 | Parallel Gaussian Process Regression with Low-Rank Covariance Matrix
Approximations | cs.LG | Gaussian processes (GP) are Bayesian non-parametric models that are widely
used for probabilistic regression. Unfortunately, it cannot scale well with
large data nor perform real-time predictions due to its cubic time cost in the
data size. This paper presents two parallel GP regression methods that exploit
low-rank co... | computer science |
10,827 | Statistical guarantees for the EM algorithm: From population to
sample-based analysis | math.ST | We develop a general framework for proving rigorous guarantees on the
performance of the EM algorithm and a variant known as gradient EM. Our
analysis is divided into two parts: a treatment of these algorithms at the
population level (in the limit of infinite data), followed by results that
apply to updates based on a ... | computer science |
10,828 | Optimum Statistical Estimation with Strategic Data Sources | stat.ML | We propose an optimum mechanism for providing monetary incentives to the data
sources of a statistical estimator such as linear regression, so that high
quality data is provided at low cost, in the sense that the sum of payments and
estimation error is minimized. The mechanism applies to a broad range of
estimators, in... | computer science |
10,829 | Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction
of Tumors | cs.LG | Statistical machine learning methods, especially nonparametric Bayesian
methods, have become increasingly popular to infer clonal population structure
of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant
process (CRP), a popular construction used in nonparametric mixture models, to
infer the ... | computer science |
10,830 | On Data Preconditioning for Regularized Loss Minimization | cs.NA | In this work, we study data preconditioning, a well-known and long-existing
technique, for boosting the convergence of first-order methods for regularized
loss minimization. It is well understood that the condition number of the
problem, i.e., the ratio of the Lipschitz constant to the strong convexity
modulus, has a h... | computer science |
10,831 | Robust Statistical Ranking: Theory and Algorithms | stat.ME | Deeply rooted in classical social choice and voting theory, statistical
ranking with paired comparison data experienced its renaissance with the wide
spread of crowdsourcing technique. As the data quality might be significantly
damaged in an uncontrolled crowdsourcing environment, outlier detection and
robust ranking h... | computer science |
10,832 | A new integral loss function for Bayesian optimization | stat.CO | We consider the problem of maximizing a real-valued continuous function $f$
using a Bayesian approach. Since the early work of Jonas Mockus and Antanas
\v{Z}ilinskas in the 70's, the problem of optimization is usually formulated by
considering the loss function $\max f - M_n$ (where $M_n$ denotes the best
function valu... | computer science |
10,833 | Diffusion Fingerprints | stat.ML | We introduce, test and discuss a method for classifying and clustering data
modeled as directed graphs. The idea is to start diffusion processes from any
subset of a data collection, generating corresponding distributions for
reaching points in the network. These distributions take the form of
high-dimensional numerica... | computer science |
10,834 | Uniform Sampling for Matrix Approximation | cs.DS | Random sampling has become a critical tool in solving massive matrix
problems. For linear regression, a small, manageable set of data rows can be
randomly selected to approximate a tall, skinny data matrix, improving
processing time significantly. For theoretical performance guarantees, each row
must be sampled with pr... | computer science |
10,835 | An application of topological graph clustering to protein function
prediction | cs.CE | We use a semisupervised learning algorithm based on a topological data
analysis approach to assign functional categories to yeast proteins using
similarity graphs. This new approach to analyzing biological networks yields
results that are as good as or better than state of the art existing
approaches. | computer science |
10,836 | Inference of Cancer Progression Models with Biological Noise | stat.ML | Many applications in translational medicine require the understanding of how
diseases progress through the accumulation of persistent events. Specialized
Bayesian networks called monotonic progression networks offer a statistical
framework for modeling this sort of phenomenon. Current machine learning tools
to reconstr... | computer science |
10,837 | Falsifiable implies Learnable | cs.LG | The paper demonstrates that falsifiability is fundamental to learning. We
prove the following theorem for statistical learning and sequential prediction:
If a theory is falsifiable then it is learnable -- i.e. admits a strategy that
predicts optimally. An analogous result is shown for universal induction. | computer science |
10,838 | Deep learning of fMRI big data: a novel approach to subject-transfer
decoding | stat.ML | As a technology to read brain states from measurable brain activities, brain
decoding are widely applied in industries and medical sciences. In spite of
high demands in these applications for a universal decoder that can be applied
to all individuals simultaneously, large variation in brain activities across
individual... | computer science |
