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12,001 | Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration | cs.LG | We study the combinatorial pure exploration problem Best-Set in stochastic
multi-armed bandits. In a Best-Set instance, we are given $n$ arms with unknown
reward distributions, as well as a family $\mathcal{F}$ of feasible subsets
over the arms. Our goal is to identify the feasible subset in $\mathcal{F}$
with the maxi... | computer science |
12,002 | Stochastic Reformulations of Linear Systems: Algorithms and Convergence
Theory | math.NA | We develop a family of reformulations of an arbitrary consistent linear
system into a stochastic problem. The reformulations are governed by two
user-defined parameters: a positive definite matrix defining a norm, and an
arbitrary discrete or continuous distribution over random matrices. Our
reformulation has several e... | computer science |
12,003 | Deep MIMO Detection | stat.ML | In this paper, we consider the use of deep neural networks in the context of
Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction
to deep learning and propose a modern neural network architecture suitable for
this detection task. First, we consider the case in which the MIMO channel is
constant... | computer science |
12,004 | Bayesian LSTMs in medicine | stat.ML | The medical field stands to see significant benefits from the recent advances
in deep learning. Knowing the uncertainty in the decision made by any machine
learning algorithm is of utmost importance for medical practitioners. This
study demonstrates the utility of using Bayesian LSTMs for classification of
medical time... | computer science |
12,005 | Stochastic Gradient Monomial Gamma Sampler | stat.ML | Recent advances in stochastic gradient techniques have made it possible to
estimate posterior distributions from large datasets via Markov Chain Monte
Carlo (MCMC). However, when the target posterior is multimodal, mixing
performance is often poor. This results in inadequate exploration of the
posterior distribution. A... | computer science |
12,006 | Beyond Volume: The Impact of Complex Healthcare Data on the Machine
Learning Pipeline | cs.CY | From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public healt... | computer science |
12,007 | Retrosynthetic reaction prediction using neural sequence-to-sequence
models | cs.LG | We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in... | computer science |
12,008 | Limitations on Variance-Reduction and Acceleration Schemes for Finite
Sum Optimization | math.OC | We study the conditions under which one is able to efficiently apply
variance-reduction and acceleration schemes on finite sum optimization
problems. First, we show that, perhaps surprisingly, the finite sum structure
by itself, is not sufficient for obtaining a complexity bound of
$\tilde{\cO}((n+L/\mu)\ln(1/\epsilon)... | computer science |
12,009 | Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic
Gradient Riemannian MCMC | stat.ML | It is challenging to develop stochastic gradient based scalable inference for
deep discrete latent variable models (LVMs), due to the difficulties in not
only computing the gradients, but also adapting the step sizes to different
latent factors and hidden layers. For the Poisson gamma belief network (PGBN),
a recently ... | computer science |
12,010 | Online Adaptive Machine Learning Based Algorithm for Implied Volatility
Surface Modeling | stat.ML | In this work, we design a machine learning based method, online adaptive
primal support vector regression (SVR), to model the implied volatility
surface. The algorithm proposed is the first derivation and implementation of
an online primal kernel SVR. It features enhancements that allow online
adaptive learning by embe... | computer science |
12,011 | Attributed Network Embedding for Learning in a Dynamic Environment | cs.SI | Network embedding leverages the node proximity manifested to learn a
low-dimensional node vector representation. The learned embeddings could
advance various learning tasks such as node classification, network clustering,
and link prediction. Most, if not all, of the existing work, is overwhelmingly
performed in the co... | computer science |
12,012 | Inductive Representation Learning on Large Graphs | cs.SI | Low-dimensional embeddings of nodes in large graphs have proved extremely
useful in a variety of prediction tasks, from content recommendation to
identifying protein functions. However, most existing approaches require that
all nodes in the graph are present during training of the embeddings; these
previous approaches ... | computer science |
