Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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40,610 | Structured Sparsity Models for Multiparty Speech Recovery from
Reverberant Recordings | cs.LG | We tackle the multi-party speech recovery problem through modeling the
acoustic of the reverberant chambers. Our approach exploits structured sparsity
models to perform room modeling and speech recovery. We propose a scheme for
characterizing the room acoustic from the unknown competing speech sources
relying on locali... | computer science |
40,611 | Predicting Near-Future Churners and Win-Backs in the Telecommunications
Industry | cs.CE | In this work, we presented the strategies and techniques that we have
developed for predicting the near-future churners and win-backs for a telecom
company. On a large-scale and real-world database containing customer profiles
and some transaction data from a telecom company, we first analyzed the data
schema, develope... | computer science |
40,612 | A Game-theoretic Machine Learning Approach for Revenue Maximization in
Sponsored Search | cs.GT | Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to opt... | computer science |
40,613 | Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets | math.OC | The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth
optimization has regained much interest in recent years in the context of large
scale optimization and machine learning. A key advantage of the method is that
it avoids projections - the computational bottleneck in many applications -
replacing i... | computer science |
40,614 | Machine learning approach for text and document mining | cs.IR | Text Categorization (TC), also known as Text Classification, is the task of
automatically classifying a set of text documents into different categories
from a predefined set. If a document belongs to exactly one of the categories,
it is a single-label classification task; otherwise, it is a multi-label
classification t... | computer science |
40,615 | Computational role of eccentricity dependent cortical magnification | cs.LG | We develop a sampling extension of M-theory focused on invariance to scale
and translation. Quite surprisingly, the theory predicts an architecture of
early vision with increasing receptive field sizes and a high resolution fovea
-- in agreement with data about the cortical magnification factor, V1 and the
retina. From... | computer science |
40,616 | Memristor models for machine learning | cs.LG | In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be ... | computer science |
40,617 | Budget-Constrained Item Cold-Start Handling in Collaborative Filtering
Recommenders via Optimal Design | cs.IR | It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literat... | computer science |
40,618 | Quaternion Gradient and Hessian | math.NA | The optimization of real scalar functions of quaternion variables, such as
the mean square error or array output power, underpins many practical
applications. Solutions often require the calculation of the gradient and
Hessian, however, real functions of quaternion variables are essentially
non-analytic. To address thi... | computer science |
40,619 | Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based
Support Vector Regression Modeling Framework | cs.LG | Highly accurate interval forecasting of electricity demand is fundamental to
the success of reducing the risk when making power system planning and
operational decisions by providing a range rather than point estimation. In
this study, a novel modeling framework integrating bivariate empirical mode
decomposition (BEMD)... | computer science |
40,620 | Learning An Invariant Speech Representation | cs.SD | Recognition of speech, and in particular the ability to generalize and learn
from small sets of labelled examples like humans do, depends on an appropriate
representation of the acoustic input. We formulate the problem of finding
robust speech features for supervised learning with small sample complexity as
a problem o... | computer science |
40,621 | Construction of non-convex polynomial loss functions for training a
binary classifier with quantum annealing | cs.LG | Quantum annealing is a heuristic quantum algorithm which exploits quantum
resources to minimize an objective function embedded as the energy levels of a
programmable physical system. To take advantage of a potential quantum
advantage, one needs to be able to map the problem of interest to the native
hardware with reaso... | computer science |
40,622 | Predictive Modelling of Bone Age through Classification and Regression
of Bone Shapes | cs.LG | Bone age assessment is a task performed daily in hospitals worldwide. This
involves a clinician estimating the age of a patient from a radiograph of the
non-dominant hand.
