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11,301 | Sparse PCA via Bipartite Matchings | stat.ML | We consider the following multi-component sparse PCA problem: given a set of
data points, we seek to extract a small number of sparse components with
disjoint supports that jointly capture the maximum possible variance. These
components can be computed one by one, repeatedly solving the single-component
problem and def... | computer science |
11,302 | Bayesian mixtures of spatial spline regressions | stat.ME | This work relates the framework of model-based clustering for spatial
functional data where the data are surfaces. We first introduce a Bayesian
spatial spline regression model with mixed-effects (BSSR) for modeling spatial
function data. The BSSR model is based on Nodal basis functions for spatial
regression and accom... | computer science |
11,303 | An End-to-End Neural Network for Polyphonic Piano Music Transcription | stat.ML | We present a supervised neural network model for polyphonic piano music
transcription. The architecture of the proposed model is analogous to speech
recognition systems and comprises an acoustic model and a music language model.
The acoustic model is a neural network used for estimating the probabilities of
pitches in ... | computer science |
11,304 | A variational approach to the consistency of spectral clustering | math.ST | This paper establishes the consistency of spectral approaches to data
clustering. We consider clustering of point clouds obtained as samples of a
ground-truth measure. A graph representing the point cloud is obtained by
assigning weights to edges based on the distance between the points they
connect. We investigate the... | computer science |
11,305 | A Randomized Rounding Algorithm for Sparse PCA | cs.DS | We present and analyze a simple, two-step algorithm to approximate the
optimal solution of the sparse PCA problem. Our approach first solves a L1
penalized version of the NP-hard sparse PCA optimization problem and then uses
a randomized rounding strategy to sparsify the resulting dense solution. Our
main theoretical r... | computer science |
11,306 | Introduction to Cross-Entropy Clustering The R Package CEC | cs.LG | The R Package CEC performs clustering based on the cross-entropy clustering
(CEC) method, which was recently developed with the use of information theory.
The main advantage of CEC is that it combines the speed and simplicity of
$k$-means with the ability to use various Gaussian mixture models and reduce
unnecessary cl... | computer science |
11,307 | Time Series Clustering via Community Detection in Networks | stat.ML | In this paper, we propose a technique for time series clustering using
community detection in complex networks. Firstly, we present a method to
transform a set of time series into a network using different distance
functions, where each time series is represented by a vertex and the most
similar ones are connected. The... | computer science |
11,308 | Review and Perspective for Distance Based Trajectory Clustering | stat.ML | In this paper we tackle the issue of clustering trajectories of geolocalized
observations. Using clustering technics based on the choice of a distance
between the observations, we first provide a comprehensive review of the
different distances used in the literature to compare trajectories. Then based
on the limitation... | computer science |
11,309 | A Deep Bag-of-Features Model for Music Auto-Tagging | cs.LG | Feature learning and deep learning have drawn great attention in recent years
as a way of transforming input data into more effective representations using
learning algorithms. Such interest has grown in the area of music information
retrieval (MIR) as well, particularly in music audio classification tasks such
as auto... | computer science |
11,310 | AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization | stat.ML | We study distributed stochastic convex optimization under the delayed
gradient model where the server nodes perform parameter updates, while the
worker nodes compute stochastic gradients. We discuss, analyze, and experiment
with a setup motivated by the behavior of real-world distributed computation
networks, where the... | computer science |
11,311 | A review of homomorphic encryption and software tools for encrypted
statistical machine learning | stat.ML | Recent advances in cryptography promise to enable secure statistical
computation on encrypted data, whereby a limited set of operations can be
carried out without the need to first decrypt. We review these homomorphic
encryption schemes in a manner accessible to statisticians and machine
learners, focusing on pertinent... | computer science |
11,312 | Encrypted statistical machine learning: new privacy preserving methods | stat.ML | We present two new statistical machine learning methods designed to learn on
fully homomorphic encrypted (FHE) data. The introduction of FHE schemes
following Gentry (2009) opens up the prospect of privacy preserving statistical
machine learning analysis and modelling of encrypted data without compromising
security con... | computer science |
11,313 | Compressive Sensing via Low-Rank Gaussian Mixture Models | stat.ML | We develop a new compressive sensing (CS) inversion algorithm by utilizing
the Gaussian mixture model (GMM). While the compressive sensing is performed
globally on the entire image as implemented in our lensless camera, a low-rank
GMM is imposed on the local image patches. This low-rank GMM is derived via
eigenvalue th... | computer science |
11,314 | Directional Decision Lists | stat.ML | In this paper we introduce a novel family of decision lists consisting of
highly interpretable models which can be learned efficiently in a greedy
manner. The defining property is that all rules are oriented in the same
direction. Particular examples of this family are decision lists with