12,013 | Graph Convolutional Matrix Completion | stat.ML | We consider matrix completion for recommender systems from the point of view
of link prediction on graphs. Interaction data such as movie ratings can be
represented by a bipartite user-item graph with labeled edges denoting observed
ratings. Building on recent progress in deep learning on graph-structured data,
we prop... | computer science |
12,014 | Pain-Free Random Differential Privacy with Sensitivity Sampling | cs.LG | Popular approaches to differential privacy, such as the Laplace and
exponential mechanisms, calibrate randomised smoothing through global
sensitivity of the target non-private function. Bounding such sensitivity is
often a prohibitively complex analytic calculation. As an alternative, we
propose a straightforward sampl... | computer science |
12,015 | Avoiding Discrimination through Causal Reasoning | stat.ML | Recent work on fairness in machine learning has focused on various
statistical discrimination criteria and how they trade off. Most of these
criteria are observational: They depend only on the joint distribution of
predictor, protected attribute, features, and outcome. While convenient to work
with, observational crite... | computer science |
12,016 | A Maximum Matching Algorithm for Basis Selection in Spectral Learning | cs.LG | We present a solution to scale spectral algorithms for learning sequence
functions. We are interested in the case where these functions are sparse (that
is, for most sequences they return 0). Spectral algorithms reduce the learning
problem to the task of computing an SVD decomposition over a special type of
matrix call... | computer science |
12,017 | Recovery Guarantees for One-hidden-layer Neural Networks | cs.LG | In this paper, we consider regression problems with one-hidden-layer neural
networks (1NNs). We distill some properties of activation functions that lead
to $\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth
parameters for the 1NN squared-loss objective. Most popular nonlinear
activation function... | computer science |
12,018 | On the Sampling Problem for Kernel Quadrature | stat.ML | The standard Kernel Quadrature method for numerical integration with random
point sets (also called Bayesian Monte Carlo) is known to converge in root mean
square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode
the smoothness and dimension of the integrand. However, an empirical
investigation re... | computer science |
12,019 | A Well-Tempered Landscape for Non-convex Robust Subspace Recovery | cs.LG | We present a mathematical analysis of a non-convex energy landscape for
Robust Subspace Recovery. We prove that an underlying subspace is the only
stationary point and local minimizer in a large neighborhood if a generic
condition holds for a dataset. We further show that if the generic condition is
satisfied, a geodes... | computer science |
12,020 | Analyzing the Robustness of Nearest Neighbors to Adversarial Examples | stat.ML | Motivated by safety-critical applications, test-time attacks on classifiers
via adversarial examples has recently received a great deal of attention.
However, there is a general lack of understanding on why adversarial examples
arise; whether they originate due to inherent properties of data or due to lack
of training ... | computer science |
12,021 | Recurrent Latent Variable Networks for Session-Based Recommendation | cs.IR | In this work, we attempt to ameliorate the impact of data sparsity in the
context of session-based recommendation. Specifically, we seek to devise a
machine learning mechanism capable of extracting subtle and complex underlying
temporal dynamics in the observed session data, so as to inform the
recommendation algorithm... | computer science |
12,022 | Provable Alternating Gradient Descent for Non-negative Matrix
Factorization with Strong Correlations | cs.LG | Non-negative matrix factorization is a basic tool for decomposing data into
the feature and weight matrices under non-negativity constraints, and in
practice is often solved in the alternating minimization framework. However, it
is unclear whether such algorithms can recover the ground-truth feature matrix
when the wei... | computer science |
12,023 | Leveraging Node Attributes for Incomplete Relational Data | stat.ML | Relational data are usually highly incomplete in practice, which inspires us
to leverage side information to improve the performance of community detection
and link prediction. This paper presents a Bayesian probabilistic approach that
incorporates various kinds of node attributes encoded in binary form in
relational m... | computer science |
12,024 | Accelerated Reinforcement Learning Algorithms with Nonparametric