Our approach to automated bone age assessment is to modularise the algorithm
into the following three stages: segment and verify hand outline; seg... | computer science |
40,623 | Homotopy based algorithms for $\ell_0$-regularized least-squares | cs.NA | Sparse signal restoration is usually formulated as the minimization of a
quadratic cost function $\|y-Ax\|_2^2$, where A is a dictionary and x is an
unknown sparse vector. It is well-known that imposing an $\ell_0$ constraint
leads to an NP-hard minimization problem. The convex relaxation approach has
received consider... | computer science |
40,624 | Fast Support Vector Machines Using Parallel Adaptive Shrinking on
Distributed Systems | cs.DC | Support Vector Machines (SVM), a popular machine learning technique, has been
applied to a wide range of domains such as science, finance, and social
networks for supervised learning. Whether it is identifying high-risk patients
by health-care professionals, or potential high-school students to enroll in
college by sch... | computer science |
40,625 | Constant Factor Approximation for Balanced Cut in the PIE model | cs.DS | We propose and study a new semi-random semi-adversarial model for Balanced
Cut, a planted model with permutation-invariant random edges (PIE). Our model
is much more general than planted models considered previously. Consider a set
of vertices V partitioned into two clusters $L$ and $R$ of equal size. Let $G$
be an arb... | computer science |
40,626 | Correlation Clustering with Noisy Partial Information | cs.DS | In this paper, we propose and study a semi-random model for the Correlation
Clustering problem on arbitrary graphs G. We give two approximation algorithms
for Correlation Clustering instances from this model. The first algorithm finds
a solution of value $(1+ \delta) optcost + O_{\delta}(n\log^3 n)$ with high
probabili... | computer science |
40,627 | Active Learning and Best-Response Dynamics | cs.LG | We examine an important setting for engineered systems in which low-power
distributed sensors are each making highly noisy measurements of some unknown
target function. A center wants to accurately learn this function by querying a
small number of sensors, which ordinarily would be impossible due to the high
noise rate... | computer science |
40,628 | An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with
Quality Assurance | cs.GT | Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an... | computer science |
40,629 | Stock Market Prediction from WSJ: Text Mining via Sparse Matrix
Factorization | cs.LG | We revisit the problem of predicting directional movements of stock prices
based on news articles: here our algorithm uses daily articles from The Wall
Street Journal to predict the closing stock prices on the same day. We propose
a unified latent space model to characterize the "co-movements" between stock
prices and ... | computer science |
40,630 | Quantum adiabatic machine learning | cs.LG | We develop an approach to machine learning and anomaly detection via quantum
adiabatic evolution. In the training phase we identify an optimal set of weak
classifiers, to form a single strong classifier. In the testing phase we
adiabatically evolve one or more strong classifiers on a superposition of
inputs in order to... | computer science |
40,631 | The Variational Garrote | stat.ME | In this paper, we present a new variational method for sparse regression
using $L_0$ regularization. The variational parameters appear in the
approximate model in a way that is similar to Breiman's Garrote model. We refer
to this method as the variational Garrote (VG). We show that the combination of
the variational ap... | computer science |
40,632 | How Open Should Open Source Be? | cs.CR | Many open-source projects land security fixes in public repositories before
shipping these patches to users. This paper presents attacks on such projects -
taking Firefox as a case-study - that exploit patch metadata to efficiently
search for security patches prior to shipping. Using access-restricted bug
reports linke... | computer science |
40,633 | Gossip Learning with Linear Models on Fully Distributed Data | cs.LG | Machine learning over fully distributed data poses an important problem in
peer-to-peer (P2P) applications. In this model we have one data record at each
network node, but without the possibility to move raw data due to privacy
considerations. For example, user profiles, ratings, history, or sensor
readings can represe... | computer science |
40,634 | Anomaly Sequences Detection from Logs Based on Compression | cs.LG | Mining information from logs is an old and still active research topic. In
recent years, with the rapid emerging of cloud computing, log mining becomes
increasingly important to industry. This paper focus on one major mission of
log mining: anomaly detection, and proposes a novel method for mining abnormal
sequences fr... | computer science |
40,635 | Convergence Rates of Inexact Proximal-Gradient Methods for Convex
Optimization | cs.LG | We consider the problem of optimizing the sum of a smooth convex function and
a non-smooth convex function using proximal-gradient methods, where an error is
present in the calculation of the gradient of the smooth term or in the