monotonically decreasing (or i... | computer science |
11,315 | Multi-Sensor Slope Change Detection | stat.ML | We develop a mixture procedure for multi-sensor systems to monitor data
streams for a change-point that causes a gradual degradation to a subset of the
streams. Observations are assumed to be initially normal random variables with
known constant means and variances. After the change-point, observations in the
subset wi... | computer science |
11,316 | Online Supervised Subspace Tracking | cs.LG | We present a framework for supervised subspace tracking, when there are two
time series $x_t$ and $y_t$, one being the high-dimensional predictors and the
other being the response variables and the subspace tracking needs to take into
consideration of both sequences. It extends the classic online subspace
tracking work... | computer science |
11,317 | Train faster, generalize better: Stability of stochastic gradient
descent | cs.LG | We show that parametric models trained by a stochastic gradient method (SGM)
with few iterations have vanishing generalization error. We prove our results
by arguing that SGM is algorithmically stable in the sense of Bousquet and
Elisseeff. Our analysis only employs elementary tools from convex and
continuous optimizat... | computer science |
11,318 | Predicting SLA Violations in Real Time using Online Machine Learning | cs.NI | Detecting faults and SLA violations in a timely manner is critical for
telecom providers, in order to avoid loss in business, revenue and reputation.
At the same time predicting SLA violations for user services in telecom
environments is difficult, due to time-varying user demands and infrastructure
load conditions.
... | computer science |
11,319 | Fast low-rank estimation by projected gradient descent: General
statistical and algorithmic guarantees | math.ST | Optimization problems with rank constraints arise in many applications,
including matrix regression, structured PCA, matrix completion and matrix
decomposition problems. An attractive heuristic for solving such problems is to
factorize the low-rank matrix, and to run projected gradient descent on the
nonconvex factoriz... | computer science |
11,320 | Performance Bounds for Pairwise Entity Resolution | stat.ML | One significant challenge to scaling entity resolution algorithms to massive
datasets is understanding how performance changes after moving beyond the realm
of small, manually labeled reference datasets. Unlike traditional machine
learning tasks, when an entity resolution algorithm performs well on small
hold-out datas... | computer science |
11,321 | Dynamic Poisson Factorization | cs.LG | Models for recommender systems use latent factors to explain the preferences
and behaviors of users with respect to a set of items (e.g., movies, books,
academic papers). Typically, the latent factors are assumed to be static and,
given these factors, the observed preferences and behaviors of users are
assumed to be ge... | computer science |
11,322 | Revealed Preference at Scale: Learning Personalized Preferences from
Assortment Choices | stat.ML | We consider the problem of learning the preferences of a heterogeneous
population by observing choices from an assortment of products, ads, or other
offerings. Our observation model takes a form common in assortment planning
applications: each arriving customer is offered an assortment consisting of a
subset of all pos... | computer science |
11,323 | Significance Analysis of High-Dimensional, Low-Sample Size Partially
Labeled Data | stat.ML | Classification and clustering are both important topics in statistical
learning. A natural question herein is whether predefined classes are really
different from one another, or whether clusters are really there. Specifically,
we may be interested in knowing whether the two classes defined by some class
labels (when t... | computer science |
11,324 | Efficient Neighborhood Selection for Gaussian Graphical Models | stat.ML | This paper addresses the problem of neighborhood selection for Gaussian
graphical models. We present two heuristic algorithms: a forward-backward
greedy algorithm for general Gaussian graphical models based on mutual
information test, and a threshold-based algorithm for walk summable Gaussian
graphical models. Both alg... | computer science |
11,325 | Fast k-NN search | stat.ML | Efficient index structures for fast approximate nearest neighbor queries are
required in many applications such as recommendation systems. In
high-dimensional spaces, many conventional methods suffer from excessive usage
of memory and slow response times. We propose a method where multiple random
projection trees are c... | computer science |
11,326 | Evasion and Hardening of Tree Ensemble Classifiers | cs.LG | Classifier evasion consists in finding for a given instance $x$ the nearest
instance $x'$ such that the classifier predictions of $x$ and $x'$ are
different. We present two novel algorithms for systematically computing
evasions for tree ensembles such as boosted trees and random forests. Our first
algorithm uses a Mixe... | computer science |
11,327 | Convergence of Stochastic Gradient Descent for PCA | cs.LG | We consider the problem of principal component analysis (PCA) in a streaming
stochastic setting, where our goal is to find a direction of approximate
maximal variance, based on a stream of i.i.d. data points in $\reals^d$. A
simple and computationally cheap algorithm for this is stochastic gradient
descent (SGD), which... | computer science |
11,328 | Learning From Missing Data Using Selection Bias in Movie Recommendation | stat.ML | Recommending items to users is a challenging task due to the large amount of
missing information. In many cases, the data solely consist of ratings or tags
voluntarily contributed by each user on a very limited subset of the available
items, so that most of the data of potential interest is actually missing.