Function Approximation for Opportunistic Spectrum Access | cs.IT | We study the problem of throughput maximization by predicting spectrum
opportunities using reinforcement learning. Our kernel-based reinforcement
learning approach is coupled with a sparsification technique that efficiently
captures the environment states to control dimensionality and finds the best
possible channel ac... | computer science |
12,025 | Information Potential Auto-Encoders | cs.LG | In this paper, we suggest a framework to make use of mutual information as a
regularization criterion to train Auto-Encoders (AEs). In the proposed
framework, AEs are regularized by minimization of the mutual information
between input and encoding variables of AEs during the training phase. In order
to estimate the ent... | computer science |
12,026 | Differentially Private Learning of Undirected Graphical Models using
Collective Graphical Models | cs.LG | We investigate the problem of learning discrete, undirected graphical models
in a differentially private way. We show that the approach of releasing noisy
sufficient statistics using the Laplace mechanism achieves a good trade-off
between privacy, utility, and practicality. A naive learning algorithm that
uses the nois... | computer science |
12,027 | Generalization for Adaptively-chosen Estimators via Stable Median | cs.LG | Datasets are often reused to perform multiple statistical analyses in an
adaptive way, in which each analysis may depend on the outcomes of previous
analyses on the same dataset. Standard statistical guarantees do not account
for these dependencies and little is known about how to provably avoid
overfitting and false d... | computer science |
12,028 | A framework for Multi-A(rmed)/B(andit) testing with online FDR control | stat.ML | We propose an alternative framework to existing setups for controlling false
alarms when multiple A/B tests are run over time. This setup arises in many
practical applications, e.g. when pharmaceutical companies test new treatment
options against control pills for different diseases, or when internet
companies test the... | computer science |
12,029 | Control Variates for Stochastic Gradient MCMC | stat.CO | It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly
with dataset size. A popular class of methods for solving this issue is
stochastic gradient MCMC. These methods use a noisy estimate of the gradient of
the log posterior, which reduces the per iteration computational cost of the
algorithm. Despi... | computer science |
12,030 | Addressing Item-Cold Start Problem in Recommendation Systems using Model
Based Approach and Deep Learning | cs.IR | Traditional recommendation systems rely on past usage data in order to
generate new recommendations. Those approaches fail to generate sensible
recommendations for new users and items into the system due to missing
information about their past interactions. In this paper, we propose a solution
for successfully addressi... | computer science |
12,031 | On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex
Sparse Learning in High Dimensions | stat.ML | We propose a DC proximal Newton algorithm for solving nonconvex regularized
sparse learning problems in high dimensions. Our proposed algorithm integrates
the proximal Newton algorithm with multi-stage convex relaxation based on the
difference of convex (DC) programming, and enjoys both strong computational and
statist... | computer science |
12,032 | Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint | cs.LG | We propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegative
matrix factorization problem under a simplicial constraint. Compared with
existing solutions, this algorithm is extremely simple to implement, and has
almost no hyperparameters to be tuned. Building on the recent advances of FW
algorithms in non... | computer science |
12,033 | Inference in Deep Networks in High Dimensions | cs.LG | Deep generative networks provide a powerful tool for modeling complex data in
a wide range of applications. In inverse problems that use these networks as
generative priors on data, one must often perform inference of the inputs of
the networks from the outputs. Inference is also required for sampling during
stochastic... | computer science |
12,034 | A Unified Approach to Adaptive Regularization in Online and Stochastic
Optimization | cs.LG | We describe a framework for deriving and analyzing online optimization
algorithms that incorporate adaptive, data-dependent regularization, also
termed preconditioning. Such algorithms have been proven useful in stochastic
optimization by reshaping the gradients according to the geometry of the data.