proximity operator with respect to the non-smooth term. We show that both the
basic proxim... | computer science |
40,636 | Making Gradient Descent Optimal for Strongly Convex Stochastic
Optimization | cs.LG | Stochastic gradient descent (SGD) is a simple and popular method to solve
stochastic optimization problems which arise in machine learning. For strongly
convex problems, its convergence rate was known to be O(\log(T)/T), by running
SGD for T iterations and returning the average point. However, recent results
showed tha... | computer science |
40,637 | Deterministic Feature Selection for $k$-means Clustering | cs.LG | We study feature selection for $k$-means clustering. Although the literature
contains many methods with good empirical performance, algorithms with provable
theoretical behavior have only recently been developed. Unfortunately, these
algorithms are randomized and fail with, say, a constant probability. We
address this ... | computer science |
40,638 | ProPPA: A Fast Algorithm for $\ell_1$ Minimization and Low-Rank Matrix
Completion | cs.LG | We propose a Projected Proximal Point Algorithm (ProPPA) for solving a class
of optimization problems. The algorithm iteratively computes the proximal point
of the last estimated solution projected into an affine space which itself is
parallel and approaching to the feasible set. We provide convergence analysis
theoret... | computer science |
40,639 | Detecting Spammers via Aggregated Historical Data Set | cs.CR | The battle between email service providers and senders of mass unsolicited
emails (Spam) continues to gain traction. Vast numbers of Spam emails are sent
mainly from automatic botnets distributed over the world. One method for
mitigating Spam in a computationally efficient manner is fast and accurate
blacklisting of th... | computer science |
40,640 | Hamiltonian Annealed Importance Sampling for partition function
estimation | cs.LG | We introduce an extension to annealed importance sampling that uses
Hamiltonian dynamics to rapidly estimate normalization constants. We
demonstrate this method by computing log likelihoods in directed and undirected
probabilistic image models. We compare the performance of linear generative
models with both Gaussian a... | computer science |
40,641 | The representer theorem for Hilbert spaces: a necessary and sufficient
condition | math.FA | A family of regularization functionals is said to admit a linear representer
theorem if every member of the family admits minimizers that lie in a fixed
finite dimensional subspace. A recent characterization states that a general
class of regularization functionals with differentiable regularizer admits a
linear repres... | computer science |
40,642 | Hamiltonian Monte Carlo with Reduced Momentum Flips | cs.LG | Hamiltonian Monte Carlo (or hybrid Monte Carlo) with partial momentum
refreshment explores the state space more slowly than it otherwise would due to
the momentum reversals which occur on proposal rejection. These cause
trajectories to double back on themselves, leading to random walk behavior on
timescales longer than... | computer science |
40,643 | Ordinal Boltzmann Machines for Collaborative Filtering | cs.IR | Collaborative filtering is an effective recommendation technique wherein the
preference of an individual can potentially be predicted based on preferences
of other members. Early algorithms often relied on the strong locality in the
preference data, that is, it is enough to predict preference of a user on a
particular ... | computer science |
40,644 | Censored Exploration and the Dark Pool Problem | cs.LG | We introduce and analyze a natural algorithm for multi-venue exploration from
censored data, which is motivated by the Dark Pool Problem of modern
quantitative finance. We prove that our algorithm converges in polynomial time
to a near-optimal allocation policy; prior results for similar problems in
stochastic inventor... | computer science |
40,645 | Density Sensitive Hashing | cs.IR | Nearest neighbors search is a fundamental problem in various research fields
like machine learning, data mining and pattern recognition. Recently,
hashing-based approaches, e.g., Locality Sensitive Hashing (LSH), are proved to
be effective for scalable high dimensional nearest neighbors search. Many
hashing algorithms ... | computer science |
40,646 | Malware Detection Module using Machine Learning Algorithms to Assist in
Centralized Security in Enterprise Networks | cs.CR | Malicious software is abundant in a world of innumerable computer users, who
are constantly faced with these threats from various sources like the internet,
local networks and portable drives. Malware is potentially low to high risk and
can cause systems to function incorrectly, steal data and even crash. Malware
may b... | computer science |
40,647 | Universal Algorithm for Online Trading Based on the Method of
Calibration | cs.LG | We present a universal algorithm for online trading in Stock Market which
performs asymptotically at least as good as any stationary trading strategy
that computes the investment at each step using a fixed function of the side
information that belongs to a given RKHS (Reproducing Kernel Hilbert Space).
