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10,601 | Towards stability and optimality in stochastic gradient descent | stat.ME | Iterative procedures for parameter estimation based on stochastic gradient
descent allow the estimation to scale to massive data sets. However, in both
theory and practice, they suffer from numerical instability. Moreover, they are
statistically inefficient as estimators of the true parameter value. To address
these tw... | computer science |
10,602 | The Boundary Forest Algorithm for Online Supervised and Unsupervised
Learning | cs.LG | We describe a new instance-based learning algorithm called the Boundary
Forest (BF) algorithm, that can be used for supervised and unsupervised
learning. The algorithm builds a forest of trees whose nodes store previously
seen examples. It can be shown data points one at a time and updates itself
incrementally, hence i... | computer science |
10,603 | Detecting the large entries of a sparse covariance matrix in
sub-quadratic time | stat.CO | The covariance matrix of a $p$-dimensional random variable is a fundamental
quantity in data analysis. Given $n$ i.i.d. observations, it is typically
estimated by the sample covariance matrix, at a computational cost of
$O(np^{2})$ operations. When $n,p$ are large, this computation may be
prohibitively slow. Moreover, ... | computer science |
10,604 | Removing systematic errors for exoplanet search via latent causes | stat.ML | We describe a method for removing the effect of confounders in order to
reconstruct a latent quantity of interest. The method, referred to as
half-sibling regression, is inspired by recent work in causal inference using
additive noise models. We provide a theoretical justification and illustrate
the potential of the me... | computer science |
10,605 | Mind the duality gap: safer rules for the Lasso | stat.ML | Screening rules allow to early discard irrelevant variables from the
optimization in Lasso problems, or its derivatives, making solvers faster. In
this paper, we propose new versions of the so-called $\textit{safe rules}$ for
the Lasso. Based on duality gap considerations, our new rules create safe test
regions whose d... | computer science |
10,606 | Global Convergence of Unmodified 3-Block ADMM for a Class of Convex
Minimization Problems | math.OC | The alternating direction method of multipliers (ADMM) has been successfully
applied to solve structured convex optimization problems due to its superior
practical performance. The convergence properties of the 2-block ADMM have been
studied extensively in the literature. Specifically, it has been proven that
the 2-blo... | computer science |
10,607 | Layered Adaptive Importance Sampling | stat.CO | Monte Carlo methods represent the "de facto" standard for approximating
complicated integrals involving multidimensional target distributions. In order
to generate random realizations from the target distribution, Monte Carlo
techniques use simpler proposal probability densities to draw candidate
samples. The performan... | computer science |
10,608 | Modelling-based experiment retrieval: A case study with gene expression
clustering | stat.ML | Motivation: Public and private repositories of experimental data are growing
to sizes that require dedicated methods for finding relevant data. To improve
on the state of the art of keyword searches from annotations, methods for
content-based retrieval have been proposed. In the context of gene expression
experiments, ... | computer science |
10,609 | The development of an information criterion for Change-Point Analysis | cs.LG | Change-point analysis is a flexible and computationally tractable tool for
the analysis of times series data from systems that transition between discrete
states and whose observables are corrupted by noise. The change-point algorithm
is used to identify the time indices (change points) at which the system
transitions ... | computer science |
10,610 | Inferring Graphs from Cascades: A Sparse Recovery Framework | cs.SI | In the Network Inference problem, one seeks to recover the edges of an
unknown graph from the observations of cascades propagating over this graph. In
this paper, we approach this problem from the sparse recovery perspective. We
introduce a general model of cascades, including the voter model and the
independent cascad... | computer science |
10,611 | Greedy Biomarker Discovery in the Genome with Applications to
Antimicrobial Resistance | cs.LG | The Set Covering Machine (SCM) is a greedy learning algorithm that produces
sparse classifiers. We extend the SCM for datasets that contain a huge number
of features. The whole genetic material of living organisms is an example of
such a case, where the number of feature exceeds 10^7. Three human pathogens
were used to... | computer science |
10,612 | Detecting bird sound in unknown acoustic background using crowdsourced
training data | stat.ML | Biodiversity monitoring using audio recordings is achievable at a truly
global scale via large-scale deployment of inexpensive, unattended recording
stations or by large-scale crowdsourcing using recording and species
recognition on mobile devices. The ability, however, to reliably identify
vocalising animal species is... | computer science |
10,613 | Clustering via Content-Augmented Stochastic Blockmodels | stat.ML | Much of the data being created on the web contains interactions between users
and items. Stochastic blockmodels, and other methods for community detection
and clustering of bipartite graphs, can infer latent user communities and
latent item clusters from this interaction data. These methods, however,
