Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
10,901 | BaTFLED: Bayesian Tensor Factorization Linked to External Data | stat.ML | The vast majority of current machine learning algorithms are designed to
predict single responses or a vector of responses, yet many types of response
are more naturally organized as matrices or higher-order tensor objects where
characteristics are shared across modes. We present a new machine learning
algorithm BaTFLE... | computer science |
10,902 | Phase transitions in Restricted Boltzmann Machines with generic priors | cs.LG | We study Generalised Restricted Boltzmann Machines with generic priors for
units and weights, interpolating between Boolean and Gaussian variables. We
present a complete analysis of the replica symmetric phase diagram of these
systems, which can be regarded as Generalised Hopfield models. We underline the
role of the r... | computer science |
10,903 | Low-Rank Inducing Norms with Optimality Interpretations | math.OC | Optimization problems with rank constraints appear in many diverse fields
such as control, machine learning and image analysis. Since the rank constraint
is non-convex, these problems are often approximately solved via convex
relaxations. Nuclear norm regularization is the prevailing convexifying
technique for dealing ... | computer science |
10,904 | Optimal Generalized Decision Trees via Integer Programming | cs.LG | Decision trees have been a very popular class of predictive models for
decades due to their interpretability and good performance on categorical
features. However, they are not always robust and tend to overfit the data.
Additionally, if allowed to grow large, they lose interpretability. In this
paper, we present a nov... | computer science |
10,905 | Non-negative Factorization of the Occurrence Tensor from Financial
Contracts | cs.CE | We propose an algorithm for the non-negative factorization of an occurrence
tensor built from heterogeneous networks. We use l0 norm to model sparse errors
over discrete values (occurrences), and use decomposed factors to model the
embedded groups of nodes. An efficient splitting method is developed to
optimize the non... | computer science |
10,906 | Noisy subspace clustering via matching pursuits | cs.LG | Sparsity-based subspace clustering algorithms have attracted significant
attention thanks to their excellent performance in practical applications. A
prominent example is the sparse subspace clustering (SSC) algorithm by
Elhamifar and Vidal, which performs spectral clustering based on an adjacency
matrix obtained by sp... | computer science |
10,907 | Upper Bound of Bayesian Generalization Error in Non-negative Matrix
Factorization | math.ST | Non-negative matrix factorization (NMF) is a new knowledge discovery method
that is used for text mining, signal processing, bioinformatics, and consumer
analysis. However, its basic property as a learning machine is not yet
clarified, as it is not a regular statistical model, resulting that theoretical
optimization me... | computer science |
10,908 | End-to-End Deep Reinforcement Learning for Lane Keeping Assist | stat.ML | Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes, but it has not yet been successfully used for automotive
applications. There has recently been a revival of interest in the topic,
however, drive... | computer science |
10,909 | Uncovering the Dynamics of Crowdlearning and the Value of Knowledge | cs.SI | Learning from the crowd has become increasingly popular in the Web and social
media. There is a wide variety of crowdlearning sites in which, on the one
hand, users learn from the knowledge that other users contribute to the site,
and, on the other hand, knowledge is reviewed and curated by the same users
using assessm... | computer science |
10,910 | Efficient Distributed Semi-Supervised Learning using Stochastic
Regularization over Affinity Graphs | stat.ML | We describe a computationally efficient, stochastic graph-regularization
technique that can be utilized for the semi-supervised training of deep neural
networks in a parallel or distributed setting. We utilize a technique, first
described in [13] for the construction of mini-batches for stochastic gradient
descent (SGD... | computer science |
10,911 | Graph-based semi-supervised learning for relational networks | cs.SI | We address the problem of semi-supervised learning in relational networks,
networks in which nodes are entities and links are the relationships or
interactions between them. Typically this problem is confounded with the
problem of graph-based semi-supervised learning (GSSL), because both problems
represent the data as ... | computer science |
10,912 | Optimal structure and parameter learning of Ising models | cs.LG | Reconstruction of structure and parameters of an Ising model from binary
samples is a problem of practical importance in a variety of disciplines,
ranging from statistical physics and computational biology to image processing
and machine learning. The focus of the research community shifted towards