Current ap... | computer science |
11,329 | Fast Discrete Distribution Clustering Using Wasserstein Barycenter with
Sparse Support | stat.CO | In a variety of research areas, the weighted bag of vectors and the histogram
are widely used descriptors for complex objects. Both can be expressed as
discrete distributions. D2-clustering pursues the minimum total within-cluster
variation for a set of discrete distributions subject to the
Kantorovich-Wasserstein metr... | computer science |
11,330 | Distributed Parameter Map-Reduce | cs.DC | This paper describes how to convert a machine learning problem into a series
of map-reduce tasks. We study logistic regression algorithm. In logistic
regression algorithm, it is assumed that samples are independent and each
sample is assigned a probability. Parameters are obtained by maxmizing the
product of all sample... | computer science |
11,331 | Learning in Unlabeled Networks - An Active Learning and Inference
Approach | stat.ML | The task of determining labels of all network nodes based on the knowledge
about network structure and labels of some training subset of nodes is called
the within-network classification. It may happen that none of the labels of the
nodes is known and additionally there is no information about number of classes
to whic... | computer science |
11,332 | Toward a Better Understanding of Leaderboard | stat.ML | The leaderboard in machine learning competitions is a tool to show the
performance of various participants and to compare them. However, the
leaderboard quickly becomes no longer accurate, due to hack or overfitting.
This article gives two pieces of advice to prevent easy hack or overfitting. By
following these advice,... | computer science |
11,333 | The intrinsic value of HFO features as a biomarker of epileptic activity | cs.LG | High frequency oscillations (HFOs) are a promising biomarker of epileptic
brain tissue and activity. HFOs additionally serve as a prototypical example of
challenges in the analysis of discrete events in high-temporal resolution,
intracranial EEG data. Two primary challenges are 1) dimensionality reduction,
and 2) asses... | computer science |
11,334 | On Equivalence of Martingale Tail Bounds and Deterministic Regret
Inequalities | math.PR | We study an equivalence of (i) deterministic pathwise statements appearing in
the online learning literature (termed \emph{regret bounds}), (ii)
high-probability tail bounds for the supremum of a collection of martingales
(of a specific form arising from uniform laws of large numbers for
martingales), and (iii) in-expe... | computer science |
11,335 | A Bayesian Network Model for Interesting Itemsets | stat.ML | Mining itemsets that are the most interesting under a statistical model of
the underlying data is a commonly used and well-studied technique for
exploratory data analysis, with the most recent interestingness models
exhibiting state of the art performance. Continuing this highly promising line
of work, we propose the f... | computer science |
11,336 | Group-Invariant Subspace Clustering | cs.IT | In this paper we consider the problem of group invariant subspace clustering
where the data is assumed to come from a union of group-invariant subspaces of
a vector space, i.e. subspaces which are invariant with respect to action of a
given group. Algebraically, such group-invariant subspaces are also referred to
as su... | computer science |
11,337 | Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse
Perturbations | cs.LG | Robust tensor CP decomposition involves decomposing a tensor into low rank
and sparse components. We propose a novel non-convex iterative algorithm with
guaranteed recovery. It alternates between low-rank CP decomposition through
gradient ascent (a variant of the tensor power method), and hard thresholding
of the resid... | computer science |
11,338 | A cost function for similarity-based hierarchical clustering | cs.DS | The development of algorithms for hierarchical clustering has been hampered
by a shortage of precise objective functions. To help address this situation,
we introduce a simple cost function on hierarchies over a set of points, given
pairwise similarities between those points. We show that this criterion behaves
sensibl... | computer science |
11,339 | Piecewise-Linear Approximation for Feature Subset Selection in a
Sequential Logit Model | stat.ME | This paper concerns a method of selecting a subset of features for a
sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer
quadratic optimization formulation for solving the problem based on a quadratic
approximation of the logistic loss function. However, since there is a
significant gap between ... | computer science |
11,340 | Stochastically Transitive Models for Pairwise Comparisons: Statistical
and Computational Issues | stat.ML | There are various parametric models for analyzing pairwise comparison data,
including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance
on strong parametric assumptions is limiting. In this work, we study a flexible
model for pairwise comparisons, under which the probabilities of outcomes are
requir... | computer science |
11,341 | Optimal Cluster Recovery in the Labeled Stochastic Block Model | math.PR | We consider the problem of community detection or clustering in the labeled
Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes
linearly growing with the global population of items $n$. Every pair of items
is labeled independently at random, and label $\ell$ appears with probability
$p(i,j,\ell)... | computer science |
11,342 | Regularization vs. Relaxation: A conic optimization perspective of
statistical variable selection | cs.LG | Variable selection is a fundamental task in statistical data analysis.
Sparsity-inducing regularization methods are a popular class of methods that
simultaneously perform variable selection and model estimation. The central
problem is a quadratic optimization problem with an l0-norm penalty. Exactly
enforcing the l0-no... | computer science |
11,343 | Learning-based Compressive Subsampling | cs.IT | The problem of recovering a structured signal $\mathbf{x} \in \mathbb{C}^p$
from a set of dimensionality-reduced linear measurements $\mathbf{b} = \mathbf
{A}\mathbf {x}$ arises in a variety of applications, such as medical imaging,
spectroscopy, Fourier optics, and computerized tomography. Due to computational
and sto... | computer science |
11,344 | Application of Quantum Annealing to Training of Deep Neural Networks | cs.LG | In Deep Learning, a well-known approach for training a Deep Neural Network
starts by training a generative Deep Belief Network model, typically using
Contrastive Divergence (CD), then fine-tuning the weights using backpropagation
or other discriminative techniques. However, the generative training can be
time-consuming... | computer science |
11,345 | Generalized conditional gradient: analysis of convergence and
applications | cs.LG | The objectives of this technical report is to provide additional results on
the generalized conditional gradient methods introduced by Bredies et al.
[BLM05]. Indeed , when the objective function is smooth, we provide a novel
certificate of optimality and we show that the algorithm has a linear
convergence rate. Applic... | computer science |
11,346 | Collective Prediction of Individual Mobility Traces with Exponential
Weights | cs.CY | We present and test a sequential learning algorithm for the short-term
prediction of human mobility. This novel approach pairs the Exponential Weights
forecaster with a very large ensemble of experts. The experts are individual
sequence prediction algorithms constructed from the mobility traces of 10
million roaming mo... | computer science |
11,347 | On the complexity of switching linear regression | stat.ML | This technical note extends recent results on the computational complexity of
globally minimizing the error of piecewise-affine models to the related problem
of minimizing the error of switching linear regression models. In particular,
we show that, on the one hand the problem is NP-hard, but on the other hand, it
admi... | computer science |
11,348 | Modeling User Exposure in Recommendation | stat.ML | Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings, all the items, including the ones
that a user did not consume, are taken into consideration. But this assumption
does not acc... | computer science |
11,349 | Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a
Convergence Guarantee | stat.ML | Frank-Wolfe (FW) algorithms have been often proposed over the last few years
as efficient solvers for a variety of optimization problems arising in the
field of Machine Learning. The ability to work with cheap projection-free
iterations and the incremental nature of the method make FW a very effective
choice for many l... | computer science |
11,350 | Statistically efficient thinning of a Markov chain sampler | stat.CO | It is common to subsample Markov chain output to reduce the storage burden.