Using a universa... | computer science |
40,648 | Constrained Overcomplete Analysis Operator Learning for Cosparse Signal
Modelling | math.NA | We consider the problem of learning a low-dimensional signal model from a
collection of training samples. The mainstream approach would be to learn an
overcomplete dictionary to provide good approximations of the training samples
using sparse synthesis coefficients. This famous sparse model has a less well
known counte... | computer science |
40,649 | Diffusion Adaptation over Networks | cs.MA | Adaptive networks are well-suited to perform decentralized information
processing and optimization tasks and to model various types of self-organized
and complex behavior encountered in nature. Adaptive networks consist of a
collection of agents with processing and learning abilities. The agents are
linked together thr... | computer science |
40,650 | From Exact Learning to Computing Boolean Functions and Back Again | cs.LG | The goal of the paper is to relate complexity measures associated with the
evaluation of Boolean functions (certificate complexity, decision tree
complexity) and learning dimensions used to characterize exact learning
(teaching dimension, extended teaching dimension). The high level motivation is
to discover non-trivia... | computer science |
40,651 | Streaming Algorithms for Pattern Discovery over Dynamically Changing
Event Sequences | cs.LG | Discovering frequent episodes over event sequences is an important data
mining task. In many applications, events constituting the data sequence arrive
as a stream, at furious rates, and recent trends (or frequent episodes) can
change and drift due to the dynamical nature of the underlying event generation
process. The... | computer science |
40,652 | Visual and semantic interpretability of projections of high dimensional
data for classification tasks | cs.HC | A number of visual quality measures have been introduced in visual analytics
literature in order to automatically select the best views of high dimensional
data from a large number of candidate data projections. These methods generally
concentrate on the interpretability of the visualization and pay little
attention to... | computer science |
40,653 | Clustering is difficult only when it does not matter | cs.LG | Numerous papers ask how difficult it is to cluster data. We suggest that the
more relevant and interesting question is how difficult it is to cluster data
sets {\em that can be clustered well}. More generally, despite the ubiquity and
the great importance of clustering, we still do not have a satisfactory
mathematical ... | computer science |
40,654 | On the practically interesting instances of MAXCUT | cs.CC | The complexity of a computational problem is traditionally quantified based
on the hardness of its worst case. This approach has many advantages and has
led to a deep and beautiful theory. However, from the practical perspective,
this leaves much to be desired. In application areas, practically interesting
instances ve... | computer science |
40,655 | A hybrid clustering algorithm for data mining | cs.DB | Data clustering is a process of arranging similar data into groups. A
clustering algorithm partitions a data set into several groups such that the
similarity within a group is better than among groups. In this paper a hybrid
clustering algorithm based on K-mean and K-harmonic mean (KHM) is described.
The proposed algor... | computer science |
40,656 | Algorithms for Approximate Minimization of the Difference Between
Submodular Functions, with Applications | cs.DS | We extend the work of Narasimhan and Bilmes [30] for minimizing set functions
representable as a difference between submodular functions. Similar to [30],
our new algorithms are guaranteed to monotonically reduce the objective
function at every step. We empirically and theoretically show that the
per-iteration cost of ... | computer science |
40,657 | Robust Online Hamiltonian Learning | cs.LG | In this work we combine two distinct machine learning methodologies,
sequential Monte Carlo and Bayesian experimental design, and apply them to the
problem of inferring the dynamical parameters of a quantum system. We design
the algorithm with practicality in mind by including parameters that control
trade-offs between... | computer science |
40,658 | From Fields to Trees | stat.CO | We present new MCMC algorithms for computing the posterior distributions and
expectations of the unknown variables in undirected graphical models with
regular structure. For demonstration purposes, we focus on Markov Random Fields
(MRFs). By partitioning the MRFs into non-overlapping trees, it is possible to
compute th... | computer science |
40,659 | Maximum Entropy for Collaborative Filtering | cs.IR | Within the task of collaborative filtering two challenges for computing
conditional probabilities exist. First, the amount of training data available
is typically sparse with respect to the size of the domain. Thus, support for
higher-order interactions is generally not present. Second, the variables that
we are condit... | computer science |
40,660 | Learning Probabilistic Systems from Tree Samples | cs.LO | We consider the problem of learning a non-deterministic probabilistic system
consistent with a given finite set of positive and negative tree samples.