developing universal... | computer science |
10,913 | Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme
under Weak Strong Convexity Assumption | cs.LG | We propose a projected semi-stochastic gradient descent method with
mini-batch for improving both the theoretical complexity and practical
performance of the general stochastic gradient descent method (SGD). We are
able to prove linear convergence under weak strong convexity assumption. This
requires no strong convexit... | computer science |
10,914 | Mutual information for fitting deep nonlinear models | math.OC | Deep nonlinear models pose a challenge for fitting parameters due to lack of
knowledge of the hidden layer and the potentially non-affine relation of the
initial and observed layers. In the present work we investigate the use of
information theoretic measures such as mutual information and Kullback-Leibler
(KL) diverge... | computer science |
10,915 | Simple Black-Box Adversarial Perturbations for Deep Networks | cs.LG | Deep neural networks are powerful and popular learning models that achieve
state-of-the-art pattern recognition performance on many computer vision,
speech, and language processing tasks. However, these networks have also been
shown susceptible to carefully crafted adversarial perturbations which force
misclassificatio... | computer science |
10,916 | Enhancing Observability in Distribution Grids using Smart Meter Data | math.OC | Due to limited metering infrastructure, distribution grids are currently
challenged by observability issues. On the other hand, smart meter data,
including local voltage magnitudes and power injections, are communicated to
the utility operator from grid buses with renewable generation and
demand-response programs. This... | computer science |
10,917 | Robust mixture of experts modeling using the skew $t$ distribution | stat.ME | Mixture of Experts (MoE) is a popular framework in the fields of statistics
and machine learning for modeling heterogeneity in data for regression,
classification and clustering. MoE for continuous data are usually based on the
normal distribution. However, it is known that for data with asymmetric
behavior, heavy tail... | computer science |
10,918 | Bayesian Decision Process for Cost-Efficient Dynamic Ranking via
Crowdsourcing | stat.ML | Rank aggregation based on pairwise comparisons over a set of items has a wide
range of applications. Although considerable research has been devoted to the
development of rank aggregation algorithms, one basic question is how to
efficiently collect a large amount of high-quality pairwise comparisons for the
ranking pur... | computer science |
10,919 | Microstructure Representation and Reconstruction of Heterogeneous
Materials via Deep Belief Network for Computational Material Design | cs.LG | Integrated Computational Materials Engineering (ICME) aims to accelerate
optimal design of complex material systems by integrating material science and
design automation. For tractable ICME, it is required that (1) a structural
feature space be identified to allow reconstruction of new designs, and (2) the
reconstructi... | computer science |
10,920 | Robustness of Voice Conversion Techniques Under Mismatched Conditions | cs.SD | Most of the existing studies on voice conversion (VC) are conducted in
acoustically matched conditions between source and target signal. However, the
robustness of VC methods in presence of mismatch remains unknown. In this
paper, we report a comparative analysis of different VC techniques under
mismatched conditions. ... | computer science |
10,921 | Multi-Region Neural Representation: A novel model for decoding visual
stimuli in human brains | stat.ML | Multivariate Pattern (MVP) classification holds enormous potential for
decoding visual stimuli in the human brain by employing task-based fMRI data
sets. There is a wide range of challenges in the MVP techniques, i.e.
decreasing noise and sparsity, defining effective regions of interest (ROIs),
visualizing results, and... | computer science |
10,922 | Provable learning of Noisy-or Networks | cs.LG | Many machine learning applications use latent variable models to explain
structure in data, whereby visible variables (= coordinates of the given
datapoint) are explained as a probabilistic function of some hidden variables.
Finding parameters with the maximum likelihood is NP-hard even in very simple
settings. In rece... | computer science |
10,923 | Geometric descent method for convex composite minimization | math.OC | In this paper, we extend the geometric descent method recently proposed by
Bubeck, Lee and Singh to tackle nonsmooth and strongly convex composite
problems. We prove that our proposed algorithm, dubbed geometric proximal
gradient method (GeoPG), converges with a linear rate $(1-1/\sqrt{\kappa})$ and
thus achieves the o... | computer science |
10,924 | The interplay between system identification and machine learning | cs.SY | Learning from examples is one of the key problems in science and engineering.