typically ignore t... | computer science |
10,614 | MLlib: Machine Learning in Apache Spark | cs.LG | Apache Spark is a popular open-source platform for large-scale data
processing that is well-suited for iterative machine learning tasks. In this
paper we present MLlib, Spark's open-source distributed machine learning
library. MLlib provides efficient functionality for a wide range of learning
settings and includes sev... | computer science |
10,615 | Multilevel Weighted Support Vector Machine for Classification on
Healthcare Data with Missing Values | stat.ML | This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often ... | computer science |
10,616 | A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and Finite
Constraint Violations for Online Convex Optimization with Long Term
Constraints | math.OC | This paper considers online convex optimization over a complicated constraint
set, which typically consists of multiple functional constraints and a set
constraint. The conventional projection based online projection algorithm
(Zinkevich, 2003) can be difficult to implement due to the potentially high
computation compl... | computer science |
10,617 | A General Retraining Framework for Scalable Adversarial Classification | cs.GT | Traditional classification algorithms assume that training and test data come
from similar distributions. This assumption is violated in adversarial
settings, where malicious actors modify instances to evade detection. A number
of custom methods have been developed for both adversarial evasion attacks and
robust learni... | computer science |
10,618 | Demystifying Fixed k-Nearest Neighbor Information Estimators | cs.LG | Estimating mutual information from i.i.d. samples drawn from an unknown joint
density function is a basic statistical problem of broad interest with
multitudinous applications. The most popular estimator is one proposed by
Kraskov and St\"ogbauer and Grassberger (KSG) in 2004, and is nonparametric and
based on the dist... | computer science |
10,619 | A General Distributed Dual Coordinate Optimization Framework for
Regularized Loss Minimization | cs.LG | In modern large-scale machine learning applications, the training data are
often partitioned and stored on multiple machines. It is customary to employ
the "data parallelism" approach, where the aggregated training loss is
minimized without moving data across machines. In this paper, we introduce a
novel distributed du... | computer science |
10,620 | Efficient Algorithms for Large-scale Generalized Eigenvector Computation
and Canonical Correlation Analysis | cs.LG | This paper considers the problem of canonical-correlation analysis (CCA)
(Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a
pair of symmetric matrices. These are two fundamental problems in data analysis
and scientific computing with numerous applications in machine learning and
statistics (... | computer science |
10,621 | Positive Definite Estimation of Large Covariance Matrix Using
Generalized Nonconvex Penalties | cs.IT | This work addresses the issue of large covariance matrix estimation in
high-dimensional statistical analysis. Recently, improved iterative algorithms
with positive-definite guarantee have been developed. However, these algorithms
cannot be directly extended to use a nonconvex penalty for sparsity inducing.
Generally, a... | computer science |
10,622 | Delta divergence: A novel decision cognizant measure of classifier
incongruence | cs.LG | Disagreement between two classifiers regarding the class membership of an
observation in pattern recognition can be indicative of an anomaly and its
nuance. As in general classifiers base their decision on class aposteriori
probabilities, the most natural approach to detecting classifier incongruence
is to use divergen... | computer science |
10,623 | Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor
Analysis | stat.ML | Factor analysis aims to determine latent factors, or traits, which summarize
a given data set. Inter-battery factor analysis extends this notion to multiple
views of the data. In this paper we show how a nonlinear, nonparametric version
of these models can be recovered through the Gaussian process latent variable
model... | computer science |
10,624 | Learning Sparse Additive Models with Interactions in High Dimensions | cs.LG | A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is referred to as a
Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in
\mathcal{S}}\phi_{l}(x_l)$, where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll
d$. Assuming $\phi_l$'s and $\mathcal{S}$ to be unknown, the problem of
estimating $f$ f... | computer science |
10,625 | Agnostic Estimation of Mean and Covariance | cs.DS | We consider the problem of estimating the mean and covariance of a
distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$
fraction of malicious noise; this is in contrast to much recent work where the
noise itself is assumed to be from a distribution of known type. The agnostic
problem includes m... | computer science |
10,626 | Stochastic Variance-Reduced ADMM | cs.LG | The alternating direction method of multipliers (ADMM) is a powerful
optimization solver in machine learning. Recently, stochastic ADMM has been
integrated with variance reduction methods for stochastic gradient, leading to
SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration
complexities. However,... | computer science |
10,627 | Evaluating the effect of topic consideration in identifying communities
of rating-based social networks | cs.SI | Finding meaningful communities in social network has attracted the attentions