Geyer (1992) shows that discarding $k-1$ out of every $k$ observations will not
improve statistical efficiency, as quantified through variance in a given
computational budget. That observation is often taken to mean that thinning
MCMC output ca... | computer science |
11,351 | WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet
Allocation | stat.ML | Developing efficient and scalable algorithms for Latent Dirichlet Allocation
(LDA) is of wide interest for many applications. Previous work has developed an
O(1) Metropolis-Hastings sampling method for each token. However, the
performance is far from being optimal due to random accesses to the parameter
matrices and fr... | computer science |
11,352 | Principal Differences Analysis: Interpretable Characterization of
Differences between Distributions | stat.ML | We introduce principal differences analysis (PDA) for analyzing differences
between high-dimensional distributions. The method operates by finding the
projection that maximizes the Wasserstein divergence between the resulting
univariate populations. Relying on the Cramer-Wold device, it requires no
assumptions about th... | computer science |
11,353 | Faster Stochastic Variational Inference using Proximal-Gradient Methods
with General Divergence Functions | stat.ML | Several recent works have explored stochastic gradient methods for
variational inference that exploit the geometry of the variational-parameter
space. However, the theoretical properties of these methods are not
well-understood and these methods typically only apply to
conditionally-conjugate models. We present a new s... | computer science |
11,354 | An Impossibility Result for Reconstruction in a Degree-Corrected
Planted-Partition Model | math.PR | We consider a Degree-Corrected Planted-Partition model: a random graph on $n$
nodes with two asymptotically equal-sized clusters. The model parameters are
two constants $a,b > 0$ and an i.i.d. sequence of weights $(\phi_u)_{u=1}^n$,
with finite second moment $\Phi^{(2)}$. Vertices $u$ and $v$ are joined by an
edge with... | computer science |
11,355 | adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs | cs.LG | Recurrent Neural Networks (RNNs) are powerful models that achieve exceptional
performance on several pattern recognition problems. However, the training of
RNNs is a computationally difficult task owing to the well-known
"vanishing/exploding" gradient problem. Algorithms proposed for training RNNs
either exploit no (or... | computer science |
11,356 | Study of a bias in the offline evaluation of a recommendation algorithm | cs.IR | Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical... | computer science |
11,357 | Co-Clustering Network-Constrained Trajectory Data | stat.ML | Recently, clustering moving object trajectories kept gaining interest from
both the data mining and machine learning communities. This problem, however,
was studied mainly and extensively in the setting where moving objects can move
freely on the euclidean space. In this paper, we study the problem of
clustering trajec... | computer science |
11,358 | Interpretable classifiers using rules and Bayesian analysis: Building a
better stroke prediction model | stat.AP | We aim to produce predictive models that are not only accurate, but are also
interpretable to human experts. Our models are decision lists, which consist of
a series of if...then... statements (e.g., if high blood pressure, then stroke)
that discretize a high-dimensional, multivariate feature space into a series of
sim... | computer science |
11,359 | Stop Wasting My Gradients: Practical SVRG | cs.LG | We present and analyze several strategies for improving the performance of
stochastic variance-reduced gradient (SVRG) methods. We first show that the
convergence rate of these methods can be preserved under a decreasing sequence
of errors in the control variate, and use this to derive variants of SVRG that
use growing... | computer science |
11,360 | Barrier Frank-Wolfe for Marginal Inference | stat.ML | We introduce a globally-convergent algorithm for optimizing the
tree-reweighted (TRW) variational objective over the marginal polytope. The
algorithm is based on the conditional gradient method (Frank-Wolfe) and moves
pseudomarginals within the marginal polytope through repeated maximum a
posteriori (MAP) calls. This m... | computer science |
11,361 | Hierarchical Variational Models | stat.ML | Black box variational inference allows researchers to easily prototype and
evaluate an array of models. Recent advances allow such algorithms to scale to
high dimensions. However, a central question remains: How to specify an
expressive variational distribution that maintains efficient computation? To
address this, we ... | computer science |
11,362 | Speed learning on the fly | math.OC | The practical performance of online stochastic gradient descent algorithms is
highly dependent on the chosen step size, which must be tediously hand-tuned in
many applications. The same is true for more advanced variants of stochastic
gradients, such as SAGA, SVRG, or AdaGrad. Here we propose to adapt the step
size by ... | computer science |
11,363 | Sandwiching the marginal likelihood using bidirectional Monte Carlo | stat.ML | Computing the marginal likelihood (ML) of a model requires marginalizing out
all of the parameters and latent variables, a difficult high-dimensional
summation or integration problem. To make matters worse, it is often hard to
measure the accuracy of one's ML estimates. We present bidirectional Monte
Carlo, a technique... | computer science |
11,364 | Online Principal Component Analysis in High Dimension: Which Algorithm