It deals with function reconstruction from a finite set of direct and noisy
samples. Regularization in reproducing kernel Hilbert spaces (RKHSs) is widely
used to solve this task and includes powerful estimators such as regularization
network... | computer science |
10,925 | Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex
Matrix Factorization | cs.LG | We propose a general theory for studying the \xl{landscape} of nonconvex
\xl{optimization} with underlying symmetric structures \tz{for a class of
machine learning problems (e.g., low-rank matrix factorization, phase
retrieval, and deep linear neural networks)}. In specific, we characterize the
locations of stationary ... | computer science |
10,926 | Counterfactual Prediction with Deep Instrumental Variables Networks | stat.AP | We are in the middle of a remarkable rise in the use and capability of
artificial intelligence. Much of this growth has been fueled by the success of
deep learning architectures: models that map from observables to outputs via
multiple layers of latent representations. These deep learning algorithms are
effective tools... | computer science |
10,927 | Minimax Manifold Estimation | stat.ML | We find the minimax rate of convergence in Hausdorff distance for estimating
a manifold M of dimension d embedded in R^D given a noisy sample from the
manifold. We assume that the manifold satisfies a smoothness condition and that
the noise distribution has compact support. We show that the optimal rate of
convergence ... | computer science |
10,928 | A generalized risk approach to path inference based on hidden Markov
models | stat.ML | Motivated by the unceasing interest in hidden Markov models (HMMs), this
paper re-examines hidden path inference in these models, using primarily a
risk-based framework. While the most common maximum a posteriori (MAP), or
Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have
long been around, ... | computer science |
10,929 | Learning Functions of Few Arbitrary Linear Parameters in High Dimensions | math.NA | Let us assume that $f$ is a continuous function defined on the unit ball of
$\mathbb R^d$, of the form $f(x) = g (A x)$, where $A$ is a $k \times d$ matrix
and $g$ is a function of $k$ variables for $k \ll d$. We are given a budget $m
\in \mathbb N$ of possible point evaluations $f(x_i)$, $i=1,...,m$, of $f$,
which we ... | computer science |
10,930 | Ultrametric and Generalized Ultrametric in Computational Logic and in
Data Analysis | cs.LO | Following a review of metric, ultrametric and generalized ultrametric, we
review their application in data analysis. We show how they allow us to explore
both geometry and topology of information, starting with measured data. Some
themes are then developed based on the use of metric, ultrametric and
generalized ultrame... | computer science |
10,931 | Structured sparsity-inducing norms through submodular functions | cs.LG | Sparse methods for supervised learning aim at finding good linear predictors
from as few variables as possible, i.e., with small cardinality of their
supports. This combinatorial selection problem is often turned into a convex
optimization problem by replacing the cardinality function by its convex
envelope (tightest c... | computer science |
10,932 | Entropy-Based Search Algorithm for Experimental Design | stat.ML | The scientific method relies on the iterated processes of inference and
inquiry. The inference phase consists of selecting the most probable models
based on the available data; whereas the inquiry phase consists of using what
is known about the models to select the most relevant experiment. Optimizing
inquiry involves ... | computer science |
10,933 | Fixed-point and coordinate descent algorithms for regularized kernel
methods | cs.LG | In this paper, we study two general classes of optimization algorithms for
kernel methods with convex loss function and quadratic norm regularization, and
analyze their convergence. The first approach, based on fixed-point iterations,
is simple to implement and analyze, and can be easily parallelized. The second,
based... | computer science |
10,934 | Adaptive Forgetting Factor Fictitious Play | stat.ML | It is now well known that decentralised optimisation can be formulated as a
potential game, and game-theoretical learning algorithms can be used to find an
optimum. One of the most common learning techniques in game theory is
fictitious play. However fictitious play is founded on an implicit assumption
that opponents' ... | computer science |
10,935 | Finding Density Functionals with Machine Learning | cs.LG | Machine learning is used to approximate density functionals. For the model
problem of the kinetic energy of non-interacting fermions in 1d, mean absolute
errors below 1 kcal/mol on test densities similar to the training set are
reached with fewer than 100 training densities. A predictor identifies if a
test density is ... | computer science |
10,936 | High-Rank Matrix Completion and Subspace Clustering with Missing Data | cs.IT | This paper considers the problem of completing a matrix with many missing
entries under the assumption that the columns of the matrix belong to a union
of multiple low-rank subspaces. This generalizes the standard low-rank matrix
completion problem to situations in which the matrix rank can be quite high or
even full r... | computer science |
10,937 | High-dimensional Sparse Inverse Covariance Estimation using Greedy
Methods | cs.LG | In this paper we consider the task of estimating the non-zero pattern of the
sparse inverse covariance matrix of a zero-mean Gaussian random vector from a