Our framework capt... | computer science |
12,035 | Most Ligand-Based Benchmarks Measure Overfitting Rather than Accuracy | cs.LG | Undetected overfitting can occur when there are significant redundancies
between training and validation data. We describe AVE, a new measure of
training-validation redundancy for ligand-based classification problems that
accounts for the similarity amongst inactive molecules as well as active. We
investigated nine wid... | computer science |
12,036 | Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed
Anomaly Detection via Cache Lookups | cs.DB | Anomaly detection is one of the frequent and important subroutines deployed
in large-scale data processing systems. Even being a well-studied topic,
existing techniques for unsupervised anomaly detection require storing
significant amounts of data, which is prohibitive from memory and latency
perspective. In the big-da... | computer science |
12,037 | The Theory is Predictive, but is it Complete? An Application to Human
Perception of Randomness | cs.LG | When we test a theory using data, it is common to focus on correctness: do
the predictions of the theory match what we see in the data? But we also care
about completeness: how much of the predictable variation in the data is
captured by the theory? This question is difficult to answer, because in
general we do not kno... | computer science |
12,038 | Improved Optimization of Finite Sums with Minibatch Stochastic Variance
Reduced Proximal Iterations | math.OC | We present novel minibatch stochastic optimization methods for empirical risk
minimization problems, the methods efficiently leverage variance reduced
first-order and sub-sampled higher-order information to accelerate the
convergence speed. For quadratic objectives, we prove improved iteration
complexity over state-of-... | computer science |
12,039 | Constrained Bayesian Optimization with Noisy Experiments | stat.ML | Randomized experiments are the gold standard for evaluating the effects of
changes to real-world systems, including Internet services. Data in these tests
may be difficult to collect and outcomes may have high variance, resulting in
potentially large measurement error. Bayesian optimization is a promising
technique for... | computer science |
12,040 | Compressive Statistical Learning with Random Feature Moments | stat.ML | We describe a general framework --compressive statistical learning-- for
resource-efficient large-scale learning: the training collection is compressed
in one pass into a low-dimensional sketch (a vector of random empirical
generalized moments) that captures the information relevant to the considered
learning task. A n... | computer science |
12,041 | On Sampling Strategies for Neural Network-based Collaborative Filtering | cs.LG | Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive ... | computer science |
12,042 | Reservoir Computing on the Hypersphere | cs.LG | Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs)
framework, frequently used for sequence learning and time series prediction.
The RC system consists of a random fixed-weight RNN (the input-hidden reservoir
layer) and a classifier (the hidden-output readout layer). Here we focus on the
sequence lear... | computer science |
12,043 | GPU-acceleration for Large-scale Tree Boosting | stat.ML | In this paper, we present a novel massively parallel algorithm for
accelerating the decision tree building procedure on GPUs (Graphics Processing
Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and
random forests training. Previous GPU based tree building algorithms are based
on parallel multi-... | computer science |
12,044 | Efficiency of quantum versus classical annealing in non-convex learning
problems | cs.LG | Quantum annealers aim at solving non-convex optimization problems by
exploiting cooperative tunneling effects to escape local minima. The underlying
idea consists in designing a classical energy function whose ground states are
the sought optimal solutions of the original optimization problem and add a
controllable qua... | computer science |
12,045 | On conditional parity as a notion of non-discrimination in machine
learning | stat.ML | We identify conditional parity as a general notion of non-discrimination in
machine learning. In fact, several recently proposed notions of
non-discrimination, including a few counterfactual notions, are instances of
conditional parity. We show that conditional parity is amenable to statistical
analysis by studying ran... | computer science |
12,046 | Fast and robust tensor decomposition with applications to dictionary
learning | cs.LG | We develop fast spectral algorithms for tensor decomposition that match the
robustness guarantees of the best known polynomial-time algorithms for this
problem based on the sum-of-squares (SOS) semidefinite programming hierarchy.
Our algorithms can decompose a 4-tensor with $n$-dimensional orthonormal
components in t... | computer science |
12,047 | Unsupervised Feature Selection Based on Space Filling Concept | stat.ML | The paper deals with the adaptation of a new measure for the unsupervised
feature selection problems. The proposed measure is based on space filling
concept and is called the coverage measure. This measure was used for judging
the quality of an experimental space filling design. In the present work, the
coverage measur... | computer science |
12,048 | Energy-Based Sequence GANs for Recommendation and Their Connection to
Imitation Learning | cs.IR | Recommender systems aim to find an accurate and efficient mapping from
historic data of user-preferred items to a new item that is to be liked by a
user. Towards this goal, energy-based sequence generative adversarial nets
(EB-SeqGANs) are adopted for recommendation by learning a generative model for
the time series of... | computer science |
12,049 | Image classification using local tensor singular value decompositions | stat.ML | From linear classifiers to neural networks, image classification has been a
widely explored topic in mathematics, and many algorithms have proven to be
effective classifiers. However, the most accurate classifiers typically have
significantly high storage costs, or require complicated procedures that may be
computation... | computer science |
12,050 | Interpretability via Model Extraction | cs.LG | The ability to interpret machine learning models has become increasingly
important now that machine learning is used to inform consequential decisions.
