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10,201 | Robust Deep Reinforcement Learning with Adversarial Attacks | cs.LG | This paper proposes adversarial attacks for Reinforcement Learning (RL) and
then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to
parameter uncertainties with the help of these attacks. We show that even a
naively engineered attack successfully degrades the performance of DRL
algorithm. We fur... | computer science |
10,202 | DeepConfig: Automating Data Center Network Topologies Management with
Machine Learning | cs.NI | In recent years, many techniques have been developed to improve the
performance and efficiency of data center networks. While these techniques
provide high accuracy, they are often designed using heuristics that leverage
domain-specific properties of the workload or hardware.
In this vision paper, we argue that many ... | computer science |
10,203 | Sequential Prediction of Social Media Popularity with Deep Temporal
Context Networks | cs.SI | Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For exa... | computer science |
10,204 | Parallel Complexity of Forward and Backward Propagation | cs.LG | We show that the forward and backward propagation can be formulated as a
solution of lower and upper triangular systems of equations. For standard
feedforward (FNNs) and recurrent neural networks (RNNs) the triangular systems
are always block bi-diagonal, while for a general computation graph (directed
acyclic graph) t... | computer science |
10,205 | Ray RLlib: A Framework for Distributed Reinforcement Learning | cs.AI | Reinforcement learning (RL) training involves the deep nesting of highly
irregular computation patterns, each of which typically exhibits opportunities
for distributed computation. Current RL libraries offer parallelism at the
level of the entire program, coupling all algorithm components together and
making existing i... | computer science |
10,206 | VulDeePecker: A Deep Learning-Based System for Vulnerability Detection | cs.CR | The automatic detection of software vulnerabilities is an important research
problem. However, existing solutions to this problem rely on human experts to
define features and often miss many vulnerabilities (i.e., incurring high false
negative rate). In this paper, we initiate the study of using deep
learning-based vul... | computer science |
10,207 | Audio Adversarial Examples: Targeted Attacks on Speech-to-Text | cs.LG | We construct targeted audio adversarial examples on automatic speech
recognition. Given any audio waveform, we can produce another that is over
99.9% similar, but transcribes as any phrase we choose (at a rate of up to 50
characters per second). We apply our iterative optimization-based attack to
Mozilla's implementati... | computer science |
10,208 | Which Training Methods for GANs do actually Converge? | cs.LG | Recent work has shown local convergence of GAN training for absolutely
continuous data and generator distributions. In this paper, we show that the
requirement of absolute continuity is necessary: we describe a simple yet
prototypical counterexample showing that in the more realistic case of
distributions that are not ... | computer science |
10,209 | Experience-driven Networking: A Deep Reinforcement Learning based
Approach | cs.NI | Modern communication networks have become very complicated and highly
dynamic, which makes them hard to model, predict and control. In this paper, we
develop a novel experience-driven approach that can learn to well control a
communication network from its own experience rather than an accurate
mathematical model, just... | computer science |
10,210 | Mitigating Unwanted Biases with Adversarial Learning | cs.LG | Machine learning is a tool for building models that accurately represent
input training data. When undesired biases concerning demographic groups are in
the training data, well-trained models will reflect those biases. We present a
framework for mitigating such biases by including a variable for the group of
interest a... | computer science |
10,211 | Opinion Dynamics with Varying Susceptibility to Persuasion | cs.SI | A long line of work in social psychology has studied variations in people's
susceptibility to persuasion -- the extent to which they are willing to modify
their opinions on a topic. This body of literature suggests an interesting
perspective on theoretical models of opinion formation by interacting parties
in a network... | computer science |
10,212 | Active Neural Localization | cs.LG | Localization is the problem of estimating the location of an autonomous agent
from an observation and a map of the environment. Traditional methods of
localization, which filter the belief based on the observations, are
sub-optimal in the number of steps required, as they do not decide the actions
taken by the agent. W... | computer science |
10,213 | JointDNN: An Efficient Training and Inference Engine for Intelligent
Mobile Cloud Computing Services | cs.DC | Deep neural networks are among the most influential architectures of deep
learning algorithms, being deployed in many mobile intelligent applications.