Consistency is defined with respect to strong simulation conformance. We
propose learning algorithms that use traditional and a new "stochastic"
state-space partitioning... | computer science |
40,661 | Touchalytics: On the Applicability of Touchscreen Input as a Behavioral
Biometric for Continuous Authentication | cs.CR | We investigate whether a classifier can continuously authenticate users based
on the way they interact with the touchscreen of a smart phone. We propose a
set of 30 behavioral touch features that can be extracted from raw touchscreen
logs and demonstrate that different users populate distinct subspaces of this
feature ... | computer science |
40,662 | Gaussian process regression as a predictive model for Quality-of-Service
in Web service systems | cs.NI | In this paper, we present the Gaussian process regression as the predictive
model for Quality-of-Service (QoS) attributes in Web service systems. The goal
is to predict performance of the execution system expressed as QoS attributes
given existing execution system, service repository, and inputs, e.g., streams
of reque... | computer science |
40,663 | Predicate Generation for Learning-Based Quantifier-Free Loop Invariant
Inference | cs.LO | We address the predicate generation problem in the context of loop invariant
inference. Motivated by the interpolation-based abstraction refinement
technique, we apply the interpolation theorem to synthesize predicates
implicitly implied by program texts. Our technique is able to improve the
effectiveness and efficienc... | computer science |
40,664 | Label-dependent Feature Extraction in Social Networks for Node
Classification | cs.SI | A new method of feature extraction in the social network for within-network
classification is proposed in the paper. The method provides new features
calculated by combination of both: network structure information and class
labels assigned to nodes. The influence of various features on classification
performance has a... | computer science |
40,665 | Discovery of factors in matrices with grades | cs.LG | We present an approach to decomposition and factor analysis of matrices with
ordinal data. The matrix entries are grades to which objects represented by
rows satisfy attributes represented by columns, e.g. grades to which an image
is red, a product has a given feature, or a person performs well in a test. We
assume tha... | computer science |
40,666 | Mining Representative Unsubstituted Graph Patterns Using Prior
Similarity Matrix | cs.CE | One of the most powerful techniques to study protein structures is to look
for recurrent fragments (also called substructures or spatial motifs), then use
them as patterns to characterize the proteins under study. An emergent trend
consists in parsing proteins three-dimensional (3D) structures into graphs of
amino acid... | computer science |
40,667 | Mini-Batch Primal and Dual Methods for SVMs | cs.LG | We address the issue of using mini-batches in stochastic optimization of
SVMs. We show that the same quantity, the spectral norm of the data, controls
the parallelization speedup obtained for both primal stochastic subgradient
descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it
to derive novel... | computer science |
40,668 | Revealing Cluster Structure of Graph by Path Following Replicator
Dynamic | cs.LG | In this paper, we propose a path following replicator dynamic, and
investigate its potentials in uncovering the underlying cluster structure of a
graph. The proposed dynamic is a generalization of the discrete replicator
dynamic. The replicator dynamic has been successfully used to extract dense
clusters of graphs; how... | computer science |
40,669 | Hybrid Q-Learning Applied to Ubiquitous recommender system | cs.LG | Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to bring a solution to some of the
problems concerning the acceptance of the system by users. To achieve this, we
propose a funda... | computer science |
40,670 | Machine Learning for Bioclimatic Modelling | cs.LG | Many machine learning (ML) approaches are widely used to generate bioclimatic
models for prediction of geographic range of organism as a function of climate.
Applications such as prediction of range shift in organism, range of invasive
species influenced by climate change are important parameters in understanding
the i... | computer science |
40,671 | A Cooperative Q-learning Approach for Real-time Power Allocation in
Femtocell Networks | cs.MA | In this paper, we address the problem of distributed interference management
of cognitive femtocells that share the same frequency range with macrocells
(primary user) using distributed multi-agent Q-learning. We formulate and solve
three problems representing three different Q-learning algorithms: namely,
centralized,... | computer science |
40,672 | Improving CUR Matrix Decomposition and the Nyström Approximation via
Adaptive Sampling | cs.LG | The CUR matrix decomposition and the Nystr\"{o}m approximation are two
important low-rank matrix approximation techniques. The Nystr\"{o}m method
approximates a symmetric positive semidefinite matrix in terms of a small
number of its columns, while CUR approximates an arbitrary data matrix by a
small number of its colu... | computer science |
40,673 | On Improving Energy Efficiency within Green Femtocell Networks: A
Hierarchical Reinforcement Learning Approach | cs.LG | One of the efficient solutions of improving coverage and increasing capacity
in cellular networks is the deployment of femtocells. As the cellular networks
are becoming more complex, energy consumption of whole network infrastructure
is becoming important in terms of both operational costs and environmental
impacts. Th... | computer science |
40,674 | Non-Asymptotic Convergence Analysis of Inexact Gradient Methods for
Machine Learning Without Strong Convexity | math.OC | Many recent applications in machine learning and data fitting call for the
algorithmic solution of structured smooth convex optimization problems.