We propose an approach called model extraction for interpreting complex,
blackbox models. Our approach approximates the complex model using a much more
interpretable mo... | computer science |
12,051 | Generalising Random Forest Parameter Optimisation to Include Stability
and Cost | stat.ML | Random forests are among the most popular classification and regression
methods used in industrial applications. To be effective, the parameters of
random forests must be carefully tuned. This is usually done by choosing values
that minimize the prediction error on a held out dataset. We argue that error
reduction is o... | computer science |
12,052 | Prepaid or Postpaid? That is the question. Novel Methods of Subscription
Type Prediction in Mobile Phone Services | cs.SI | In this paper we investigate the behavioural differences between mobile phone
customers with prepaid and postpaid subscriptions. Our study reveals that (a)
postpaid customers are more active in terms of service usage and (b) there are
strong structural correlations in the mobile phone call network as connections
betwee... | computer science |
12,053 | Dimensionality reduction with missing values imputation | cs.LG | In this study, we propose a new statical approach for high-dimensionality
reduction of heterogenous data that limits the curse of dimensionality and
deals with missing values. To handle these latter, we propose to use the Random
Forest imputation's method. The main purpose here is to extract useful
information and so r... | computer science |
12,054 | Fair Pipelines | cs.CY | This work facilitates ensuring fairness of machine learning in the real world
by decoupling fairness considerations in compound decisions. In particular,
this work studies how fairness propagates through a compound decision-making
processes, which we call a pipeline. Prior work in algorithmic fairness only
focuses on f... | computer science |
12,055 | Parle: parallelizing stochastic gradient descent | cs.LG | We propose a new algorithm called Parle for parallel training of deep
networks that converges 2-4x faster than a data-parallel implementation of SGD,
while achieving significantly improved error rates that are nearly
state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100,
without introducing any additi... | computer science |
12,056 | Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback | cs.LG | Recommendation is the task of improving customer experience through
personalized recommendation based on users' past feedback. In this paper, we
investigate the most common scenario: the user-item (U-I) matrix of implicit
feedback. Even though many recommendation approaches are designed based on
implicit feedback, they... | computer science |
12,057 | Discriminatory Transfer | cs.CY | We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show th... | computer science |
12,058 | Robust Optimization for Non-Convex Objectives | cs.LG | We consider robust optimization problems, where the goal is to optimize in
the worst case over a class of objective functions. We develop a reduction from
robust improper optimization to Bayesian optimization: given an oracle that
returns $\alpha$-approximate solutions for distributions over objectives, we
compute a di... | computer science |
12,059 | A Data Science Approach to Understanding Residential Water Contamination
in Flint | cs.LG | When the residents of Flint learned that lead had contaminated their water
system, the local government made water-testing kits available to them free of
charge. The city government published the results of these tests, creating a
valuable dataset that is key to understanding the causes and extent of the lead
contamina... | computer science |
12,060 | End-to-End Learning of Semantic Grasping | cs.RO | We consider the task of semantic robotic grasping, in which a robot picks up
an object of a user-specified class using only monocular images. Inspired by
the two-stream hypothesis of visual reasoning, we present a semantic grasping
framework that learns object detection, classification, and grasp planning in
an end-to-... | computer science |
12,061 | Simple Classification using Binary Data | cs.LG | Binary, or one-bit, representations of data arise naturally in many
applications, and are appealing in both hardware implementations and algorithm
design. In this work, we study the problem of data classification from binary
data and propose a framework with low computation and resource costs. We
illustrate the utility... | computer science |
12,062 | Global optimality conditions for deep neural networks | cs.LG | We study the error landscape of deep linear and nonlinear neural networks
with the squared error loss. Minimizing the loss of a deep linear neural
network is a nonconvex problem, and despite recent progress, our understanding
of this loss surface is still incomplete. For deep linear networks, we present
necessary and s... | computer science |
12,063 | Composition Properties of Inferential Privacy for Time-Series Data | cs.LG | With the proliferation of mobile devices and the internet of things,
developing principled solutions for privacy in time series applications has
become increasingly important. While differential privacy is the gold standard
for database privacy, many time series applications require a different kind of
guarantee, and a... | computer science |
12,064 | Semi-Supervised Haptic Material Recognition for Robots using Generative
Adversarial Networks | cs.RO | Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as "bring me the metal coffee mug", and recognizing plastic versus metal is
crucial whe... | computer science |
12,065 | Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization | math.OC | In this paper, we consider multi-stage stochastic optimization problems with
convex objectives and conic constraints at each stage. We present a new
stochastic first-order method, namely the dynamic stochastic approximation
(DSA) algorithm, for solving these types of stochastic optimization problems.