set of iid samples. Note that this is also equivalent to recovering the
underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We
present two no... | computer science |
10,938 | Understanding the Interaction between Interests, Conversations and
Friendships in Facebook | cs.SI | In this paper, we explore salient questions about user interests,
conversations and friendships in the Facebook social network, using a novel
latent space model that integrates several data types. A key challenge of
studying Facebook's data is the wide range of data modalities such as text,
network links, and categoric... | computer science |
10,939 | Deep Gaussian Processes | stat.ML | In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a
deep belief network based on Gaussian process mappings. The data is modeled as
the output of a multivariate GP. The inputs to that Gaussian process are then
governed by another GP. A single layer model is equivalent to a standard GP or
the GP ... | computer science |
10,940 | Learning curves for multi-task Gaussian process regression | cs.LG | We study the average case performance of multi-task Gaussian process (GP)
regression as captured in the learning curve, i.e. the average Bayes error for
a chosen task versus the total number of examples $n$ for all tasks. For GP
covariances that are the product of an input-dependent covariance function and
a free-form ... | computer science |
10,941 | Partition Tree Weighting | cs.IT | This paper introduces the Partition Tree Weighting technique, an efficient
meta-algorithm for piecewise stationary sources. The technique works by
performing Bayesian model averaging over a large class of possible partitions
of the data into locally stationary segments. It uses a prior, closely related
to the Context T... | computer science |
10,942 | Stochastic ADMM for Nonsmooth Optimization | cs.LG | We present a stochastic setting for optimization problems with nonsmooth
convex separable objective functions over linear equality constraints. To solve
such problems, we propose a stochastic Alternating Direction Method of
Multipliers (ADMM) algorithm. Our algorithm applies to a more general class of
nonsmooth convex ... | computer science |
10,943 | Discussion: Latent variable graphical model selection via convex
optimization | math.ST | Discussion of "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290]. | computer science |
10,944 | Discussion: Latent variable graphical model selection via convex
optimization | math.ST | Discussion of "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290]. | computer science |
10,945 | Discussion: Latent variable graphical model selection via convex
optimization | math.ST | Discussion of "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290]. | computer science |
10,946 | Discussion: Latent variable graphical model selection via convex
optimization | math.ST | Discussion of "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290]. | computer science |
10,947 | Rejoinder: Latent variable graphical model selection via convex
optimization | math.ST | Rejoinder to "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290]. | computer science |
10,948 | Active and passive learning of linear separators under log-concave
distributions | cs.LG | We provide new results concerning label efficient, polynomial time, passive
and active learning of linear separators. We prove that active learning
provides an exponential improvement over PAC (passive) learning of homogeneous
linear separators under nearly log-concave distributions. Building on this, we
provide a comp... | computer science |
10,949 | Random walk kernels and learning curves for Gaussian process regression
on random graphs | stat.ML | We consider learning on graphs, guided by kernels that encode similarity
between vertices. Our focus is on random walk kernels, the analogues of squared
exponential kernels in Euclidean spaces. We show that on large, locally
treelike, graphs these have some counter-intuitive properties, specifically in
the limit of lar... | computer science |
10,950 | Blind Signal Separation in the Presence of Gaussian Noise | cs.LG | A prototypical blind signal separation problem is the so-called cocktail
party problem, with n people talking simultaneously and n different microphones
within a room. The goal is to recover each speech signal from the microphone
inputs. Mathematically this can be modeled by assuming that we are given
samples from an n... | computer science |
10,951 | LAGE: A Java Framework to reconstruct Gene Regulatory Networks from
Large-Scale Continues Expression Data | cs.LG | LAGE is a systematic framework developed in Java. The motivation of LAGE is
to provide a scalable and parallel solution to reconstruct Gene Regulatory
Networks (GRNs) from continuous gene expression data for very large amount of
genes. The basic idea of our framework is motivated by the philosophy of
divideand-conquer.... | computer science |
10,952 | Efficient Monte Carlo Methods for Multi-Dimensional Learning with
Classifier Chains | cs.LG | Multi-dimensional classification (MDC) is the supervised learning problem
where an instance is associated with multiple classes, rather than with a
single class, as in traditional classification problems. Since these classes
are often strongly correlated, modeling the dependencies between them allows
MDC methods to imp... | computer science |
10,953 | Efficient learning of simplices | cs.LG | We show an efficient algorithm for the following problem: Given uniformly
random points from an arbitrary n-dimensional simplex, estimate the simplex.