of many researchers. The community structure of complex networks reveals both
their organization and hidden relations among their constituents. Most of the
researches in the field of community detection mainly focus on the topological
structu... | computer science |
10,628 | UBL: an R package for Utility-based Learning | cs.MS | This document describes the R package UBL that allows the use of several
methods for handling utility-based learning problems. Classification and
regression problems that assume non-uniform costs and/or benefits pose serious
challenges to predictive analytic tasks. In the context of meteorology,
finance, medicine, ecol... | computer science |
10,629 | Local Uncertainty Sampling for Large-Scale Multi-Class Logistic
Regression | stat.CO | A major challenge for building statistical models in the big data era is that
the available data volume may exceed the computational capability. A common
approach to solve this problem is to employ a subsampled dataset that can be
handled by the available computational resources. In this paper, we propose a
general sub... | computer science |
10,630 | Learning Compact Structural Representations for Audio Events Using
Regressor Banks | cs.SD | We introduce a new learned descriptor for audio signals which is efficient
for event representation. The entries of the descriptor are produced by
evaluating a set of regressors on the input signal. The regressors are
class-specific and trained using the random regression forests framework. Given
an input signal, each ... | computer science |
10,631 | Minimax Rates of Estimation for Sparse PCA in High Dimensions | stat.ML | We study sparse principal components analysis in the high-dimensional
setting, where $p$ (the number of variables) can be much larger than $n$ (the
number of observations). We prove optimal, non-asymptotic lower and upper
bounds on the minimax estimation error for the leading eigenvector when it
belongs to an $\ell_q$ ... | computer science |
10,632 | Active Bayesian Optimization: Minimizing Minimizer Entropy | stat.ME | The ultimate goal of optimization is to find the minimizer of a target
function.However, typical criteria for active optimization often ignore the
uncertainty about the minimizer. We propose a novel criterion for global
optimization and an associated sequential active learning strategy using
Gaussian processes.Our crit... | computer science |
10,633 | Fast MCMC sampling for Markov jump processes and continuous time
Bayesian networks | stat.ME | Markov jump processes and continuous time Bayesian networks are important
classes of continuous time dynamical systems. In this paper, we tackle the
problem of inferring unobserved paths in these models by introducing a fast
auxiliary variable Gibbs sampler. Our approach is based on the idea of
uniformization, and sets... | computer science |
10,634 | Learning mixed graphical models from data with p larger than n | stat.ME | Structure learning of Gaussian graphical models is an extensively studied
problem in the classical multivariate setting where the sample size n is larger
than the number of random variables p, as well as in the more challenging
setting when p>>n. However, analogous approaches for learning the structure of
graphical mod... | computer science |
10,635 | Rank/Norm Regularization with Closed-Form Solutions: Application to
Subspace Clustering | cs.LG | When data is sampled from an unknown subspace, principal component analysis
(PCA) provides an effective way to estimate the subspace and hence reduce the
dimension of the data. At the heart of PCA is the Eckart-Young-Mirsky theorem,
which characterizes the best rank k approximation of a matrix. In this paper,
we prove ... | computer science |
10,636 | mlpy: Machine Learning Python | cs.MS | mlpy is a Python Open Source Machine Learning library built on top of
NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of
state-of-the-art machine learning methods for supervised and unsupervised
problems and it is aimed at finding a reasonable compromise among modularity,
maintainability, repro... | computer science |
10,637 | Topological graph clustering with thin position | math.GT | A clustering algorithm partitions a set of data points into smaller sets
(clusters) such that each subset is more tightly packed than the whole. Many
approaches to clustering translate the vector data into a graph with edges
reflecting a distance or similarity metric on the points, then look for highly
connected subgra... | computer science |
10,638 | Factoring nonnegative matrices with linear programs | math.OC | This paper describes a new approach, based on linear programming, for
computing nonnegative matrix factorizations (NMFs). The key idea is a
data-driven model for the factorization where the most salient features in the
data are used to express the remaining features. More precisely, given a data
matrix X, the algorithm... | computer science |
10,639 | Learning Inclusion-Optimal Chordal Graphs | cs.LG | Chordal graphs can be used to encode dependency models that are representable
by both directed acyclic and undirected graphs. This paper discusses a very
simple and efficient algorithm to learn the chordal structure of a
probabilistic model from data. The algorithm is a greedy hill-climbing search
algorithm that uses t... | computer science |
10,640 | Clique Matrices for Statistical Graph Decomposition and Parameterising
Restricted Positive Definite Matrices | cs.DM | We introduce Clique Matrices as an alternative representation of undirected
graphs, being a generalisation of the incidence matrix representation. Here we