End-side services, such as intelligent personal assistants (IPAs), autonomous
cars, and smart home services often employ either simple local models or
complex remote mod... | computer science |
10,214 | Improving Active Learning in Systematic Reviews | cs.IR | Systematic reviews are essential to summarizing the results of different
clinical and social science studies. The first step in a systematic review task
is to identify all the studies relevant to the review. The task of identifying
relevant studies for a given systematic review is usually performed manually,
and as a r... | computer science |
10,215 | Obfuscated Gradients Give a False Sense of Security: Circumventing
Defenses to Adversarial Examples | cs.LG | We identify obfuscated gradients, a kind of gradient masking, as a phenomenon
that leads to a false sense of security in defenses against adversarial
examples. While defenses that cause obfuscated gradients appear to defeat
iterative optimization-based attacks, we find defenses relying on this effect
can be circumvente... | computer science |
10,216 | Alleviating catastrophic forgetting using context-dependent gating and
synaptic stabilization | cs.LG | Humans and most animals can learn new tasks without forgetting old ones.
However, training artificial neural networks (ANNs) on new tasks typically
cause it to forget previously learned tasks. This phenomenon is the result of
"catastrophic forgetting", in which training an ANN disrupts connection weights
that were impo... | computer science |
10,217 | A Survey Of Methods For Explaining Black Box Models | cs.CY | In the last years many accurate decision support systems have been
constructed as black boxes, that is as systems that hide their internal logic
to the user. This lack of explanation constitutes both a practical and an
ethical issue. The literature reports many approaches aimed at overcoming this
crucial weakness somet... | computer science |
10,218 | Learning a SAT Solver from Single-Bit Supervision | cs.AI | We present NeuroSAT, a message passing neural network that learns to solve
SAT problems after only being trained as a classifier to predict
satisfiability. Although it is not competitive with state-of-the-art SAT
solvers, NeuroSAT can solve problems that are substantially larger and more
difficult than it ever saw duri... | computer science |
10,219 | Tree-to-tree Neural Networks for Program Translation | cs.AI | Program translation is an important tool to migrate legacy code in one
language into an ecosystem built in a different language. In this work, we are
the first to consider employing deep neural networks toward tackling this
problem. We observe that program translation is a modular procedure, in which a
sub-tree of the ... | computer science |
10,220 | ProofWatch: Watchlist Guidance for Large Theories in E | cs.AI | Watchlist (also hint list) is a mechanism that allows related proofs to guide
a proof search for a new conjecture. This mechanism has been used with the
Otter and Prover9 theorem provers, both for interactive formalizations and for
human-assisted proving of open conjectures in small theories. In this work we
explore th... | computer science |
10,221 | signSGD: compressed optimisation for non-convex problems | cs.LG | Training large neural networks requires distributing learning across multiple
workers, where the cost of communicating gradients can be a significant
bottleneck. signSGD alleviates this problem by transmitting just the sign of
each minibatch stochastic gradient. We prove that it can get the best of both
worlds: compres... | computer science |
10,222 | TVM: End-to-End Optimization Stack for Deep Learning | cs.LG | Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the
current popularity and utility of deep learning. However, these frameworks are
optimized for a narrow range of server-class GPUs and deploying workloads to
other platforms such as mobile phones, embedded devices, and specialized
accelerators (... | computer science |
10,223 | Mean Field Multi-Agent Reinforcement Learning | cs.MA | Existing multi-agent reinforcement learning methods are limited typically to
a small number of agents. When the agent number increases largely, the learning
becomes intractable due to the curse of the dimensionality and the exponential
growth of user interactions. In this paper, we present Mean Field Reinforcement
Lear... | computer science |
10,224 | Optimizing Interactive Systems with Data-Driven Objectives | cs.AI | Effective optimization is essential for interactive systems to provide a
satisfactory user experience. However, it is often challenging to find an
objective to optimize for. Generally, such objectives are manually crafted and
rarely capture complex user needs accurately. Conversely, we propose an
approach that infers t... | computer science |
10,225 | Deep Learning for Joint Source-Channel Coding of Text | cs.IT | We consider the problem of joint source and channel coding of structured data
such as natural language over a noisy channel. The typical approach to this
problem in both theory and practice involves performing source coding to first
compress the text and then channel coding to add robustness for the
transmission across... | computer science |
10,226 | Neural Network Ensembles to Real-time Identification of Plug-level
Appliance Measurements | cs.LG | The problem of identifying end-use electrical appliances from their
individual consumption profiles, known as the appliance identification problem,
is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated
plug-wise metering. Therefore, appliance identification has received dedicated
studies with va... | computer science |
10,227 | Incremental and Iterative Learning of Answer Set Programs from Mutually
Distinct Examples | cs.AI | Over these years the Artificial Intelligence (AI) community has produced
several datasets which have given the machine learning algorithms the
opportunity to learn various skills across various domains. However, a subclass
of these machine learning algorithms that aimed at learning logic programs,
namely the Inductive ... | computer science |
10,228 | The Secret Sharer: Measuring Unintended Neural Network Memorization &
Extracting Secrets | cs.LG | Machine learning models based on neural networks and deep learning are being
rapidly adopted for many purposes. What those models learn, and what they may
share, is a significant concern when the training data may contain secrets and
the models are public -- e.g., when a model helps users compose text messages
using mo... | computer science |
10,229 | Structured Control Nets for Deep Reinforcement Learning | cs.LG | In recent years, Deep Reinforcement Learning has made impressive advances in
solving several important benchmark problems for sequential decision making.