The size of the sample and the number of arithmetic operations of our algorithm
are polynomial in n. This answers a question of Frieze, Jerrum and Kannan
[FJK]. Our resu... | computer science |
10,954 | Measures of Entropy from Data Using Infinitely Divisible Kernels | cs.LG | Information theory provides principled ways to analyze different inference
and learning problems such as hypothesis testing, clustering, dimensionality
reduction, classification, among others. However, the use of information
theoretic quantities as test statistics, that is, as quantities obtained from
empirical data, p... | computer science |
10,955 | Random Utility Theory for Social Choice | cs.MA | Random utility theory models an agent's preferences on alternatives by
drawing a real-valued score on each alternative (typically independently) from
a parameterized distribution, and then ranking the alternatives according to
scores. A special case that has received significant attention is the
Plackett-Luce model, fo... | computer science |
10,956 | Iterative Thresholding Algorithm for Sparse Inverse Covariance
Estimation | stat.CO | The L1-regularized maximum likelihood estimation problem has recently become
a topic of great interest within the machine learning, statistics, and
optimization communities as a method for producing sparse inverse covariance
estimators. In this paper, a proximal gradient method (G-ISTA) for performing
L1-regularized co... | computer science |
10,957 | Proximal Stochastic Dual Coordinate Ascent | stat.ML | We introduce a proximal version of dual coordinate ascent method. We
demonstrate how the derived algorithmic framework can be used for numerous
regularized loss minimization problems, including $\ell_1$ regularization and
structured output SVM. The convergence rates we obtain match, and sometimes
improve, state-of-the-... | computer science |
10,958 | Boosting Simple Collaborative Filtering Models Using Ensemble Methods | cs.IR | In this paper we examine the effect of applying ensemble learning to the
performance of collaborative filtering methods. We present several systematic
approaches for generating an ensemble of collaborative filtering models based
on a single collaborative filtering algorithm (single-model or homogeneous
ensemble). We pr... | computer science |
10,959 | Time-series Scenario Forecasting | stat.ML | Many applications require the ability to judge uncertainty of time-series
forecasts. Uncertainty is often specified as point-wise error bars around a
mean or median forecast. Due to temporal dependencies, such a method obscures
some information. We would ideally have a way to query the posterior
probability of the enti... | computer science |
10,960 | Spectral Clustering: An empirical study of Approximation Algorithms and
its Application to the Attrition Problem | cs.LG | Clustering is the problem of separating a set of objects into groups (called
clusters) so that objects within the same cluster are more similar to each
other than to those in different clusters. Spectral clustering is a now
well-known method for clustering which utilizes the spectrum of the data
similarity matrix to pe... | computer science |
10,961 | Application of three graph Laplacian based semi-supervised learning
methods to protein function prediction problem | cs.LG | Protein function prediction is the important problem in modern biology. In
this paper, the un-normalized, symmetric normalized, and random walk graph
Laplacian based semi-supervised learning methods will be applied to the
integrated network combined from multiple networks to predict the functions of
all yeast proteins ... | computer science |
10,962 | Bayesian nonparametric models for ranked data | stat.ML | We develop a Bayesian nonparametric extension of the popular Plackett-Luce
choice model that can handle an infinite number of choice items. Our framework
is based on the theory of random atomic measures, with the prior specified by a
gamma process. We derive a posterior characterization and a simple and
effective Gibbs... | computer science |
10,963 | Fast Marginalized Block Sparse Bayesian Learning Algorithm | cs.IT | The performance of sparse signal recovery from noise corrupted,
underdetermined measurements can be improved if both sparsity and correlation
structure of signals are exploited. One typical correlation structure is the
intra-block correlation in block sparse signals. To exploit this structure, a
framework, called block... | computer science |
10,964 | Bayesian nonparametric Plackett-Luce models for the analysis of
preferences for college degree programmes | stat.ML | In this paper we propose a Bayesian nonparametric model for clustering
partial ranking data. We start by developing a Bayesian nonparametric extension
of the popular Plackett-Luce choice model that can handle an infinite number of
choice items. Our framework is based on the theory of random atomic measures,
with the pr... | computer science |