use clique matrices to decompose a graph into a set of possibly overlapping
clusters, de ned as well-connected subsets of vertices. The decomposition is
based on a s... | computer science |
10,641 | Learning the Bayesian Network Structure: Dirichlet Prior versus Data | cs.LG | In the Bayesian approach to structure learning of graphical models, the
equivalent sample size (ESS) in the Dirichlet prior over the model parameters
was recently shown to have an important effect on the maximum-a-posteriori
estimate of the Bayesian network structure. In our first contribution, we
theoretically analyze... | computer science |
10,642 | Continuous Time Dynamic Topic Models | cs.IR | In this paper, we develop the continuous time dynamic topic model (cDTM). The
cDTM is a dynamic topic model that uses Brownian motion to model the latent
topics through a sequential collection of documents, where a "topic" is a
pattern of word use that we expect to evolve over the course of the collection.
We derive an... | computer science |
10,643 | Learning the Structure and Parameters of Large-Population Graphical
Games from Behavioral Data | cs.LG | We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic se... | computer science |
10,644 | Quasi-Newton Methods: A New Direction | cs.NA | Four decades after their invention, quasi-Newton methods are still state of
the art in unconstrained numerical optimization. Although not usually
interpreted thus, these are learning algorithms that fit a local quadratic
approximation to the objective function. We show that many, including the most
popular, quasi-Newto... | computer science |
10,645 | A Hybrid Algorithm for Convex Semidefinite Optimization | cs.LG | We present a hybrid algorithm for optimizing a convex, smooth function over
the cone of positive semidefinite matrices. Our algorithm converges to the
global optimal solution and can be used to solve general large-scale
semidefinite programs and hence can be readily applied to a variety of machine
learning problems. We... | computer science |
10,646 | A Hierarchical Dirichlet Process Model with Multiple Levels of
Clustering for Human EEG Seizure Modeling | stat.AP | Driven by the multi-level structure of human intracranial
electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a
new variant of a hierarchical Dirichlet Process---the multi-level clustering
hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters
datasets on multiple levels. Our sei... | computer science |
10,647 | A Graphical Model Formulation of Collaborative Filtering Neighbourhood
Methods with Fast Maximum Entropy Training | cs.LG | Item neighbourhood methods for collaborative filtering learn a weighted graph
over the set of items, where each item is connected to those it is most similar
to. The prediction of a user's rating on an item is then given by that rating
of neighbouring items, weighted by their similarity. This paper presents a new
neigh... | computer science |
10,648 | A Complete Analysis of the l_1,p Group-Lasso | cs.LG | The Group-Lasso is a well-known tool for joint regularization in machine
learning methods. While the l_{1,2} and the l_{1,\infty} version have been
studied in detail and efficient algorithms exist, there are still open
questions regarding other l_{1,p} variants. We characterize conditions for
solutions of the l_{1,p} G... | computer science |
10,649 | Artist Agent: A Reinforcement Learning Approach to Automatic Stroke
Generation in Oriental Ink Painting | cs.LG | Oriental ink painting, called Sumi-e, is one of the most appealing painting
styles that has attracted artists around the world. Major challenges in
computer-based Sumi-e simulation are to abstract complex scene information and
draw smooth and natural brush strokes. To automatically find such strokes, we
propose to mode... | computer science |
10,650 | Stability of matrix factorization for collaborative filtering | cs.NA | We study the stability vis a vis adversarial noise of matrix factorization
algorithm for matrix completion. In particular, our results include: (I) we
bound the gap between the solution matrix of the factorization method and the
ground truth in terms of root mean square error; (II) we treat the matrix
factorization as ... | computer science |
10,651 | Fast Computation of Subpath Kernel for Trees | cs.DS | The kernel method is a potential approach to analyzing structured data such
as sequences, trees, and graphs; however, unordered trees have not been
investigated extensively. Kimura et al. (2011) proposed a kernel function for
unordered trees on the basis of their subpaths, which are vertical
substructures of trees resp... | computer science |
10,652 | Ensemble Methods for Convex Regression with Applications to Geometric
Programming Based Circuit Design | cs.LG | Convex regression is a promising area for bridging statistical estimation and
deterministic convex optimization. New piecewise linear convex regression
methods are fast and scalable, but can have instability when used to
approximate constraints or objective functions for optimization. Ensemble
methods, like bagging, sm... | computer science |
10,653 | Approximate Principal Direction Trees | cs.LG | We introduce a new spatial data structure for high dimensional data called
the \emph{approximate principal direction tree} (APD tree) that adapts to the
intrinsic dimension of the data. Our algorithm ensures vector-quantization
accuracy similar to that of computationally-expensive PCA trees with similar
time-complexity... | computer science |
10,654 | Comparison-Based Learning with Rank Nets | cs.LG | We consider the problem of search through comparisons, where a user is