10,839 | Advanced Mean Field Theory of Restricted Boltzmann Machine | cs.LG | Learning in restricted Boltzmann machine is typically hard due to the
computation of gradients of log-likelihood function. To describe the network
state statistics of the restricted Boltzmann machine, we develop an advanced
mean field theory based on the Bethe approximation. Our theory provides an
efficient message pas... | computer science |
10,840 | Unsupervised Incremental Learning and Prediction of Music Signals | cs.SD | A system is presented that segments, clusters and predicts musical audio in
an unsupervised manner, adjusting the number of (timbre) clusters
instantaneously to the audio input. A sequence learning algorithm adapts its
structure to a dynamically changing clustering tree. The flow of the system is
as follows: 1) segment... | computer science |
10,841 | A mixture Cox-Logistic model for feature selection from survival and
classification data | stat.ML | This paper presents an original approach for jointly fitting survival times
and classifying samples into subgroups. The Coxlogit model is a generalized
linear model with a common set of selected features for both tasks. Survival
times and class labels are here assumed to be conditioned by a common risk
score which depe... | computer science |
10,842 | A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM
Classification | stat.ML | Frank-Wolfe algorithms have recently regained the attention of the Machine
Learning community. Their solid theoretical properties and sparsity guarantees
make them a suitable choice for a wide range of problems in this field. In
addition, several variants of the basic procedure exist that improve its
theoretical proper... | computer science |
10,843 | From Pixels to Torques: Policy Learning with Deep Dynamical Models | stat.ML | Data-efficient learning in continuous state-action spaces using very
high-dimensional observations remains a key challenge in developing fully
autonomous systems. In this paper, we consider one instance of this challenge,
the pixels to torques problem, where an agent must learn a closed-loop control
policy from pixel i... | computer science |
10,844 | Probabilistic Line Searches for Stochastic Optimization | cs.LG | In deterministic optimization, line searches are a standard tool ensuring
stability and efficiency. Where only stochastic gradients are available, no
direct equivalent has so far been formulated, because uncertain gradients do
not allow for a strict sequence of decisions collapsing the search space. We
construct a prob... | computer science |
10,845 | Gaussian Processes for Data-Efficient Learning in Robotics and Control | stat.ML | Autonomous learning has been a promising direction in control and robotics
for more than a decade since data-driven learning allows to reduce the amount
of engineering knowledge, which is otherwise required. However, autonomous
reinforcement learning (RL) approaches typically require many interactions with
the system t... | computer science |
10,846 | Gaussian Process Models for HRTF based Sound-Source Localization and
Active-Learning | cs.SD | From a machine learning perspective, the human ability localize sounds can be
modeled as a non-parametric and non-linear regression problem between binaural
spectral features of sound received at the ears (input) and their sound-source
directions (output). The input features can be summarized in terms of the
individual... | computer science |
10,847 | Proximal Algorithms in Statistics and Machine Learning | stat.ML | In this paper we develop proximal methods for statistical learning. Proximal
point algorithms are useful in statistics and machine learning for obtaining
optimization solutions for composite functions. Our approach exploits
closed-form solutions of proximal operators and envelope representations based
on the Moreau, Fo... | computer science |
10,848 | Combinatorial Bandits Revisited | cs.LG | This paper investigates stochastic and adversarial combinatorial multi-armed
bandit problems. In the stochastic setting under semi-bandit feedback, we
derive a problem-specific regret lower bound, and discuss its scaling with the
dimension of the decision space. We propose ESCB, an algorithm that efficiently
exploits t... | computer science |
10,849 | Particle Gibbs for Bayesian Additive Regression Trees | stat.ML | Additive regression trees are flexible non-parametric models and popular
off-the-shelf tools for real-world non-linear regression. In application
domains, such as bioinformatics, where there is also demand for probabilistic
predictions with measures of uncertainty, the Bayesian additive regression
trees (BART) model, i... | computer science |
10,850 | Parameter estimation in softmax decision-making models with linear
objective functions | math.OC | With an eye towards human-centered automation, we contribute to the
development of a systematic means to infer features of human decision-making
from behavioral data. Motivated by the common use of softmax selection in
models of human decision-making, we study the maximum likelihood parameter
estimation problem for sof... | computer science |
10,851 | Exact tensor completion using t-SVD | cs.LG | In this paper we focus on the problem of completion of multidimensional
arrays (also referred to as tensors) from limited sampling. Our approach is
based on a recently proposed tensor-Singular Value Decomposition (t-SVD) [1].