Many control applications use a generic multilayer perceptron (MLP) for
non-vision parts of the policy network. In this work, we propose a new neural
network architec... | computer science |
10,230 | Weighted Double Deep Multiagent Reinforcement Learning in Stochastic
Cooperative Environments | cs.MA | Despite single agent deep reinforcement learning has achieved significant
success due to the experience replay mechanism, Concerns should be reconsidered
in multiagent environments. This work focus on the stochastic cooperative
environment. We apply a specific adaptation to one recently proposed weighted
double estimat... | computer science |
10,231 | An Algorithmic Framework to Control Bias in Bandit-based Personalization | cs.LG | Personalization is pervasive in the online space as it leads to higher
efficiency and revenue by allowing the most relevant content to be served to
each user. However, recent studies suggest that personalization methods can
propagate societal or systemic biases and polarize opinions; this has led to
calls for regulator... | computer science |
10,232 | GraphRNN: A Deep Generative Model for Graphs | cs.LG | Modeling and generating graphs is fundamental for studying networks in
biology, engineering, and social sciences. However, modeling complex
distributions over graphs and then efficiently sampling from these
distributions is challenging due to the non-unique, high-dimensional nature of
graphs and the complex, non-local ... | computer science |
10,233 | Kitsune: An Ensemble of Autoencoders for Online Network Intrusion
Detection | cs.CR | Neural networks have become an increasingly popular solution for network
intrusion detection systems (NIDS). Their capability of learning complex
patterns and behaviors make them a suitable solution for differentiating
between normal traffic and network attacks. However, a drawback of neural
networks is the amount of r... | computer science |
10,234 | Multi-Goal Reinforcement Learning: Challenging Robotics Environments and
Request for Research | cs.LG | The purpose of this technical report is two-fold. First of all, it introduces
a suite of challenging continuous control tasks (integrated with OpenAI Gym)
based on currently existing robotics hardware. The tasks include pushing,
sliding and pick & place with a Fetch robotic arm as well as in-hand object
manipulation wi... | computer science |
10,235 | Reinforcement and Imitation Learning for Diverse Visuomotor Skills | cs.RO | We propose a model-free deep reinforcement learning method that leverages a
small amount of demonstration data to assist a reinforcement learning agent. We
apply this approach to robotic manipulation tasks and train end-to-end
visuomotor policies that map directly from RGB camera inputs to joint
velocities. We demonstr... | computer science |
10,236 | Model-Ensemble Trust-Region Policy Optimization | cs.LG | Model-free reinforcement learning (RL) methods are succeeding in a growing
number of tasks, aided by recent advances in deep learning. However, they tend
to suffer from high sample complexity, which hinders their use in real-world
domains. Alternatively, model-based reinforcement learning promises to reduce
sample comp... | computer science |
10,237 | Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent
Reinforcement Learning Approach | cs.AI | The Iterated Prisoner's Dilemma has guided research on social dilemmas for
decades. However, it distinguishes between only two atomic actions: cooperate
and defect. In real-world prisoner's dilemmas, these choices are temporally
extended and different strategies may correspond to sequences of actions,
reflecting grades... | computer science |
10,238 | On Cognitive Preferences and the Interpretability of Rule-based Models | cs.LG | It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption, and
recapitulate ... | computer science |
10,239 | One-Class Adversarial Nets for Fraud Detection | cs.LG | Many online applications, such as online social networks or knowledge bases,
are often attacked by malicious users who commit different types of actions
such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most
of the fraud detection approaches require a training dataset that contains
records of bot... | computer science |
10,240 | Accelerated Methods for Deep Reinforcement Learning | cs.LG | Deep reinforcement learning (RL) has achieved many recent successes, yet
experiment turn-around time remains a key bottleneck in research and in
practice. We investigate how to optimize existing deep RL algorithms for modern
computers, specifically for a combination of CPUs and GPUs. We confirm that
both policy gradien... | computer science |
10,241 | Can Autism be Catered with Artificial Intelligence-Assisted Intervention
Technology? A Literature Review | cs.HC | This article presents an extensive literature review of technology based
intervention methodologies for individuals facing Autism Spectrum Disorder
(ASD). Reviewed methodologies include: contemporary Computer Aided Systems
(CAS), Computer Vision Assisted Technologies (CVAT) and Virtual Reality (VR) or
Artificial Intell... | computer science |
10,242 | Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement
Learning | cs.RO | Rearranging objects on a tabletop surface by means of nonprehensile