presented with two candidate objects and reveals which is closer to her
intended target. We study adaptive strategies for finding the target, that
require knowledge of rank relationships but not actual distances between
objects. We propose a new str... | computer science |
10,655 | Copula-based Kernel Dependency Measures | cs.LG | The paper presents a new copula based method for measuring dependence between
random variables. Our approach extends the Maximum Mean Discrepancy to the
copula of the joint distribution. We prove that this approach has several
advantageous properties. Similarly to Shannon mutual information, the proposed
dependence mea... | computer science |
10,656 | Hidden Markov Models with mixtures as emission distributions | stat.ML | In unsupervised classification, Hidden Markov Models (HMM) are used to
account for a neighborhood structure between observations. The emission
distributions are often supposed to belong to some parametric family. In this
paper, a semiparametric modeling where the emission distributions are a mixture
of parametric distr... | computer science |
10,657 | Collaborative Filtering and the Missing at Random Assumption | cs.LG | Rating prediction is an important application, and a popular research topic
in collaborative filtering. However, both the validity of learning algorithms,
and the validity of standard testing procedures rest on the assumption that
missing ratings are missing at random (MAR). In this paper we present the
results of a us... | computer science |
10,658 | Nonparametric Bayes Pachinko Allocation | cs.IR | Recent advances in topic models have explored complicated structured
distributions to represent topic correlation. For example, the pachinko
allocation model (PAM) captures arbitrary, nested, and possibly sparse
correlations between topics using a directed acyclic graph (DAG). While PAM
provides more flexibility and gr... | computer science |
10,659 | Fast Nonparametric Conditional Density Estimation | stat.ME | Conditional density estimation generalizes regression by modeling a full
density f(yjx) rather than only the expected value E(yjx). This is important
for many tasks, including handling multi-modality and generating prediction
intervals. Though fundamental and widely applicable, nonparametric conditional
density estimat... | computer science |
10,660 | A Characterization of Markov Equivalence Classes for Directed Acyclic
Graphs with Latent Variables | stat.ME | Different directed acyclic graphs (DAGs) may be Markov equivalent in the
sense that they entail the same conditional independence relations among the
observed variables. Meek (1995) characterizes Markov equivalence classes for
DAGs (with no latent variables) by presenting a set of orientation rules that
can correctly i... | computer science |
10,661 | Directed Time Series Regression for Control | cs.LG | We propose directed time series regression, a new approach to estimating
parameters of time-series models for use in certainty equivalent model
predictive control. The approach combines merits of least squares regression
and empirical optimization. Through a computational study involving a
stochastic version of a well ... | computer science |
10,662 | Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring | cs.LG | In this paper we address the following question: Can we approximately sample
from a Bayesian posterior distribution if we are only allowed to touch a small
mini-batch of data-items for every sample we generate?. An algorithm based on
the Langevin equation with stochastic gradients (SGLD) was previously proposed
to solv... | computer science |
10,663 | Improved Estimation in Time Varying Models | cs.LG | Locally adapted parameterizations of a model (such as locally weighted
regression) are expressive but often suffer from high variance. We describe an
approach for reducing the variance, based on the idea of estimating
simultaneously a transformed space for the model, as well as locally adapted
parameterizations in this... | computer science |
10,664 | Canonical Trends: Detecting Trend Setters in Web Data | cs.LG | Much information available on the web is copied, reused or rephrased. The
phenomenon that multiple web sources pick up certain information is often
called trend. A central problem in the context of web data mining is to detect
those web sources that are first to publish information which will give rise to
a trend. We p... | computer science |
10,665 | Poisoning Attacks against Support Vector Machines | cs.LG | We investigate a family of poisoning attacks against Support Vector Machines
(SVM). Such attacks inject specially crafted training data that increases the
SVM's test error. Central to the motivation for these attacks is the fact that
most learning algorithms assume that their training data comes from a natural
or well-... | computer science |
10,666 | Modeling Temporal Dependencies in High-Dimensional Sequences:
Application to Polyphonic Music Generation and Transcription | cs.LG | We investigate the problem of modeling symbolic sequences of polyphonic music
in a completely general piano-roll representation. We introduce a probabilistic
model based on distribution estimators conditioned on a recurrent neural
network that is able to discover temporal dependencies in high-dimensional
sequences. Our... | computer science |
10,667 | Nonparametric Link Prediction in Dynamic Networks | cs.LG | We propose a non-parametric link prediction algorithm for a sequence of graph
snapshots over time. The model predicts links based on the features of its
endpoints, as well as those of the local neighborhood around the endpoints.