Although the gradient descent method is a natural choice for this task, it
requires exact gradient computations and hence can be inefficient when the
problem size is large o... | computer science |
40,675 | API design for machine learning software: experiences from the
scikit-learn project | cs.LG | Scikit-learn is an increasingly popular machine learning li- brary. Written
in Python, it is designed to be simple and efficient, accessible to
non-experts, and reusable in various contexts. In this paper, we present and
discuss our design choices for the application programming interface (API) of
the project. In parti... | computer science |
40,676 | A Comparism of the Performance of Supervised and Unsupervised Machine
Learning Techniques in evolving Awale/Mancala/Ayo Game Player | cs.LG | Awale games have become widely recognized across the world, for their
innovative strategies and techniques which were used in evolving the agents
(player) and have produced interesting results under various conditions. This
paper will compare the results of the two major machine learning techniques by
reviewing their p... | computer science |
40,677 | Maximizing submodular functions using probabilistic graphical models | cs.LG | We consider the problem of maximizing submodular functions; while this
problem is known to be NP-hard, several numerically efficient local search
techniques with approximation guarantees are available. In this paper, we
propose a novel convex relaxation which is based on the relationship between
submodular functions, e... | computer science |
40,678 | Convex relaxations of structured matrix factorizations | cs.LG | We consider the factorization of a rectangular matrix $X $ into a positive
linear combination of rank-one factors of the form $u v^\top$, where $u$ and
$v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode
specific structures regarding the factors, such as positivity or sparsity. In
this paper, w... | computer science |
40,679 | Attribute-Efficient Evolvability of Linear Functions | cs.LG | In a seminal paper, Valiant (2006) introduced a computational model for
evolution to address the question of complexity that can arise through
Darwinian mechanisms. Valiant views evolution as a restricted form of
computational learning, where the goal is to evolve a hypothesis that is close
to the ideal function. Feldm... | computer science |
40,680 | Bayesian rules and stochastic models for high accuracy prediction of
solar radiation | cs.LG | It is essential to find solar predictive methods to massively insert
renewable energies on the electrical distribution grid. The goal of this study
is to find the best methodology allowing predicting with high accuracy the
hourly global radiation. The knowledge of this quantity is essential for the
grid manager or the ... | computer science |
40,681 | Speedy Model Selection (SMS) for Copula Models | cs.LG | We tackle the challenge of efficiently learning the structure of expressive
multivariate real-valued densities of copula graphical models. We start by
theoretically substantiating the conjecture that for many copula families the
magnitude of Spearman's rank correlation coefficient is monotone in the
expected contributi... | computer science |
40,682 | Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based
Methods | cs.CY | The ability to automatically detect fraudulent escrow websites is important
in order to alleviate online auction fraud. Despite research on related topics,
fake escrow website categorization has received little attention. In this study
we evaluated the effectiveness of various features and techniques for detecting
fake... | computer science |
40,683 | Evaluating Link-Based Techniques for Detecting Fake Pharmacy Websites | cs.CY | Fake online pharmacies have become increasingly pervasive, constituting over
90% of online pharmacy websites. There is a need for fake website detection
techniques capable of identifying fake online pharmacy websites with a high
degree of accuracy. In this study, we compared several well-known link-based
detection tech... | computer science |
40,684 | Optimal Hybrid Channel Allocation:Based On Machine Learning Algorithms | cs.NI | Recent advances in cellular communication systems resulted in a huge increase
in spectrum demand. To meet the requirements of the ever-growing need for
spectrum, efficient utilization of the existing resources is of utmost
importance. Channel Allocation, has thus become an inevitable research topic in
wireless communic... | computer science |
40,685 | Context-aware recommendations from implicit data via scalable tensor
factorization | cs.LG | Albeit the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be... | computer science |
40,686 | A Statistical Learning Based System for Fake Website Detection | cs.CY | Existing fake website detection systems are unable to effectively detect fake
websites. In this study, we advocate the development of fake website detection
systems that employ classification methods grounded in statistical learning
theory (SLT). Experimental results reveal that a prototype system developed
using SLT-b... | computer science |
40,687 | Using PCA to Efficiently Represent State Spaces | cs.LG | Reinforcement learning algorithms need to deal with the exponential growth of
states and actions when exploring optimal control in high-dimensional spaces.