manipulation is a task which requires skillful interaction with the physical
world. Usually, this is achieved by precisely modeling physical properties of
the objects, robot, and the environment for explicit planning. In contrast, as
explicitly modelin... | computer science |
10,243 | Some HCI Priorities for GDPR-Compliant Machine Learning | cs.HC | In this short paper, we consider the roles of HCI in enabling the better
governance of consequential machine learning systems using the rights and
obligations laid out in the recent 2016 EU General Data Protection Regulation
(GDPR)---a law which involves heavy interaction with people and systems.
Focussing on those are... | computer science |
10,244 | Snap Machine Learning | cs.LG | We describe an efficient, scalable machine learning library that enables very
fast training of generalized linear models. We demonstrate that our library can
remove the training time as a bottleneck for machine learning workloads,
opening the door to a range of new applications. For instance, it allows more
agile devel... | computer science |
10,245 | Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement
Learning | cs.IR | Sponsored search is an indispensable business model and a major revenue
contributor of almost all the search engines. From the advertisers' side,
participating in ranking the search results by paying for the sponsored search
advertisement to attract more awareness and purchase facilitates their
commercial goal. From th... | computer science |
10,246 | Similar Elements and Metric Labeling on Complete Graphs | cs.DS | We consider a problem that involves finding similar elements in a collection
of sets. The problem is motivated by applications in machine learning and
pattern recognition. We formulate the similar elements problem as an
optimization and give an efficient approximation algorithm that finds a
solution within a factor of ... | computer science |
10,247 | Learning-based Model Predictive Control for Safe Exploration and
Reinforcement Learning | cs.SY | Learning-based methods have been successful in solving complex control tasks
without significant prior knowledge about the system. However, these methods
typically do not provide any safety guarantees, which prevents their use in
safety-critical, real-world applications. In this paper, we present a
learning-based model... | computer science |
10,248 | Bolasso: model consistent Lasso estimation through the bootstrap | cs.LG | We consider the least-square linear regression problem with regularization by
the l1-norm, a problem usually referred to as the Lasso. In this paper, we
present a detailed asymptotic analysis of model consistency of the Lasso. For
various decays of the regularization parameter, we compute asymptotic
equivalents of the ... | computer science |
10,249 | Predictive Hypothesis Identification | cs.LG | While statistics focusses on hypothesis testing and on estimating (properties
of) the true sampling distribution, in machine learning the performance of
learning algorithms on future data is the primary issue. In this paper we
bridge the gap with a general principle (PHI) that identifies hypotheses with
best predictive... | computer science |
10,250 | Practical Robust Estimators for the Imprecise Dirichlet Model | math.ST | Walley's Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends
the classical Dirichlet model to a set of priors. It overcomes several
fundamental problems which other approaches to uncertainty suffer from. Yet, to
be useful in practice, one needs efficient ways for computing the
imprecise=robust sets or i... | computer science |
10,251 | Online Multi-task Learning with Hard Constraints | stat.ML | We discuss multi-task online learning when a decision maker has to deal
simultaneously with M tasks. The tasks are related, which is modeled by
imposing that the M-tuple of actions taken by the decision maker needs to
satisfy certain constraints. We give natural examples of such restrictions and
then discuss a general ... | computer science |
10,252 | Online Learning for Matrix Factorization and Sparse Coding | stat.ML | Sparse coding--that is, modelling data vectors as sparse linear combinations
of basis elements--is widely used in machine learning, neuroscience, signal
processing, and statistics. This paper focuses on the large-scale matrix
factorization problem that consists of learning the basis set, adapting it to
specific data. V... | computer science |
10,253 | A Geometric Approach to Sample Compression | cs.LG | The Sample Compression Conjecture of Littlestone & Warmuth has remained
unsolved for over two decades. This paper presents a systematic geometric
investigation of the compression of finite maximum concept classes. Simple
arrangements of hyperplanes in Hyperbolic space, and Piecewise-Linear
hyperplane arrangements, are ... | computer science |
10,254 | Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for
Sparsity Regularized Estimation | stat.ML | We analyze the convergence behaviour of a recently proposed algorithm for
regularized estimation called Dual Augmented Lagrangian (DAL). Our analysis is
based on a new interpretation of DAL as a proximal minimization algorithm. We
theoretically show under some conditions that DAL converges super-linearly in a
non-asymp... | computer science |