to Choose? | stat.ML | In the current context of data explosion, online techniques that do not
require storing all data in memory are indispensable to routinely perform tasks
like principal component analysis (PCA). Recursive algorithms that update the
PCA with each new observation have been studied in various fields of research
and found wi... | computer science |
11,365 | Random Multi-Constraint Projection: Stochastic Gradient Methods for
Convex Optimization with Many Constraints | stat.ML | Consider convex optimization problems subject to a large number of
constraints. We focus on stochastic problems in which the objective takes the
form of expected values and the feasible set is the intersection of a large
number of convex sets. We propose a class of algorithms that perform both
stochastic gradient desce... | computer science |
11,366 | Learning Nonparametric Forest Graphical Models with Prior Information | stat.ME | We present a framework for incorporating prior information into nonparametric
estimation of graphical models. To avoid distributional assumptions, we
restrict the graph to be a forest and build on the work of forest density
estimation (FDE). We reformulate the FDE approach from a Bayesian perspective,
and introduce pri... | computer science |
11,367 | Bayesian Analysis of Dynamic Linear Topic Models | stat.ML | In dynamic topic modeling, the proportional contribution of a topic to a
document depends on the temporal dynamics of that topic's overall prevalence in
the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by
explicitly modeling document level topic proportions with covariates and
dynamic structure... | computer science |
11,368 | Block-diagonal covariance selection for high-dimensional Gaussian
graphical models | math.ST | Gaussian graphical models are widely utilized to infer and visualize networks
of dependencies between continuous variables. However, inferring the graph is
difficult when the sample size is small compared to the number of variables. To
reduce the number of parameters to estimate in the model, we propose a
non-asymptoti... | computer science |
11,369 | Neuroprosthetic decoder training as imitation learning | stat.ML | Neuroprosthetic brain-computer interfaces function via an algorithm which
decodes neural activity of the user into movements of an end effector, such as
a cursor or robotic arm. In practice, the decoder is often learned by updating
its parameters while the user performs a task. When the user's intention is not
directly... | computer science |
11,370 | Handling Class Imbalance in Link Prediction using Learning to Rank
Techniques | stat.ML | We consider the link prediction problem in a partially observed network,
where the objective is to make predictions in the unobserved portion of the
network. Many existing methods reduce link prediction to binary classification
problem. However, the dominance of absent links in real world networks makes
misclassificati... | computer science |
11,371 | Causal interpretation rules for encoding and decoding models in
neuroimaging | stat.ML | Causal terminology is often introduced in the interpretation of encoding and
decoding models trained on neuroimaging data. In this article, we investigate
which causal statements are warranted and which ones are not supported by
empirical evidence. We argue that the distinction between encoding and decoding
models is n... | computer science |
11,372 | Random sampling of bandlimited signals on graphs | cs.SI | We study the problem of sampling k-bandlimited signals on graphs. We propose
two sampling strategies that consist in selecting a small subset of nodes at
random. The first strategy is non-adaptive, i.e., independent of the graph
structure, and its performance depends on a parameter called the graph
coherence. On the co... | computer science |
11,373 | Extending Gossip Algorithms to Distributed Estimation of U-Statistics | stat.ML | Efficient and robust algorithms for decentralized estimation in networks are
essential to many distributed systems. Whereas distributed estimation of sample
mean statistics has been the subject of a good deal of attention, computation
of $U$-statistics, relying on more expensive averaging over pairs of
observations, is... | computer science |
11,374 | Online learning in repeated auctions | cs.GT | Motivated by online advertising auctions, we consider repeated Vickrey
auctions where goods of unknown value are sold sequentially and bidders only
learn (potentially noisy) information about a good's value once it is
purchased. We adopt an online learning approach with bandit feedback to model
this problem and derive ... | computer science |
11,375 | On the Global Linear Convergence of Frank-Wolfe Optimization Variants | math.OC | The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity
thanks in particular to its ability to nicely handle the structured constraints
appearing in machine learning applications. However, its convergence rate is
known to be slow (sublinear) when the solution lies at the boundary. A simple
less-know... | computer science |
11,376 | Regret Analysis of the Finite-Horizon Gittins Index Strategy for
Multi-Armed Bandits | cs.LG | I analyse the frequentist regret of the famous Gittins index strategy for
multi-armed bandits with Gaussian noise and a finite horizon. Remarkably it
turns out that this approach leads to finite-time regret guarantees comparable
to those available for the popular UCB algorithm. Along the way I derive
finite-time bounds... | computer science |
11,377 | A Novel Approach for Phase Identification in Smart Grids Using Graph