We show that DSA c... | computer science |
12,066 | Deep Learning for Real Time Crime Forecasting | math.NA | Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatica... | computer science |
12,067 | Underdamped Langevin MCMC: A non-asymptotic analysis | stat.ML | We study the underdamped Langevin diffusion when the log of the target
distribution is smooth and strongly concave. We present a MCMC algorithm based
on its discretization and show that it achieves $\varepsilon$ error (in
2-Wasserstein distance) in $\mathcal{O}(\sqrt{d}/\varepsilon)$ steps. This is a
significant improv... | computer science |
12,068 | Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via
Ranking | stat.ML | Methods that learn representations of nodes in a graph play a critical role
in network analysis since they enable many downstream learning tasks. We
propose Graph2Gauss - an approach that can efficiently learn versatile node
embeddings on large scale (attributed) graphs that show strong performance on
tasks such as lin... | computer science |
12,069 | Process Monitoring on Sequences of System Call Count Vectors | cs.CR | We introduce a methodology for efficient monitoring of processes running on
hosts in a corporate network. The methodology is based on collecting streams of
system calls produced by all or selected processes on the hosts, and sending
them over the network to a monitoring server, where machine learning algorithms
are use... | computer science |
12,070 | Influence of Resampling on Accuracy of Imbalanced Classification | stat.ML | In many real-world binary classification tasks (e.g. detection of certain
objects from images), an available dataset is imbalanced, i.e., it has much
less representatives of a one class (a minor class), than of another.
Generally, accurate prediction of the minor class is crucial but it's hard to
achieve since there is... | computer science |
12,071 | Model Selection for Anomaly Detection | stat.ML | Anomaly detection based on one-class classification algorithms is broadly
used in many applied domains like image processing (e.g. detection of whether a
patient is "cancerous" or "healthy" from mammography image), network intrusion
detection, etc. Performance of an anomaly detection algorithm crucially depends
on a ke... | computer science |
12,072 | Predicting multicellular function through multi-layer tissue networks | cs.LG | Motivation: Understanding functions of proteins in specific human tissues is
essential for insights into disease diagnostics and therapeutics, yet
prediction of tissue-specific cellular function remains a critical challenge
for biomedicine.
Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature
... | computer science |
12,073 | An Ensemble Boosting Model for Predicting Transfer to the Pediatric
Intensive Care Unit | cs.LG | Our work focuses on the problem of predicting the transfer of pediatric
patients from the general ward of a hospital to the pediatric intensive care
unit. Using data collected over 5.5 years from the electronic health records of
two medical facilities, we develop classifiers based on adaptive boosting and
gradient tree... | computer science |
12,074 | Graph learning under sparsity priors | cs.LG | Graph signals offer a very generic and natural representation for data that
lives on networks or irregular structures. The actual data structure is however
often unknown a priori but can sometimes be estimated from the knowledge of the
application domain. If this is not possible, the data structure has to be
inferred f... | computer science |
12,075 | Improving Gibbs Sampler Scan Quality with DoGS | stat.ML | The pairwise influence matrix of Dobrushin has long been used as an
analytical tool to bound the rate of convergence of Gibbs sampling. In this
work, we use Dobrushin influence as the basis of a practical tool to certify
and efficiently improve the quality of a discrete Gibbs sampler. Our
Dobrushin-optimized Gibbs samp... | computer science |
12,076 | Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical
Viewpoints | cs.LG | Algorithm-dependent generalization error bounds are central to statistical
learning theory. A learning algorithm may use a large hypothesis space, but the
limited number of iterations controls its model capacity and generalization
error. The impacts of stochastic gradient methods on generalization error for
non-convex ... | computer science |
12,077 | Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning | math.ST | We investigate a family of regression problems in a semi-supervised setting.