Using this factorization one can derive notion of tensor rank, referred to as
the tensor tubal... | computer science |
10,852 | MILJS : Brand New JavaScript Libraries for Matrix Calculation and
Machine Learning | stat.ML | MILJS is a collection of state-of-the-art, platform-independent, scalable,
fast JavaScript libraries for matrix calculation and machine learning. Our core
library offering a matrix calculation is called Sushi, which exhibits far
better performance than any other leading machine learning libraries written in
JavaScript.... | computer science |
10,853 | Learning with Square Loss: Localization through Offset Rademacher
Complexity | stat.ML | We consider regression with square loss and general classes of functions
without the boundedness assumption. We introduce a notion of offset Rademacher
complexity that provides a transparent way to study localization both in
expectation and in high probability. For any (possibly non-convex) class, the
excess loss of a ... | computer science |
10,854 | Learning with Differential Privacy: Stability, Learnability and the
Sufficiency and Necessity of ERM Principle | stat.ML | While machine learning has proven to be a powerful data-driven solution to
many real-life problems, its use in sensitive domains has been limited due to
privacy concerns. A popular approach known as **differential privacy** offers
provable privacy guarantees, but it is often observed in practice that it could
substanti... | computer science |
10,855 | On The Identifiability of Mixture Models from Grouped Samples | stat.ML | Finite mixture models are statistical models which appear in many problems in
statistics and machine learning. In such models it is assumed that data are
drawn from random probability measures, called mixture components, which are
themselves drawn from a probability measure P over probability measures. When
estimating ... | computer science |
10,856 | On the Equivalence between Kernel Quadrature Rules and Random Feature
Expansions | cs.LG | We show that kernel-based quadrature rules for computing integrals can be
seen as a special case of random feature expansions for positive definite
kernels, for a particular decomposition that always exists for such kernels. We
provide a theoretical analysis of the number of required samples for a given
approximation e... | computer science |
10,857 | On the consistency theory of high dimensional variable screening | math.ST | Variable screening is a fast dimension reduction technique for assisting high
dimensional feature selection. As a preselection method, it selects a moderate
size subset of candidate variables for further refining via feature selection
to produce the final model. The performance of variable screening depends on
both com... | computer science |
10,858 | Stochastic Dual Coordinate Ascent with Adaptive Probabilities | math.OC | This paper introduces AdaSDCA: an adaptive variant of stochastic dual
coordinate ascent (SDCA) for solving the regularized empirical risk
minimization problems. Our modification consists in allowing the method
adaptively change the probability distribution over the dual variables
throughout the iterative process. AdaSD... | computer science |
10,859 | Influence Maximization with Bandits | cs.SI | We consider the problem of \emph{influence maximization}, the problem of
maximizing the number of people that become aware of a product by finding the
`best' set of `seed' users to expose the product to. Most prior work on this
topic assumes that we know the probability of each user influencing each other
user, or we h... | computer science |
10,860 | Contrastive Pessimistic Likelihood Estimation for Semi-Supervised
Classification | stat.ML | Improvement guarantees for semi-supervised classifiers can currently only be
given under restrictive conditions on the data. We propose a general way to
perform semi-supervised parameter estimation for likelihood-based classifiers
for which, on the full training set, the estimates are never worse than the
supervised so... | computer science |
10,861 | Recovering PCA from Hybrid-$(\ell_1,\ell_2)$ Sparse Sampling of Data
Elements | cs.IT | This paper addresses how well we can recover a data matrix when only given a
few of its elements. We present a randomized algorithm that element-wise
sparsifies the data, retaining only a few its elements. Our new algorithm
independently samples the data using sampling probabilities that depend on both
the squares ($\e... | computer science |
10,862 | Hierarchies of Relaxations for Online Prediction Problems with Evolving
Constraints | cs.LG | We study online prediction where regret of the algorithm is measured against
a benchmark defined via evolving constraints. This framework captures online
prediction on graphs, as well as other prediction problems with combinatorial
structure. A key aspect here is that finding the optimal benchmark predictor
(even in hi... | computer science |
10,863 | Class Probability Estimation via Differential Geometric Regularization | cs.LG | We study the problem of supervised learning for both binary and multiclass
classification from a unified geometric perspective. In particular, we propose
a geometric regularization technique to find the submanifold corresponding to a
robust estimator of the class probability $P(y|\pmb{x})$. The regularization
term meas... | computer science |
10,864 | Min-Max Kernels | stat.ML | The min-max kernel is a generalization of the popular resemblance kernel
(which is designed for binary data). In this paper, we demonstrate, through an
extensive classification study using kernel machines, that the min-max kernel
often provides an effective measure of similarity for nonnegative data. As the
min-max ker... | computer science |
10,865 | Hamiltonian ABC | stat.ML | Approximate Bayesian computation (ABC) is a powerful and elegant framework
for performing inference in simulation-based models. However, due to the
difficulty in scaling likelihood estimates, ABC remains useful for relatively
low-dimensional problems. We introduce Hamiltonian ABC (HABC), a set of
likelihood-free algori... | computer science |
10,866 | Escaping From Saddle Points --- Online Stochastic Gradient for Tensor
Decomposition | cs.LG | We analyze stochastic gradient descent for optimizing non-convex functions.