This is known as the curse of dimensionality. By projecting the agent's state
onto a low-dimensional manifold, we can represent the state space in a smaller
and mor... | computer science |
40,688 | fastFM: A Library for Factorization Machines | cs.LG | Factorization Machines (FM) are only used in a narrow range of applications
and are not part of the standard toolbox of machine learning models. This is a
pity, because even though FMs are recognized as being very successful for
recommender system type applications they are a general model to deal with
sparse and high ... | computer science |
40,689 | An $O(n\log(n))$ Algorithm for Projecting Onto the Ordered Weighted
$\ell_1$ Norm Ball | math.OC | The ordered weighted $\ell_1$ (OWL) norm is a newly developed generalization
of the Octogonal Shrinkage and Clustering Algorithm for Regression (OSCAR)
norm. This norm has desirable statistical properties and can be used to perform
simultaneous clustering and regression. In this paper, we show how to compute
the projec... | computer science |
40,690 | Blind Compressive Sensing Framework for Collaborative Filtering | cs.IR | Existing works based on latent factor models have focused on representing the
rating matrix as a product of user and item latent factor matrices, both being
dense. Latent (factor) vectors define the degree to which a trait is possessed
by an item or the affinity of user towards that trait. A dense user matrix is a
reas... | computer science |
40,691 | Context-Aware Mobility Management in HetNets: A Reinforcement Learning
Approach | cs.NI | The use of small cell deployments in heterogeneous network (HetNet)
environments is expected to be a key feature of 4G networks and beyond, and
essential for providing higher user throughput and cell-edge coverage. However,
due to different coverage sizes of macro and pico base stations (BSs), such a
paradigm shift int... | computer science |
40,692 | Human Social Interaction Modeling Using Temporal Deep Networks | cs.CY | We present a novel approach to computational modeling of social interactions
based on modeling of essential social interaction predicates (ESIPs) such as
joint attention and entrainment. Based on sound social psychological theory and
methodology, we collect a new "Tower Game" dataset consisting of audio-visual
capture ... | computer science |
40,693 | $k$-center Clustering under Perturbation Resilience | cs.DS | The $k$-center problem is a canonical and long-studied facility location and
clustering problem with many applications in both its symmetric and asymmetric
forms. Both versions of the problem have tight approximation factors on worst
case instances: a $2$-approximation for symmetric $k$-center and an
$O(\log^*(k))$-app... | computer science |
40,694 | Complexity Theoretic Limitations on Learning Halfspaces | cs.CC | We study the problem of agnostically learning halfspaces which is defined by
a fixed but unknown distribution $\mathcal{D}$ on $\mathbb{Q}^n\times \{\pm
1\}$. We define $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$ as the least error
of a halfspace classifier for $\mathcal{D}$. A learner who can access
$\mathcal{D}$ has ... | computer science |
40,695 | Machine Learning for Indoor Localization Using Mobile Phone-Based
Sensors | cs.LG | In this paper we investigate the problem of localizing a mobile device based
on readings from its embedded sensors utilizing machine learning methodologies.
We consider a real-world environment, collect a large dataset of 3110
datapoints, and examine the performance of a substantial number of machine
learning algorithm... | computer science |
40,696 | Times series averaging from a probabilistic interpretation of
time-elastic kernel | cs.LG | At the light of regularized dynamic time warping kernels, this paper
reconsider the concept of time elastic centroid (TEC) for a set of time series.
From this perspective, we show first how TEC can easily be addressed as a
preimage problem. Unfortunately this preimage problem is ill-posed, may suffer
from over-fitting ... | computer science |
40,697 | A Practical Guide to Randomized Matrix Computations with MATLAB
Implementations | cs.MS | Matrix operations such as matrix inversion, eigenvalue decomposition,
singular value decomposition are ubiquitous in real-world applications.