The task is to assign real-valued labels to a set of $n$ sample points,
provided a small training subset of $N$ labeled points. A goal of
semi-supervised learning is to take advantage of the (geometric) structure
provided by the large number o... | computer science |
12,078 | Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite
Optimization | math.OC | Due to their simplicity and excellent performance, parallel asynchronous
variants of stochastic gradient descent have become popular methods to solve a
wide range of large-scale optimization problems on multi-core architectures.
Yet, despite their practical success, support for nonsmooth objectives is still
lacking, ma... | computer science |
12,079 | Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex
Optimization | stat.ML | We present a unified framework to analyze the global convergence of Langevin
dynamics based algorithms for nonconvex finite-sum optimization with $n$
component functions. At the core of our analysis is a direct analysis of the
ergodicity of the numerical approximations to Langevin dynamics, which leads to
faster conver... | computer science |
12,080 | Engineering fast multilevel support vector machines | cs.LG | The computational complexity of solving nonlinear support vector machine
(SVM) is prohibitive on large-scale data. In particular, this issue becomes
very sensitive when the data represents additional difficulties such as highly
imbalanced class sizes. Typically, nonlinear kernels produce significantly
higher classifica... | computer science |
12,081 | Prediction of amino acid side chain conformation using a deep neural
network | cs.LG | A deep neural network based architecture was constructed to predict amino
acid side chain conformation with unprecedented accuracy. Amino acid side chain
conformation prediction is essential for protein homology modeling and protein
design. Current widely-adopted methods use physics-based energy functions to
evaluate s... | computer science |
12,082 | TensorLayer: A Versatile Library for Efficient Deep Learning Development | cs.LG | Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, pre... | computer science |
12,083 | A Robust Multi-Batch L-BFGS Method for Machine Learning | math.OC | This paper describes an implementation of the L-BFGS method designed to deal
with two adversarial situations. The first occurs in distributed computing
environments where some of the computational nodes devoted to the evaluation of
the function and gradient are unable to return results on time. A similar
challenge occu... | computer science |
12,084 | Quantum machine learning: a classical perspective | cs.LG | Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication al... | computer science |
12,085 | MLBench: How Good Are Machine Learning Clouds for Binary Classification
Tasks on Structured Data? | cs.DC | We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task,... | computer science |
12,086 | Efficient Regret Minimization in Non-Convex Games | cs.LG | We consider regret minimization in repeated games with non-convex loss
functions. Minimizing the standard notion of regret is computationally
intractable. Thus, we define a natural notion of regret which permits efficient
optimization and generalizes offline guarantees for convergence to an
approximate local optimum. W... | computer science |
12,087 | Machine learning for neural decoding | cs.LG | While machine learning tools have been rapidly advancing, the majority of
neural decoding approaches still use last century's methods. Improving the
performance of neural decoding algorithms allows us to better understand what
information is contained in the brain, and can help advance engineering
applications such as ... | computer science |
12,088 | On the convergence properties of a $K$-step averaging stochastic
gradient descent algorithm for nonconvex optimization | cs.LG | Despite their popularity, the practical performance of asynchronous
stochastic gradient descent methods (ASGD) for solving large scale machine
learning problems are not as good as theoretical results indicate. We adopt and
analyze a synchronous K-step averaging stochastic gradient descent algorithm
which we call K-AVG.... | computer science |
12,089 | Variance-Reduced Stochastic Learning under Random Reshuffling | cs.LG | Several useful variance-reduced stochastic gradient algorithms, such as SVRG,
SAGA, Finito, and SAG, have been proposed to minimize empirical risks with
linear convergence properties to the exact minimizer. The existing convergence
results assume uniform data sampling with replacement. However, it has been
observed in ... | computer science |
12,090 | Variance-Reduced Stochastic Learning by Networked Agents under Random
Reshuffling | cs.LG | A new amortized variance-reduced gradient (AVRG) algorithm was developed in
[1], which has constant storage requirement in comparison to SAGA and balanced
gradient computations in comparison to SVRG. One key advantage of the AVRG
strategy is its amenability to decentralized implementations. In this work, we
show how AV... | computer science |
12,091 | A Bootstrap Method for Error Estimation in Randomized Matrix
Multiplication | stat.ML | In recent years, randomized methods for numerical linear algebra have
received growing interest as a general approach to large-scale problems.