In many cases for non-convex functions the goal is to find a reasonable local
minimum, and the main concern is that gradient updates are trapped in saddle
points. In this paper we identify strict saddle property for non-convex problem
that allo... | computer science |
10,867 | Higher order Matching Pursuit for Low Rank Tensor Learning | stat.ML | Low rank tensor learning, such as tensor completion and multilinear multitask
learning, has received much attention in recent years. In this paper, we
propose higher order matching pursuit for low rank tensor learning problems
with a convex or a nonconvex cost function, which is a generalization of the
matching pursuit... | computer science |
10,868 | A Characterization of Deterministic Sampling Patterns for Low-Rank
Matrix Completion | stat.ML | Low-rank matrix completion (LRMC) problems arise in a wide variety of
applications. Previous theory mainly provides conditions for completion under
missing-at-random samplings. This paper studies deterministic conditions for
completion. An incomplete $d \times N$ matrix is finitely rank-$r$ completable
if there are at ... | computer science |
10,869 | L_1-regularized Boltzmann machine learning using majorizer minimization | stat.ML | We propose an inference method to estimate sparse interactions and biases
according to Boltzmann machine learning. The basis of this method is $L_1$
regularization, which is often used in compressed sensing, a technique for
reconstructing sparse input signals from undersampled outputs. $L_1$
regularization impedes the ... | computer science |
10,870 | Automatic Unsupervised Tensor Mining with Quality Assessment | stat.ML | A popular tool for unsupervised modelling and mining multi-aspect data is
tensor decomposition. In an exploratory setting, where and no labels or ground
truth are available how can we automatically decide how many components to
extract? How can we assess the quality of our results, so that a domain expert
can factor th... | computer science |
10,871 | Switching to Learn | cs.LG | A network of agents attempt to learn some unknown state of the world drawn by
nature from a finite set. Agents observe private signals conditioned on the
true state, and form beliefs about the unknown state accordingly. Each agent
may face an identification problem in the sense that she cannot distinguish the
truth in ... | computer science |
10,872 | Deep Unsupervised Learning using Nonequilibrium Thermodynamics | cs.LG | A central problem in machine learning involves modeling complex data-sets
using highly flexible families of probability distributions in which learning,
sampling, inference, and evaluation are still analytically or computationally
tractable. Here, we develop an approach that simultaneously achieves both
flexibility and... | computer science |
10,873 | Hierarchical learning of grids of microtopics | stat.ML | The counting grid is a grid of microtopics, sparse word/feature
distributions. The generative model associated with the grid does not use these
microtopics individually. Rather, it groups them in overlapping rectangular
windows and uses these grouped microtopics as either mixture or admixture
components. This paper bui... | computer science |
10,874 | Learning Mixed Membership Community Models in Social Tagging Networks
through Tensor Methods | cs.LG | Community detection in graphs has been extensively studied both in theory and
in applications. However, detecting communities in hypergraphs is more
challenging. In this paper, we propose a tensor decomposition approach for
guaranteed learning of communities in a special class of hypergraphs modeling
social tagging sys... | computer science |
10,875 | Relaxed Leverage Sampling for Low-rank Matrix Completion | cs.IT | We consider the problem of exact recovery of any $m\times n$ matrix of rank
$\varrho$ from a small number of observed entries via the standard nuclear norm
minimization framework. Such low-rank matrices have degrees of freedom
$(m+n)\varrho - \varrho^2$. We show that any arbitrary low-rank matrices can be
recovered exa... | computer science |
10,876 | On some provably correct cases of variational inference for topic models | cs.LG | Variational inference is a very efficient and popular heuristic used in
various forms in the context of latent variable models. It's closely related to