Unfortunately, many of these matrix operations so time and memory expensive
that they are prohibitive when the scale of data is large. In real-world
applications, since the data ... | computer science |
40,698 | SAM: Support Vector Machine Based Active Queue Management | cs.NI | Recent years have seen an increasing interest in the design of AQM (Active
Queue Management) controllers. The purpose of these controllers is to manage
the network congestion under varying loads, link delays and bandwidth. In this
paper, a new AQM controller is proposed which is trained by using the SVM
(Support Vector... | computer science |
40,699 | Heavy hitters via cluster-preserving clustering | cs.DS | In turnstile $\ell_p$ $\varepsilon$-heavy hitters, one maintains a
high-dimensional $x\in\mathbb{R}^n$ subject to $\texttt{update}(i,\Delta)$
causing $x_i\leftarrow x_i + \Delta$, where $i\in[n]$, $\Delta\in\mathbb{R}$.
Upon receiving a query, the goal is to report a small list $L\subset[n]$, $|L|
= O(1/\varepsilon^p)$... | computer science |
40,700 | Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms | cs.NA | Many theoretical results in the machine learning domain stand only for
functions that are Lipschitz continuous. Lipschitz continuity is a strong form
of continuity that linearly bounds the variations of a function. In this paper,
we derive tight Lipschitz constants for two families of metrics: Mahalanobis
distances and... | computer science |
40,701 | Single-Molecule Protein Identification by Sub-Nanopore Sensors | cs.LG | Recent advances in top-down mass spectrometry enabled identification of
intact proteins, but this technology still faces challenges. For example,
top-down mass spectrometry suffers from a lack of sensitivity since the ion
counts for a single fragmentation event are often low. In contrast, nanopore
technology is exquisi... | computer science |
40,702 | M3: Scaling Up Machine Learning via Memory Mapping | cs.LG | To process data that do not fit in RAM, conventional wisdom would suggest
using distributed approaches. However, recent research has demonstrated virtual
memory's strong potential in scaling up graph mining algorithms on a single
machine. We propose to use a similar approach for general machine learning. We
contribute:... | computer science |
40,703 | Learning Simple Auctions | cs.LG | We present a general framework for proving polynomial sample complexity
bounds for the problem of learning from samples the best auction in a class of
"simple" auctions. Our framework captures all of the most prominent examples of
"simple" auctions, including anonymous and non-anonymous item and bundle
pricings, with e... | computer science |
40,704 | Leveraging Network Dynamics for Improved Link Prediction | cs.SI | The aim of link prediction is to forecast connections that are most likely to
occur in the future, based on examples of previously observed links. A key
insight is that it is useful to explicitly model network dynamics, how
frequently links are created or destroyed when doing link prediction. In this
paper, we introduc... | computer science |
40,705 | The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for
Identifying Optimal Thresholds in Data | cs.LG | In many data classification problems, there is no linear relationship between
an explanatory and the dependent variables. Instead, there may be ranges of the
input variable for which the observed outcome is signficantly more or less
likely. This paper describes an algorithm for automatic detection of such
thresholds, c... | computer science |
40,706 | Typical Stability | cs.LG | In this paper, we introduce a notion of algorithmic stability called typical
stability. When our goal is to release real-valued queries (statistics)
computed over a dataset, this notion does not require the queries to be of
bounded sensitivity -- a condition that is generally assumed under differential
privacy [DMNS06,... | computer science |
40,707 | An Unbiased Data Collection and Content Exploitation/Exploration
Strategy for Personalization | cs.IR | One of missions for personalization systems and recommender systems is to
show content items according to users' personal interests. In order to achieve
such goal, these systems are learning user interests over time and trying to
present content items tailoring to user profiles. Recommending items according
to users' p... | computer science |
40,708 | Asynchronous Stochastic Gradient Descent with Variance Reduction for
Non-Convex Optimization | cs.LG | We provide the first theoretical analysis on the convergence rate of the
asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on
non-convex optimization. Recent studies have shown that the asynchronous
stochastic gradient descent (SGD) based algorithms with variance reduction
converge with a linea... | computer science |
40,709 | ModelWizard: Toward Interactive Model Construction | cs.PL | Data scientists engage in model construction to discover machine learning
models that well explain a dataset, in terms of predictiveness,
understandability and generalization across domains. Questions such as "what if
we model common cause Z" and "what if Y's dependence on X reverses" inspire
many candidate models to c... | computer science |
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