Typically, the essential ingredient of these methods is some form of randomized
dimension reduction, which accelerates computations, but also creates random
approximation error.... | computer science |
12,092 | Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls | cs.LG | We propose a rank-$k$ variant of the classical Frank-Wolfe algorithm to solve
convex optimization over a trace-norm ball. Our algorithm replaces the top
singular-vector computation ($1$-SVD) in Frank-Wolfe with a top-$k$
singular-vector computation ($k$-SVD), which can be done by repeatedly applying
$1$-SVD $k$ times. ... | computer science |
12,093 | Learning non-parametric Markov networks with mutual information | cs.LG | We propose a method for learning Markov network structures for continuous
data without invoking any assumptions about the distribution of the variables.
The method makes use of previous work on a non-parametric estimator for mutual
information which is used to create a non-parametric test for multivariate
conditional i... | computer science |
12,094 | Anomaly Detection in Multivariate Non-stationary Time Series for
Automatic DBMS Diagnosis | stat.ML | Anomaly detection in database management systems (DBMSs) is difficult because
of increasing number of statistics (stat) and event metrics in big data system.
In this paper, I propose an automatic DBMS diagnosis system that detects
anomaly periods with abnormal DB stat metrics and finds causal events in the
periods. Rec... | computer science |
12,095 | Simulated Annealing with Levy Distribution for Fast Matrix
Factorization-Based Collaborative Filtering | cs.LG | Matrix factorization is one of the best approaches for collaborative
filtering, because of its high accuracy in presenting users and items latent
factors. The main disadvantages of matrix factorization are its complexity, and
being very hard to be parallelized, specially with very large matrices. In this
paper, we intr... | computer science |
12,096 | Using Deep Neural Networks to Automate Large Scale Statistical Analysis
for Big Data Applications | stat.ML | Statistical analysis (SA) is a complex process to deduce population
properties from analysis of data. It usually takes a well-trained analyst to
successfully perform SA, and it becomes extremely challenging to apply SA to
big data applications. We propose to use deep neural networks to automate the
SA process. In parti... | computer science |
12,097 | Jumping across biomedical contexts using compressive data fusion | cs.LG | Motivation: The rapid growth of diverse biological data allows us to consider
interactions between a variety of objects, such as genes, chemicals, molecular
signatures, diseases, pathways and environmental exposures. Often, any pair of
objects--such as a gene and a disease--can be related in different ways, for
example... | computer science |
12,098 | Sparse Coding and Autoencoders | cs.LG | In "Dictionary Learning" one tries to recover incoherent matrices $A^* \in
\mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are assumed
to be normalized) and sparse vectors $x^* \in \mathbb{R}^h$ with a small
support of size $h^p$ for some $0 <p < 1$ while having access to observations
$y \in \mathbb{... | computer science |
12,099 | Direct-Manipulation Visualization of Deep Networks | cs.LG | The recent successes of deep learning have led to a wave of interest from
non-experts. Gaining an understanding of this technology, however, is
difficult. While the theory is important, it is also helpful for novices to
develop an intuitive feel for the effect of different hyperparameters and
structural variations. We ... | computer science |
12,100 | ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural
Networks without Training Substitute Models | stat.ML | Deep neural networks (DNNs) are one of the most prominent technologies of our
time, as they achieve state-of-the-art performance in many machine learning
tasks, including but not limited to image classification, text mining, and
speech processing. However, recent research on DNNs has indicated
ever-increasing concern o... | computer science |
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