This allows for different types of neighborhoods in a graph, each with its own
dynamics (e.... | computer science |
10,668 | Convergence Rates for Differentially Private Statistical Estimation | cs.LG | Differential privacy is a cryptographically-motivated definition of privacy
which has gained significant attention over the past few years. Differentially
private solutions enforce privacy by adding random noise to a function computed
over the data, and the challenge in designing such algorithms is to control the
added... | computer science |
10,669 | Policy Gradients with Variance Related Risk Criteria | cs.LG | Managing risk in dynamic decision problems is of cardinal importance in many
fields such as finance and process control. The most common approach to
defining risk is through various variance related criteria such as the Sharpe
Ratio or the standard deviation adjusted reward. It is known that optimizing
many of the vari... | computer science |
10,670 | Scaling Up Coordinate Descent Algorithms for Large $\ell_1$
Regularization Problems | cs.LG | We present a generic framework for parallel coordinate descent (CD)
algorithms that includes, as special cases, the original sequential algorithms
Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm.
We introduce two novel parallel algorithms that are also special
cases---Thread-Greedy CD and ... | computer science |
10,671 | On the Difficulty of Nearest Neighbor Search | cs.LG | Fast approximate nearest neighbor (NN) search in large databases is becoming
popular. Several powerful learning-based formulations have been proposed
recently. However, not much attention has been paid to a more fundamental
question: how difficult is (approximate) nearest neighbor search in a given
data set? And which ... | computer science |
10,672 | The Nonparametric Metadata Dependent Relational Model | cs.LG | We introduce the nonparametric metadata dependent relational (NMDR) model, a
Bayesian nonparametric stochastic block model for network data. The NMDR allows
the entities associated with each node to have mixed membership in an unbounded
collection of latent communities. Learned regression models allow these
memberships... | computer science |
10,673 | Distributed Parameter Estimation via Pseudo-likelihood | cs.LG | Estimating statistical models within sensor networks requires distributed
algorithms, in which both data and computation are distributed across the nodes
of the network. We propose a general approach for distributed learning based on
combining local estimators defined by pseudo-likelihood components,
encompassing a num... | computer science |
10,674 | Variational Bayesian Inference with Stochastic Search | cs.LG | Mean-field variational inference is a method for approximate Bayesian
posterior inference. It approximates a full posterior distribution with a
factorized set of distributions by maximizing a lower bound on the marginal
likelihood. This requires the ability to integrate a sum of terms in the log
joint likelihood using ... | computer science |
10,675 | Sparse Support Vector Infinite Push | cs.LG | In this paper, we address the problem of embedded feature selection for
ranking on top of the list problems. We pose this problem as a regularized
empirical risk minimization with $p$-norm push loss function ($p=\infty$) and
sparsity inducing regularizers. We leverage the issues related to this
challenging optimization... | computer science |
10,676 | Copula Mixture Model for Dependency-seeking Clustering | stat.ME | We introduce a copula mixture model to perform dependency-seeking clustering
when co-occurring samples from different data sources are available. The model
takes advantage of the great flexibility offered by the copulas framework to
extend mixtures of Canonical Correlation Analysis to multivariate data with
arbitrary c... | computer science |
10,677 | A Topic Model for Melodic Sequences | cs.LG | We examine the problem of learning a probabilistic model for melody directly
from musical sequences belonging to the same genre. This is a challenging task
as one needs to capture not only the rich temporal structure evident in music,
but also the complex statistical dependencies among different music components.