10,255 | Efficient Bayesian Learning in Social Networks with Gaussian Estimators | stat.AP | We consider a group of Bayesian agents who try to estimate a state of the
world $\theta$ through interaction on a social network. Each agent $v$
initially receives a private measurement of $\theta$: a number $S_v$ picked
from a Gaussian distribution with mean $\theta$ and standard deviation one.
Then, in each discrete ... | computer science |
10,256 | Principal Component Analysis with Contaminated Data: The High
Dimensional Case | stat.ML | We consider the dimensionality-reduction problem (finding a subspace
approximation of observed data) for contaminated data in the high dimensional
regime, where the number of observations is of the same magnitude as the number
of variables of each observation, and the data set contains some (arbitrarily)
corrupted obse... | computer science |
10,257 | Evolutionary Inference for Function-valued Traits: Gaussian Process
Regression on Phylogenies | cs.LG | Biological data objects often have both of the following features: (i) they
are functions rather than single numbers or vectors, and (ii) they are
correlated due to phylogenetic relationships. In this paper we give a flexible
statistical model for such data, by combining assumptions from phylogenetics
with Gaussian pro... | computer science |
10,258 | Clustering processes | cs.LG | The problem of clustering is considered, for the case when each data point is
a sample generated by a stationary ergodic process. We propose a very natural
asymptotic notion of consistency, and show that simple consistent algorithms
exist, under most general non-parametric assumptions. The notion of consistency
is as f... | computer science |
10,259 | Optimism in Reinforcement Learning and Kullback-Leibler Divergence | cs.LG | We consider model-based reinforcement learning in finite Markov De- cision
Processes (MDPs), focussing on so-called optimistic strategies. In MDPs,
optimism can be implemented by carrying out extended value it- erations under a
constraint of consistency with the estimated model tran- sition probabilities.
The UCRL2 alg... | computer science |
10,260 | Large Margin Multiclass Gaussian Classification with Differential
Privacy | stat.ML | As increasing amounts of sensitive personal information is aggregated into
data repositories, it has become important to develop mechanisms for processing
the data without revealing information about individual data instances. The
differential privacy model provides a framework for the development and
theoretical analy... | computer science |
10,261 | Clustering processes | cs.LG | The problem of clustering is considered, for the case when each data point is
a sample generated by a stationary ergodic process. We propose a very natural
asymptotic notion of consistency, and show that simple consistent algorithms
exist, under most general non-parametric assumptions. The notion of consistency
is as f... | computer science |
10,262 | Graph-Structured Multi-task Regression and an Efficient Optimization
Method for General Fused Lasso | stat.ML | We consider the problem of learning a structured multi-task regression, where
the output consists of multiple responses that are related by a graph and the
correlated response variables are dependent on the common inputs in a sparse
but synergistic manner. Previous methods such as l1/l2-regularized multi-task
regressio... | computer science |
10,263 | Information theoretic model validation for clustering | cs.IT | Model selection in clustering requires (i) to specify a suitable clustering
principle and (ii) to control the model order complexity by choosing an
appropriate number of clusters depending on the noise level in the data. We
advocate an information theoretic perspective where the uncertainty in the
measurements quantize... | computer science |
10,264 | Calibration and Internal no-Regret with Partial Monitoring | cs.GT | Calibrated strategies can be obtained by performing strategies that have no
internal regret in some auxiliary game. Such strategies can be constructed
explicitly with the use of Blackwell's approachability theorem, in an other
auxiliary game. We establish the converse: a strategy that approaches a convex
$B$-set can be... | computer science |
10,265 | Learning sparse gradients for variable selection and dimension reduction | stat.ML | Variable selection and dimension reduction are two commonly adopted
approaches for high-dimensional data analysis, but have traditionally been
treated separately. Here we propose an integrated approach, called sparse
gradient learning (SGL), for variable selection and dimension reduction via
learning the gradients of t... | computer science |
10,266 | A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering | cs.LG | We formulate weighted graph clustering as a prediction problem: given a
subset of edge weights we analyze the ability of graph clustering to predict
the remaining edge weights. This formulation enables practical and theoretical
comparison of different approaches to graph clustering as well as comparison of
graph cluste... | computer science |
10,267 | Sparse Inverse Covariance Selection via Alternating Linearization
Methods | cs.LG | Gaussian graphical models are of great interest in statistical learning.