Theory and Principal Component Analysis | cs.LG | Consumers with low demand, like households, are generally supplied
single-phase power by connecting their service mains to one of the phases of a
distribution transformer. The distribution companies face the problem of
keeping a record of consumer connectivity to a phase due to uninformed changes
that happen. The exact... | computer science |
11,378 | Diffusion Representations | stat.ML | Diffusion Maps framework is a kernel based method for manifold learning and
data analysis that defines diffusion similarities by imposing a Markovian
process on the given dataset. Analysis by this process uncovers the intrinsic
geometric structures in the data. Recently, it was suggested to replace the
standard kernel ... | computer science |
11,379 | Bayesian inference via rejection filtering | cs.LG | We provide a method for approximating Bayesian inference using rejection
sampling. We not only make the process efficient, but also dramatically reduce
the memory required relative to conventional methods by combining rejection
sampling with particle filtering. We also provide an approximate form of
rejection sampling ... | computer science |
11,380 | Private Posterior distributions from Variational approximations | stat.ML | Privacy preserving mechanisms such as differential privacy inject additional
randomness in the form of noise in the data, beyond the sampling mechanism.
Ignoring this additional noise can lead to inaccurate and invalid inferences.
In this paper, we incorporate the privacy mechanism explicitly into the
likelihood functi... | computer science |
11,381 | Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single
Agent Case | stat.ML | In this work and the supporting Part II, we examine the performance of
stochastic sub-gradient learning strategies under weaker conditions than
usually considered in the literature. The new conditions are shown to be
automatically satisfied by several important cases of interest including SVM,
LASSO, and Total-Variatio... | computer science |
11,382 | Random Forests for Big Data | stat.ML | Big Data is one of the major challenges of statistical science and has
numerous consequences from algorithmic and theoretical viewpoints. Big Data
always involve massive data but they also often include online data and data
heterogeneity. Recently some statistical methods have been adapted to process
Big Data, like lin... | computer science |
11,383 | Algorithms for Differentially Private Multi-Armed Bandits | stat.ML | We present differentially private algorithms for the stochastic Multi-Armed
Bandit (MAB) problem. This is a problem for applications such as adaptive
clinical trials, experiment design, and user-targeted advertising where private
information is connected to individual rewards. Our major contribution is to
show that the... | computer science |
11,384 | Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression | math.ST | We study a family of regularized score-based estimators for learning the
structure of a directed acyclic graph (DAG) for a multivariate normal
distribution from high-dimensional data with $p\gg n$. Our main results
establish support recovery guarantees and deviation bounds for a family of
penalized least-squares estima... | computer science |
11,385 | Proximal gradient method for huberized support vector machine | stat.ML | The Support Vector Machine (SVM) has been used in a wide variety of
classification problems. The original SVM uses the hinge loss function, which
is non-differentiable and makes the problem difficult to solve in particular
for regularized SVMs, such as with $\ell_1$-regularization. This paper
considers the Huberized SV... | computer science |
11,386 | Fast Low-Rank Matrix Learning with Nonconvex Regularization | cs.NA | Low-rank modeling has a lot of important applications in machine learning,
computer vision and social network analysis. While the matrix rank is often
approximated by the convex nuclear norm, the use of nonconvex low-rank
regularizers has demonstrated better recovery performance. However, the
resultant optimization pro... | computer science |
11,387 | Variance Reduction for Distributed Stochastic Gradient Descent | cs.LG | Variance reduction (VR) methods boost the performance of stochastic gradient
descent (SGD) by enabling the use of larger, constant stepsizes and preserving
linear convergence rates. However, current variance reduced SGD methods require
either high memory usage or an exact gradient computation (using the entire
dataset)... | computer science |
11,388 | Fast spectral algorithms from sum-of-squares proofs: tensor
decomposition and planted sparse vectors | cs.DS | We consider two problems that arise in machine learning applications: the
problem of recovering a planted sparse vector in a random linear subspace and
the problem of decomposing a random low-rank overcomplete 3-tensor. For both
problems, the best known guarantees are based on the sum-of-squares method. We
develop new ... | computer science |
11,389 | Distributed Training of Deep Neural Networks with Theoretical Analysis:
Under SSP Setting | stat.ML | We propose a distributed approach to train deep neural networks (DNNs), which
has guaranteed convergence theoretically and great scalability empirically:
close to 6 times faster on instance of ImageNet data set when run with 6
machines. The proposed scheme is close to optimally scalable in terms of number
of machines, ... | computer science |
11,390 | Efficient Distributed SGD with Variance Reduction | cs.LG | Stochastic Gradient Descent (SGD) has become one of the most popular
optimization methods for training machine learning models on massive datasets.