To ad... | computer science |
10,678 | Isoelastic Agents and Wealth Updates in Machine Learning Markets | cs.LG | Recently, prediction markets have shown considerable promise for developing
flexible mechanisms for machine learning. In this paper, agents with isoelastic
utilities are considered. It is shown that the costs associated with
homogeneous markets of agents with isoelastic utilities produce equilibrium
prices correspondin... | computer science |
10,679 | Smoothness and Structure Learning by Proxy | cs.LG | As data sets grow in size, the ability of learning methods to find structure
in them is increasingly hampered by the time needed to search the large spaces
of possibilities and generate a score for each that takes all of the observed
data into account. For instance, Bayesian networks, the model chosen in this
paper, ha... | computer science |
10,680 | Variational Inference in Non-negative Factorial Hidden Markov Models for
Efficient Audio Source Separation | cs.LG | The past decade has seen substantial work on the use of non-negative matrix
factorization and its probabilistic counterparts for audio source separation.
Although able to capture audio spectral structure well, these models neglect
the non-stationarity and temporal dynamics that are important properties of
audio. The re... | computer science |
10,681 | A Combinatorial Algebraic Approach for the Identifiability of Low-Rank
Matrix Completion | cs.LG | In this paper, we review the problem of matrix completion and expose its
intimate relations with algebraic geometry, combinatorics and graph theory. We
present the first necessary and sufficient combinatorial conditions for
matrices of arbitrary rank to be identifiable from a set of matrix entries,
yielding theoretical... | computer science |
10,682 | Estimation of Simultaneously Sparse and Low Rank Matrices | cs.DS | The paper introduces a penalized matrix estimation procedure aiming at
solutions which are sparse and low-rank at the same time. Such structures arise
in the context of social networks or protein interactions where underlying
graphs have adjacency matrices which are block-diagonal in the appropriate
basis. We introduce... | computer science |
10,683 | An Adaptive Algorithm for Finite Stochastic Partial Monitoring | cs.LG | We present a new anytime algorithm that achieves near-optimal regret for any
instance of finite stochastic partial monitoring. In particular, the new
algorithm achieves the minimax regret, within logarithmic factors, for both
"easy" and "hard" problems. For easy problems, it additionally achieves
logarithmic individual... | computer science |
10,684 | The Nonparanormal SKEPTIC | stat.ME | We propose a semiparametric approach, named nonparanormal skeptic, for
estimating high dimensional undirected graphical models. In terms of modeling,
we consider the nonparanormal family proposed by Liu et al (2009). In terms of
estimation, we exploit nonparametric rank-based correlation coefficient
estimators includin... | computer science |
10,685 | Gene Expression Time Course Clustering with Countably Infinite Hidden
Markov Models | cs.LG | Most existing approaches to clustering gene expression time course data treat
the different time points as independent dimensions and are invariant to
permutations, such as reversal, of the experimental time course. Approaches
utilizing HMMs have been shown to be helpful in this regard, but are hampered
by having to ch... | computer science |
10,686 | Matrix Tile Analysis | cs.LG | Many tasks require finding groups of elements in a matrix of numbers, symbols
or class likelihoods. One approach is to use efficient bi- or tri-linear
factorization techniques including PCA, ICA, sparse matrix factorization and
plaid analysis. These techniques are not appropriate when addition and
multiplication of mat... | computer science |
10,687 | Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick
Breaking Representation | stat.ME | Nonparametric Bayesian approaches to clustering, information retrieval,
language modeling and object recognition have recently shown great promise as a
new paradigm for unsupervised data analysis. Most contributions have focused on
the Dirichlet process mixture models or extensions thereof for which efficient
Gibbs sam... | computer science |
10,688 | Faster Gaussian Summation: Theory and Experiment | cs.LG | We provide faster algorithms for the problem of Gaussian summation, which
occurs in many machine learning methods. We develop two new extensions - an
O(Dp) Taylor expansion for the Gaussian kernel with rigorous error bounds and a
new error control scheme integrating any arbitrary approximation method -
within the best ... | computer science |
10,689 | A Hybrid Method for Distance Metric Learning | cs.LG | We consider the problem of learning a measure of distance among vectors in a
feature space and propose a hybrid method that simultaneously learns from
similarity ratings assigned to pairs of vectors and class labels assigned to