10,965 | Analysis of a randomized approximation scheme for matrix multiplication | cs.DS | This note gives a simple analysis of a randomized approximation scheme for
matrix multiplication proposed by Sarlos (2006) based on a random rotation
followed by uniform column sampling. The result follows from a matrix version
of Bernstein's inequality and a tail inequality for quadratic forms in
subgaussian random ve... | computer science |
10,966 | Bayesian learning of noisy Markov decision processes | stat.ML | We consider the inverse reinforcement learning problem, that is, the problem
of learning from, and then predicting or mimicking a controller based on
state/action data. We propose a statistical model for such data, derived from
the structure of a Markov decision process. Adopting a Bayesian approach to
inference, we sh... | computer science |
10,967 | Duality between subgradient and conditional gradient methods | cs.LG | Given a convex optimization problem and its dual, there are many possible
first-order algorithms. In this paper, we show the equivalence between mirror
descent algorithms and algorithms generalizing the conditional gradient method.
This is done through convex duality, and implies notably that for certain
problems, such... | computer science |
10,968 | Robustness Analysis of Hottopixx, a Linear Programming Model for
Factoring Nonnegative Matrices | stat.ML | Although nonnegative matrix factorization (NMF) is NP-hard in general, it has
been shown very recently that it is tractable under the assumption that the
input nonnegative data matrix is close to being separable (separability
requires that all columns of the input matrix belongs to the cone spanned by a
small subset of... | computer science |
10,969 | Classification Recouvrante Basée sur les Méthodes à Noyau | cs.LG | Overlapping clustering problem is an important learning issue in which
clusters are not mutually exclusive and each object may belongs simultaneously
to several clusters. This paper presents a kernel based method that produces
overlapping clusters on a high feature space using mercer kernel techniques to
improve separa... | computer science |
10,970 | Overlapping clustering based on kernel similarity metric | stat.ML | Producing overlapping schemes is a major issue in clustering. Recent proposed
overlapping methods relies on the search of an optimal covering and are based
on different metrics, such as Euclidean distance and I-Divergence, used to
measure closeness between observations. In this paper, we propose the use of
another meas... | computer science |
10,971 | Efficient algorithms for robust recovery of images from compressed data | cs.IT | Compressed sensing (CS) is an important theory for sub-Nyquist sampling and
recovery of compressible data. Recently, it has been extended by Pham and
Venkatesh to cope with the case where corruption to the CS data is modeled as
impulsive noise. The new formulation, termed as robust CS, combines robust
statistics and CS... | computer science |
10,972 | Approximate Rank-Detecting Factorization of Low-Rank Tensors | stat.ML | We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3
tensor and calculates its factorization into rank-one components. We provide
generative conditions for the algorithm to work and demonstrate on both
synthetic and real world data that AROFAC2 is a potentially outperforming
alternative to the go... | computer science |
10,973 | Memory Limited, Streaming PCA | stat.ML | We consider streaming, one-pass principal component analysis (PCA), in the
high-dimensional regime, with limited memory. Here, $p$-dimensional samples are
presented sequentially, and the goal is to produce the $k$-dimensional subspace
that best approximates these points. Standard algorithms require $O(p^2)$
memory; mea... | computer science |
10,974 | Simple one-pass algorithm for penalized linear regression with
cross-validation on MapReduce | stat.ML | In this paper, we propose a one-pass algorithm on MapReduce for penalized
linear regression
\[f_\lambda(\alpha, \beta) = \|Y - \alpha\mathbf{1} - X\beta\|_2^2 +
p_{\lambda}(\beta)\] where $\alpha$ is the intercept which can be omitted
depending on application; $\beta$ is the coefficients and $p_{\lambda}$ is the
pena... | computer science |
10,975 | Semi-supervised clustering methods | stat.ME | Cluster analysis methods seek to partition a data set into homogeneous
subgroups. It is useful in a wide variety of applications, including document
processing and modern genetics. Conventional clustering methods are
unsupervised, meaning that there is no outcome variable nor is anything known
about the relationship be... | computer science |
10,976 | Algorithms of the LDA model [REPORT] | cs.LG | We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them
are variational inference algorithms: Variational Bayesian inference and Online
Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC)
algorithm -- Collapsed Gibbs sampling. We compare their time complexity and
performance.... | computer science |
10,977 | Semi-supervised Ranking Pursuit | stat.ML | We propose a novel sparse preference learning/ranking algorithm. Our