Because the conditional independencies between different nodes correspond to
zero entries in the inverse covariance matrix of the Gaussian distribution, one
can learn the structure of the graph by estimating a sparse inverse covariance
matrix from... | computer science |
10,268 | From Sparse Signals to Sparse Residuals for Robust Sensing | stat.ML | One of the key challenges in sensor networks is the extraction of information
by fusing data from a multitude of distinct, but possibly unreliable sensors.
Recovering information from the maximum number of dependable sensors while
specifying the unreliable ones is critical for robust sensing. This sensing
task is formu... | computer science |
10,269 | Robust Matrix Decomposition with Outliers | stat.ML | Suppose a given observation matrix can be decomposed as the sum of a low-rank
matrix and a sparse matrix (outliers), and the goal is to recover these
individual components from the observed sum. Such additive decompositions have
applications in a variety of numerical problems including system
identification, latent var... | computer science |
10,270 | Online Learning: Beyond Regret | stat.ML | We study online learnability of a wide class of problems, extending the
results of (Rakhlin, Sridharan, Tewari, 2010) to general notions of performance
measure well beyond external regret. Our framework simultaneously captures such
well-known notions as internal and general Phi-regret, learning with
non-additive global... | computer science |
10,271 | PADDLE: Proximal Algorithm for Dual Dictionaries LEarning | cs.LG | Recently, considerable research efforts have been devoted to the design of
methods to learn from data overcomplete dictionaries for sparse coding.
However, learned dictionaries require the solution of an optimization problem
for coding new data. In order to overcome this drawback, we propose an
algorithm aimed at learn... | computer science |
10,272 | Classifying extremely imbalanced data sets | cs.LG | Imbalanced data sets containing much more background than signal instances
are very common in particle physics, and will also be characteristic for the
upcoming analyses of LHC data. Following up the work presented at ACAT 2008, we
use the multivariate technique presented there (a rule growing algorithm with
the meta-m... | computer science |
10,273 | An Inverse Power Method for Nonlinear Eigenproblems with Applications in
1-Spectral Clustering and Sparse PCA | cs.LG | Many problems in machine learning and statistics can be formulated as
(generalized) eigenproblems. In terms of the associated optimization problem,
computing linear eigenvectors amounts to finding critical points of a quadratic
function subject to quadratic constraints. In this paper we show that a certain
class of con... | computer science |
10,274 | An Introduction to Artificial Prediction Markets for Classification | stat.ML | Prediction markets are used in real life to predict outcomes of interest such
as presidential elections. This paper presents a mathematical theory of
artificial prediction markets for supervised learning of conditional
probability estimators. The artificial prediction market is a novel method for
fusing the prediction ... | computer science |
10,275 | Selecting the rank of truncated SVD by Maximum Approximation Capacity | cs.IT | Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$
approximation of a given input matrix. Selecting the appropriate rank $k$
defines a critical model order choice in most applications of SVD. To obtain a
principled cut-off criterion for the spectrum, we convert the underlying
optimization prob... | computer science |
10,276 | Active Clustering: Robust and Efficient Hierarchical Clustering using
Adaptively Selected Similarities | cs.IT | Hierarchical clustering based on pairwise similarities is a common tool used
in a broad range of scientific applications. However, in many problems it may
be expensive to obtain or compute similarities between the items to be
clustered. This paper investigates the hierarchical clustering of N items based