individual vectors. Our method is based on a generative model in which class
labels can prov... | computer science |
10,690 | Message-Passing Algorithms for Quadratic Minimization | cs.IT | Gaussian belief propagation (GaBP) is an iterative algorithm for computing
the mean of a multivariate Gaussian distribution, or equivalently, the minimum
of a multivariate positive definite quadratic function. Sufficient conditions,
such as walk-summability, that guarantee the convergence and correctness of
GaBP are kn... | computer science |
10,691 | Hypergraph and protein function prediction with gene expression data | stat.ML | Most network-based protein (or gene) function prediction methods are based on
the assumption that the labels of two adjacent proteins in the network are
likely to be the same. However, assuming the pairwise relationship between
proteins or genes is not complete, the information a group of genes that show
very similar p... | computer science |
10,692 | Nonparametric risk bounds for time-series forecasting | math.ST | We derive generalization error bounds for traditional time-series forecasting
models. Our results hold for many standard forecasting tools including
autoregressive models, moving average models, and, more generally, linear
state-space models. These non-asymptotic bounds need only weak assumptions on
the data-generating... | computer science |
10,693 | Low-rank Matrix Completion using Alternating Minimization | stat.ML | Alternating minimization represents a widely applicable and empirically
successful approach for finding low-rank matrices that best fit the given data.
For example, for the problem of low-rank matrix completion, this method is
believed to be one of the most accurate and efficient, and formed a major
component of the wi... | computer science |
10,694 | Evaluating Classifiers Without Expert Labels | cs.LG | This paper considers the challenge of evaluating a set of classifiers, as
done in shared task evaluations like the KDD Cup or NIST TREC, without expert
labels. While expert labels provide the traditional cornerstone for evaluating
statistical learners, limited or expensive access to experts represents a
practical bottl... | computer science |
10,695 | Using Wikipedia to Boost SVD Recommender Systems | cs.LG | Singular Value Decomposition (SVD) has been used successfully in recent years
in the area of recommender systems. In this paper we present how this model can
be extended to consider both user ratings and information from Wikipedia. By
mapping items to Wikipedia pages and quantifying their similarity, we are able
to use... | computer science |
10,696 | Stochastic Gradient Descent for Non-smooth Optimization: Convergence
Results and Optimal Averaging Schemes | cs.LG | Stochastic Gradient Descent (SGD) is one of the simplest and most popular
stochastic optimization methods. While it has already been theoretically
studied for decades, the classical analysis usually required non-trivial
smoothness assumptions, which do not apply to many modern applications of SGD
with non-smooth object... | computer science |
10,697 | A simpler approach to obtaining an O(1/t) convergence rate for the
projected stochastic subgradient method | cs.LG | In this note, we present a new averaging technique for the projected
stochastic subgradient method. By using a weighted average with a weight of t+1
for each iterate w_t at iteration t, we obtain the convergence rate of O(1/t)
with both an easy proof and an easy implementation. The new scheme is compared
empirically to... | computer science |
10,698 | Active Collaborative Filtering | cs.IR | Collaborative filtering (CF) allows the preferences of multiple users to be
pooled to make recommendations regarding unseen products. We consider in this
paper the problem of online and interactive CF: given the current ratings
associated with a user, what queries (new ratings) would most improve the
quality of the rec... | computer science |
10,699 | A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical
Models for Marginal Independence | stat.ME | Graphical models with bi-directed edges (<->) represent marginal
independence: the absence of an edge between two vertices indicates that the
corresponding variables are marginally independent. In this paper, we consider
maximum likelihood estimation in the case of continuous variables with a
Gaussian joint distributio... | computer science |
10,700 | Collaborative Ensemble Learning: Combining Collaborative and
Content-Based Information Filtering via Hierarchical Bayes | cs.LG | Collaborative filtering (CF) and content-based filtering (CBF) have widely
been used in information filtering applications. Both approaches have their
strengths and weaknesses which is why researchers have developed hybrid
systems. This paper proposes a novel approach to unify CF and CBF in a
probabilistic framework, n... | computer science |
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