algorithm approximates the true utility function by a weighted sum of basis
functions using the squared loss on pairs of data points, and is a
generalization of the kernel matching pursuit method. It can operate both in a
supervised and a semi-supervi... | computer science |
10,978 | AdaBoost and Forward Stagewise Regression are First-Order Convex
Optimization Methods | stat.ML | Boosting methods are highly popular and effective supervised learning methods
which combine weak learners into a single accurate model with good statistical
performance. In this paper, we analyze two well-known boosting methods,
AdaBoost and Incremental Forward Stagewise Regression (FS$_\varepsilon$), by
establishing t... | computer science |
10,979 | Dropout Training as Adaptive Regularization | stat.ML | Dropout and other feature noising schemes control overfitting by artificially
corrupting the training data. For generalized linear models, dropout performs a
form of adaptive regularization. Using this viewpoint, we show that the dropout
regularizer is first-order equivalent to an L2 regularizer applied after
scaling t... | computer science |
10,980 | Supervised Learning and Anti-learning of Colorectal Cancer Classes and
Survival Rates from Cellular Biology Parameters | cs.LG | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to learn re... | computer science |
10,981 | Error Rate Bounds in Crowdsourcing Models | stat.ML | Crowdsourcing is an effective tool for human-powered computation on many
tasks challenging for computers. In this paper, we provide finite-sample
exponential bounds on the error rate (in probability and in expectation) of
hyperplane binary labeling rules under the Dawid-Skene crowdsourcing model. The
bounds can be appl... | computer science |
10,982 | Flow-Based Algorithms for Local Graph Clustering | cs.DS | Given a subset S of vertices of an undirected graph G, the cut-improvement
problem asks us to find a subset S that is similar to A but has smaller
conductance. A very elegant algorithm for this problem has been given by
Andersen and Lang [AL08] and requires solving a small number of
single-commodity maximum flow comput... | computer science |
10,983 | Statistical Active Learning Algorithms for Noise Tolerance and
Differential Privacy | cs.LG | We describe a framework for designing efficient active learning algorithms
that are tolerant to random classification noise and are
differentially-private. The framework is based on active learning algorithms
that are statistical in the sense that they rely on estimates of expectations
of functions of filtered random e... | computer science |
10,984 | On Soft Power Diagrams | cs.LG | Many applications in data analysis begin with a set of points in a Euclidean
space that is partitioned into clusters. Common tasks then are to devise a
classifier deciding which of the clusters a new point is associated to, finding
outliers with respect to the clusters, or identifying the type of clustering
used for th... | computer science |
10,985 | A New Convex Relaxation for Tensor Completion | cs.LG | We study the problem of learning a tensor from a set of linear measurements.
A prominent methodology for this problem is based on a generalization of trace
norm regularization, which has been used extensively for learning low rank
matrices, to the tensor setting. In this paper, we highlight some limitations
of this app... | computer science |
10,986 | Robust Subspace Clustering via Thresholding | stat.ML | The problem of clustering noisy and incompletely observed high-dimensional
data points into a union of low-dimensional subspaces and a set of outliers is
considered. The number of subspaces, their dimensions, and their orientations
are assumed unknown. We propose a simple low-complexity subspace clustering
algorithm, w... | computer science |
10,987 | Non-stationary Stochastic Optimization | math.PR | We consider a non-stationary variant of a sequential stochastic optimization
problem, in which the underlying cost functions may change along the horizon.
We propose a measure, termed variation budget, that controls the extent of said
change, and study how restrictions on this budget impact achievable
performance. We i... | computer science |
10,988 | On GROUSE and Incremental SVD | cs.NA | GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental
algorithm for identifying a subspace of Rn from a sequence of vectors in this
subspace, where only a subset of components of each vector is revealed at each
iteration. Recent analysis has shown that GROUSE converges locally at an
expected linea... | computer science |
10,989 | Online Optimization in Dynamic Environments | stat.ML | High-velocity streams of high-dimensional data pose significant "big data"
analysis challenges across a range of applications and settings. Online
learning and online convex programming play a significant role in the rapid
recovery of important or anomalous information from these large datastreams.