However, SGD suffers from two main drawbacks: (i) The noisy gradient updates
have high variance, which slows down convergence as the iterates approach the
optimum, and (ii)... | computer science |
11,391 | A Unified Approach to Error Bounds for Structured Convex Optimization
Problems | math.OC | Error bounds, which refer to inequalities that bound the distance of vectors
in a test set to a given set by a residual function, have proven to be
extremely useful in analyzing the convergence rates of a host of iterative
methods for solving optimization problems. In this paper, we present a new
framework for establis... | computer science |
11,392 | Active Sampler: Light-weight Accelerator for Complex Data Analytics at
Scale | cs.DB | Recent years have witnessed amazing outcomes from "Big Models" trained by
"Big Data". Most popular algorithms for model training are iterative. Due to
the surging volumes of data, we can usually afford to process only a fraction
of the training data in each iteration. Typically, the data are either
uniformly sampled or... | computer science |
11,393 | Quantum assisted Gaussian process regression | cs.LG | Gaussian processes (GP) are a widely used model for regression problems in
supervised machine learning. Implementation of GP regression typically requires
$O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow
et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process
regre... | computer science |
11,394 | Fighting Bandits with a New Kind of Smoothness | cs.LG | We define a novel family of algorithms for the adversarial multi-armed bandit
problem, and provide a simple analysis technique based on convex smoothing. We
prove two main results. First, we show that regularization via the
\emph{Tsallis entropy}, which includes EXP3 as a special case, achieves the
$\Theta(\sqrt{TN})$ ... | computer science |
11,395 | Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction | math.OC | Statistical image reconstruction (SIR) methods are studied extensively for
X-ray computed tomography (CT) due to the potential of acquiring CT scans with
reduced X-ray dose while maintaining image quality. However, the longer
reconstruction time of SIR methods hinders their use in X-ray CT in practice.
To accelerate st... | computer science |
11,396 | Causal and anti-causal learning in pattern recognition for neuroimaging | stat.ML | Pattern recognition in neuroimaging distinguishes between two types of
models: encoding- and decoding models. This distinction is based on the insight
that brain state features, that are found to be relevant in an experimental
paradigm, carry a different meaning in encoding- than in decoding models. In
this paper, we a... | computer science |
11,397 | Data Driven Resource Allocation for Distributed Learning | cs.LG | In distributed machine learning, data is dispatched to multiple machines for
processing. Motivated by the fact that similar data points often belong to the
same or similar classes, and more generally, classification rules of high
accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou
1993), we propo... | computer science |
11,398 | Streaming Kernel Principal Component Analysis | cs.DS | Kernel principal component analysis (KPCA) provides a concise set of basis
vectors which capture non-linear structures within large data sets, and is a
central tool in data analysis and learning. To allow for non-linear relations,
typically a full $n \times n$ kernel matrix is constructed over $n$ data
points, but this... | computer science |
11,399 | Deep Poisson Factorization Machines: factor analysis for mapping
behaviors in journalist ecosystem | cs.CY | Newsroom in online ecosystem is difficult to untangle. With prevalence of
social media, interactions between journalists and individuals become visible,
but lack of understanding to inner processing of information feedback loop in
public sphere leave most journalists baffled. Can we provide an organized view
to charact... | computer science |
11,400 | Using machine learning for medium frequency derivative portfolio trading | cs.LG | We use machine learning for designing a medium frequency trading strategy for
a portfolio of 5 year and 10 year US Treasury note futures. We formulate this
as a classification problem where we predict the weekly direction of movement
of the portfolio using features extracted from a deep belief network trained on
techni... | computer science |
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