Expectation Maximization (EM), and is applied when exact EM is computationally
infeasible. Despite being immensely popular, current theoretical understanding
of the eff... | computer science |
10,877 | Sparse Linear Regression With Missing Data | stat.ML | This paper proposes a fast and accurate method for sparse regression in the
presence of missing data. The underlying statistical model encapsulates the
low-dimensional structure of the incomplete data matrix and the sparsity of the
regression coefficients, and the proposed algorithm jointly learns the
low-dimensional s... | computer science |
10,878 | Infinite Author Topic Model based on Mixed Gamma-Negative Binomial
Process | stat.ML | Incorporating the side information of text corpus, i.e., authors, time
stamps, and emotional tags, into the traditional text mining models has gained
significant interests in the area of information retrieval, statistical natural
language processing, and machine learning. One branch of these works is the
so-called Auth... | computer science |
10,879 | A New Method for Classification of Datasets for Data Mining | cs.LG | Decision tree is an important method for both induction research and data
mining, which is mainly used for model classification and prediction. ID3
algorithm is the most widely used algorithm in the decision tree so far. In
this paper, the shortcoming of ID3's inclining to choose attributes with many
values is discusse... | computer science |
10,880 | Learning molecular energies using localized graph kernels | cs.LG | Recent machine learning methods make it possible to model potential energy of
atomic configurations with chemical-level accuracy (as calculated from
ab-initio calculations) and at speeds suitable for molecular dynam- ics
simulation. Best performance is achieved when the known physical constraints
are encoded in the mac... | computer science |
10,881 | Diet2Vec: Multi-scale analysis of massive dietary data | stat.ML | Smart phone apps that enable users to easily track their diets have become
widespread in the last decade. This has created an opportunity to discover new
insights into obesity and weight loss by analyzing the eating habits of the
users of such apps. In this paper, we present diet2vec: an approach to modeling
latent str... | computer science |
10,882 | A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction | cs.LG | Accurately predicting drug responses to cancer is an important problem
hindering oncologists' efforts to find the most effective drugs to treat
cancer, which is a core goal in precision medicine. The scientific community
has focused on improving this prediction based on genomic, epigenomic, and
proteomic datasets measu... | computer science |
10,883 | Development of a hybrid learning system based on SVM, ANFIS and domain
knowledge: DKFIS | cs.LG | This paper presents the development of a hybrid learning system based on
Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS)
and domain knowledge to solve prediction problem. The proposed two-stage Domain
Knowledge based Fuzzy Information System (DKFIS) improves the prediction
accuracy attained... | computer science |
10,884 | Restricted Strong Convexity Implies Weak Submodularity | stat.ML | We connect high-dimensional subset selection and submodular maximization. Our
results extend the work of Das and Kempe (2011) from the setting of linear
regression to arbitrary objective functions. For greedy feature selection, this
connection allows us to obtain strong multiplicative performance bounds on
several meth... | computer science |
10,885 | A novel multiclassSVM based framework to classify lithology from well
logs: a real-world application | cs.LG | Support vector machines (SVMs) have been recognized as a potential tool for
supervised classification analyses in different domains of research. In
essence, SVM is a binary classifier. Therefore, in case of a multiclass
problem, the problem is divided into a series of binary problems which are
solved by binary classifi... | computer science |
10,886 | Estimating latent feature-feature interactions in large feature-rich
graphs | cs.SI | Real-world complex networks describe connections between objects; in reality,
those objects are often endowed with some kind of features. How does the
presence or absence of such features interplay with the network link structure?