on a small sub... | computer science |
10,277 | Noisy matrix decomposition via convex relaxation: Optimal rates in high
dimensions | stat.ML | We analyze a class of estimators based on convex relaxation for solving
high-dimensional matrix decomposition problems. The observations are noisy
realizations of a linear transformation $\mathfrak{X}$ of the sum of an
approximately) low rank matrix $\Theta^\star$ with a second matrix
$\Gamma^\star$ endowed with a comp... | computer science |
10,278 | Neyman-Pearson classification, convexity and stochastic constraints | stat.ML | Motivated by problems of anomaly detection, this paper implements the
Neyman-Pearson paradigm to deal with asymmetric errors in binary classification
with a convex loss. Given a finite collection of classifiers, we combine them
and obtain a new classifier that satisfies simultaneously the two following
properties with ... | computer science |
10,279 | Sparse Volterra and Polynomial Regression Models: Recoverability and
Estimation | cs.LG | Volterra and polynomial regression models play a major role in nonlinear
system identification and inference tasks. Exciting applications ranging from
neuroscience to genome-wide association analysis build on these models with the
additional requirement of parsimony. This requirement has high interpretative
value, but ... | computer science |
10,280 | Estimating $β$-mixing coefficients | stat.ML | The literature on statistical learning for time series assumes the asymptotic
independence or ``mixing' of the data-generating process. These mixing
assumptions are never tested, nor are there methods for estimating mixing rates
from data. We give an estimator for the $\beta$-mixing rate based on a single
stationary sa... | computer science |
10,281 | Adapting to Non-stationarity with Growing Expert Ensembles | stat.ML | When dealing with time series with complex non-stationarities, low
retrospective regret on individual realizations is a more appropriate goal than
low prospective risk in expectation. Online learning algorithms provide
powerful guarantees of this form, and have often been proposed for use with
non-stationary processes ... | computer science |
10,282 | COMET: A Recipe for Learning and Using Large Ensembles on Massive Data | cs.LG | COMET is a single-pass MapReduce algorithm for learning on large-scale data.
It builds multiple random forest ensembles on distributed blocks of data and
merges them into a mega-ensemble. This approach is appropriate when learning
from massive-scale data that is too large to fit on a single machine. To get
the best acc... | computer science |
10,283 | Interpreting Graph Cuts as a Max-Product Algorithm | cs.LG | The maximum a posteriori (MAP) configuration of binary variable models with
submodular graph-structured energy functions can be found efficiently and
exactly by graph cuts. Max-product belief propagation (MP) has been shown to be
suboptimal on this class of energy functions by a canonical counterexample
where MP conver... | computer science |
10,284 | Self-configuration from a Machine-Learning Perspective | nlin.AO | The goal of machine learning is to provide solutions which are trained by
data or by experience coming from the environment. Many training algorithms
exist and some brilliant successes were achieved. But even in structured
environments for machine learning (e.g. data mining or board games), most
applications beyond the... | computer science |
10,285 | Behavior of Graph Laplacians on Manifolds with Boundary | cs.LG | In manifold learning, algorithms based on graph Laplacians constructed from
data have received considerable attention both in practical applications and
theoretical analysis. In particular, the convergence of graph Laplacians
obtained from sampled data to certain continuous operators has become an active
research topic... | computer science |
10,286 | Adaptive and Optimal Online Linear Regression on L1-balls | stat.ML | We consider the problem of online linear regression on individual sequences.