While recent advance... | computer science |
10,990 | Modeling Human Decision-making in Generalized Gaussian Multi-armed
Bandits | cs.LG | We present a formal model of human decision-making in explore-exploit tasks
using the context of multi-armed bandit problems, where the decision-maker must
choose among multiple options with uncertain rewards. We address the standard
multi-armed bandit problem, the multi-armed bandit problem with transition
costs, and ... | computer science |
10,991 | DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering | stat.ME | The level set tree approach of Hartigan (1975) provides a probabilistically
based and highly interpretable encoding of the clustering behavior of a
dataset. By representing the hierarchy of data modes as a dendrogram of the
level sets of a density estimator, this approach offers many advantages for
exploratory analysis... | computer science |
10,992 | The Power of Localization for Efficiently Learning Linear Separators
with Noise | cs.LG | We introduce a new approach for designing computationally efficient learning
algorithms that are tolerant to noise, and demonstrate its effectiveness by
designing algorithms with improved noise tolerance guarantees for learning
linear separators.
We consider both the malicious noise model and the adversarial label no... | computer science |
10,993 | Incoherence-Optimal Matrix Completion | cs.IT | This paper considers the matrix completion problem. We show that it is not
necessary to assume joint incoherence, which is a standard but unintuitive and
restrictive condition that is imposed by previous studies. This leads to a
sample complexity bound that is order-wise optimal with respect to the
incoherence paramete... | computer science |
10,994 | Online Learning of Dynamic Parameters in Social Networks | math.OC | This paper addresses the problem of online learning in a dynamic setting. We
consider a social network in which each individual observes a private signal
about the underlying state of the world and communicates with her neighbors at
each time period. Unlike many existing approaches, the underlying state is
dynamic, and... | computer science |
10,995 | Pseudo-Marginal Bayesian Inference for Gaussian Processes | stat.ML | The main challenges that arise when adopting Gaussian Process priors in
probabilistic modeling are how to carry out exact Bayesian inference and how to
account for uncertainty on model parameters when making model-based predictions
on out-of-sample data. Using probit regression as an illustrative working
example, this ... | computer science |
10,996 | Electricity Market Forecasting via Low-Rank Multi-Kernel Learning | stat.ML | The smart grid vision entails advanced information technology and data
analytics to enhance the efficiency, sustainability, and economics of the power
grid infrastructure. Aligned to this end, modern statistical learning tools are
leveraged here for electricity market inference. Day-ahead price forecasting is
cast as a... | computer science |
10,997 | Sequential Monte Carlo Bandits | stat.ML | In this paper we propose a flexible and efficient framework for handling
multi-armed bandits, combining sequential Monte Carlo algorithms with
hierarchical Bayesian modeling techniques. The framework naturally encompasses
restless bandits, contextual bandits, and other bandit variants under a single
inferential model. ... | computer science |
10,998 | Randomized Approximation of the Gram Matrix: Exact Computation and
Probabilistic Bounds | math.NA | Given a real matrix A with n columns, the problem is to approximate the Gram
product AA^T by c << n weighted outer products of columns of A. Necessary and
sufficient conditions for the exact computation of AA^T (in exact arithmetic)
from c >= rank(A) columns depend on the right singular vector matrix of A. For
a Monte-... | computer science |
10,999 | CAM: Causal additive models, high-dimensional order search and penalized
regression | stat.ME | We develop estimation for potentially high-dimensional additive structural
equation models. A key component of our approach is to decouple order search
among the variables from feature or edge selection in a directed acyclic graph
encoding the causal structure. We show that the former can be done with
nonregularized (r... | computer science |
11,000 | Learning Hidden Structures with Relational Models by Adequately
Involving Rich Information in A Network | cs.LG | Effectively modelling hidden structures in a network is very practical but
theoretically challenging. Existing relational models only involve very limited
information, namely the binary directional link data, embedded in a network to
learn hidden networking structures. There is other rich and meaningful
information (e.... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.