Although the situation here described is truly ubiquitous, there is a limited
body of res... | computer science |
10,887 | Large scale modeling of antimicrobial resistance with interpretable
classifiers | cs.LG | Antimicrobial resistance is an important public health concern that has
implications in the practice of medicine worldwide. Accurately predicting
resistance phenotypes from genome sequences shows great promise in promoting
better use of antimicrobial agents, by determining which antibiotics are likely
to be effective i... | computer science |
10,888 | Modeling trajectories of mental health: challenges and opportunities | stat.ML | More than two thirds of mental health problems have their onset during
childhood or adolescence. Identifying children at risk for mental illness later
in life and predicting the type of illness is not easy. We set out to develop a
platform to define subtypes of childhood social-emotional development using
longitudinal,... | computer science |
10,889 | Robust nonparametric nearest neighbor random process clustering | cs.LG | We consider the problem of clustering noisy finite-length observations of
stationary ergodic random processes according to their generative models
without prior knowledge of the model statistics and the number of generative
models. Two algorithms, both using the $L^1$-distance between estimated power
spectral densities... | computer science |
10,890 | Ranking Biomarkers Through Mutual Information | stat.ML | We study information theoretic methods for ranking biomarkers. In clinical
trials there are two, closely related, types of biomarkers: predictive and
prognostic, and disentangling them is a key challenge. Our first step is to
phrase biomarker ranking in terms of optimizing an information theoretic
quantity. This formal... | computer science |
10,891 | A One class Classifier based Framework using SVDD : Application to an
Imbalanced Geological Dataset | cs.LG | Evaluation of hydrocarbon reservoir requires classification of petrophysical
properties from available dataset. However, characterization of reservoir
attributes is difficult due to the nonlinear and heterogeneous nature of the
subsurface physical properties. In this context, present study proposes a
generalized one cl... | computer science |
10,892 | Segmental Convolutional Neural Networks for Detection of Cardiac
Abnormality With Noisy Heart Sound Recordings | cs.SD | Heart diseases constitute a global health burden, and the problem is
exacerbated by the error-prone nature of listening to and interpreting heart
sounds. This motivates the development of automated classification to screen
for abnormal heart sounds. Existing machine learning-based systems achieve
accurate classificatio... | computer science |
10,893 | Statistical and Computational Guarantees of Lloyd's Algorithm and its
Variants | math.ST | Clustering is a fundamental problem in statistics and machine learning.
Lloyd's algorithm, proposed in 1957, is still possibly the most widely used
clustering algorithm in practice due to its simplicity and empirical
performance. However, there has been little theoretical investigation on the
statistical and computatio... | computer science |
10,894 | Robust Low-Complexity Randomized Methods for Locating Outliers in Large
Matrices | cs.IT | This paper examines the problem of locating outlier columns in a large,
otherwise low-rank matrix, in settings where {}{the data} are noisy, or where
the overall matrix has missing elements. We propose a randomized two-step
inference framework, and establish sufficient conditions on the required sample
complexities und... | computer science |
10,895 | Evaluating the Performance of ANN Prediction System at Shanghai Stock
Market in the Period 21-Sep-2016 to 11-Oct-2016 | cs.LG | This research evaluates the performance of an Artificial Neural Network based
prediction system that was employed on the Shanghai Stock Exchange for the
period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in
which the prices were predicted and published before September 21. Stock market
price predi... | computer science |
10,896 | A note on the triangle inequality for the Jaccard distance | cs.DM | Two simple proofs of the triangle inequality for the Jaccard distance in
terms of nonnegative, monotone, submodular functions are given and discussed. | computer science |
10,897 | CrowdMI: Multiple Imputation via Crowdsourcing | cs.LG | Can humans impute missing data with similar proficiency as machines? This is
the question we aim to answer in this paper. We present a novel idea of
converting observations with missing data in to a survey questionnaire, which
is presented to crowdworkers for completion. We replicate a multiple imputation
framework by ... | computer science |
10,898 | Scalable Influence Maximization for Multiple Products in Continuous-Time
Diffusion Networks | cs.SI | A typical viral marketing model identifies influential users in a social
network to maximize a single product adoption assuming unlimited user
attention, campaign budgets, and time. In reality, multiple products need
campaigns, users have limited attention, convincing users incurs costs, and
advertisers have limited bu... | computer science |
10,899 | Protein-Ligand Scoring with Convolutional Neural Networks | stat.ML | Computational approaches to drug discovery can reduce the time and cost
associated with experimental assays and enable the screening of novel
chemotypes. Structure-based drug design methods rely on scoring functions to
rank and predict binding affinities and poses. The ever-expanding amount of
protein-ligand binding an... | computer science |
10,900 | The Physical Systems Behind Optimization Algorithms | cs.LG | We use differential equations based approaches to provide some {\it
\textbf{physics}} insights into analyzing the dynamics of popular optimization
algorithms in machine learning. In particular, we study gradient descent,
proximal gradient descent, coordinate gradient descent, proximal coordinate
gradient, and Newton's ... | computer science |
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