The goal in this paper is for the forecaster to output sequential predictions
which are, after T time rounds, almost as good as the ones output by the best
linear predictor in a given L1-ball in R^d. We consider both the cases where
the dimens... | computer science |
10,287 | b-Bit Minwise Hashing for Large-Scale Linear SVM | cs.LG | In this paper, we propose to (seamlessly) integrate b-bit minwise hashing
with linear SVM to substantially improve the training (and testing) efficiency
using much smaller memory, with essentially no loss of accuracy. Theoretically,
we prove that the resemblance matrix, the minwise hashing matrix, and the b-bit
minwise... | computer science |
10,288 | Ranking via Sinkhorn Propagation | stat.ML | It is of increasing importance to develop learning methods for ranking. In
contrast to many learning objectives, however, the ranking problem presents
difficulties due to the fact that the space of permutations is not smooth. In
this paper, we examine the class of rank-linear objective functions, which
includes popular... | computer science |
10,289 | A Dirty Model for Multiple Sparse Regression | cs.LG | Sparse linear regression -- finding an unknown vector from linear
measurements -- is now known to be possible with fewer samples than variables,
via methods like the LASSO. We consider the multiple sparse linear regression
problem, where several related vectors -- with partially shared support sets --
have to be recove... | computer science |
10,290 | Optimization with Sparsity-Inducing Penalties | cs.LG | Sparse estimation methods are aimed at using or obtaining parsimonious
representations of data or models. They were first dedicated to linear variable
selection but numerous extensions have now emerged such as structured sparsity
or kernel selection. It turns out that many of the related estimation problems
can be cast... | computer science |
10,291 | Accurate Estimators for Improving Minwise Hashing and b-Bit Minwise
Hashing | stat.ML | Minwise hashing is the standard technique in the context of search and
databases for efficiently estimating set (e.g., high-dimensional 0/1 vector)
similarities. Recently, b-bit minwise hashing was proposed which significantly
improves upon the original minwise hashing in practice by storing only the
lowest b bits of e... | computer science |
10,292 | Activized Learning: Transforming Passive to Active with Improved Label
Complexity | stat.ML | We study the theoretical advantages of active learning over passive learning.
Specifically, we prove that, in noise-free classifier learning for VC classes,
any passive learning algorithm can be transformed into an active learning
algorithm with asymptotically strictly superior label complexity for all
nontrivial targe... | computer science |
10,293 | Generalised elastic nets | cs.LG | The elastic net was introduced as a heuristic algorithm for combinatorial
optimisation and has been applied, among other problems, to biological
modelling. It has an energy function which trades off a fitness term against a
tension term. In the original formulation of the algorithm the tension term was
implicitly based... | computer science |
10,294 | Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise
Hashing and Comparisons with Vowpal Wabbit (VW) | cs.LG | We generated a dataset of 200 GB with 10^9 features, to test our recent b-bit
minwise hashing algorithms for training very large-scale logistic regression
and SVM. The results confirm our prior work that, compared with the VW hashing
algorithm (which has the same variance as random projections), b-bit minwise
hashing i... | computer science |
10,295 | Prediction of peptide bonding affinity: kernel methods for nonlinear
modeling | stat.ML | This paper presents regression models obtained from a process of blind
prediction of peptide binding affinity from provided descriptors for several
distinct datasets as part of the 2006 Comparative Evaluation of Prediction
Algorithms (COEPRA) contest. This paper finds that kernel partial least
squares, a nonlinear part... | computer science |
10,296 | On the trade-off between complexity and correlation decay in structural
learning algorithms | stat.ML | We consider the problem of learning the structure of Ising models (pairwise
binary Markov random fields) from i.i.d. samples. While several methods have
been proposed to accomplish this task, their relative merits and limitations
remain somewhat obscure. By analyzing a number of concrete examples, we show
that low-comp... | computer science |
10,297 | The Generalization Ability of Online Algorithms for Dependent Data | stat.ML | We study the generalization performance of online learning algorithms trained
on samples coming from a dependent source of data. We show that the
generalization error of any stable online algorithm concentrates around its
regret--an easily computable statistic of the online performance of the
algorithm--when the underl... | computer science |
10,298 | Discovering Emerging Topics in Social Streams via Link Anomaly Detection | stat.ML | Detection of emerging topics are now receiving renewed interest motivated by
the rapid growth of social networks. Conventional term-frequency-based
approaches may not be appropriate in this context, because the information
exchanged are not only texts but also images, URLs, and videos. We focus on the
social aspects of... | computer science |
10,299 | Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems | cs.LG | Crowdsourcing systems, in which numerous tasks are electronically distributed
to numerous "information piece-workers", have emerged as an effective paradigm
for human-powered solving of large scale problems in domains such as image
classification, data entry, optical character recognition, recommendation, and
proofread... | computer science |
10,300 | Information, learning and falsification | cs.IT | There are (at least) three approaches to quantifying information. The first,
algorithmic information or Kolmogorov complexity, takes events as strings and,
given a universal Turing machine, quantifies the information content of a
string as the length of the shortest program producing it. The second, Shannon
information... | computer science |
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