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12,301 | Overcoming data scarcity with transfer learning | cs.LG | Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measure... | computer science |
12,302 | The Multi-layer Information Bottleneck Problem | stat.ML | The muti-layer information bottleneck (IB) problem, where information is
propagated (or successively refined) from layer to layer, is considered. Based
on information forwarded by the preceding layer, each stage of the network is
required to preserve a certain level of relevance with regards to a specific
hidden variab... | computer science |
12,303 | Near-Optimal Discrete Optimization for Experimental Design: A Regret
Minimization Approach | stat.ML | The experimental design problem concerns the selection of k points from a
potentially large design pool of p-dimensional vectors, so as to maximize the
statistical efficiency regressed on the selected k design points. Statistical
efficiency is measured by optimality criteria, including A(verage),
D(eterminant), T(race)... | computer science |
12,304 | Random gradient extrapolation for distributed and stochastic
optimization | math.OC | In this paper, we consider a class of finite-sum convex optimization problems
defined over a distributed multiagent network with $m$ agents connected to a
central server. In particular, the objective function consists of the average
of $m$ ($\ge 1$) smooth components associated with each network agent together
with a s... | computer science |
12,305 | BoostJet: Towards Combining Statistical Aggregates with Neural
Embeddings for Recommendations | cs.IR | Recommenders have become widely popular in recent years because of their
broader applicability in many e-commerce applications. These applications rely
on recommenders for generating advertisements for various offers or providing
content recommendations. However, the quality of the generated recommendations
depends on ... | computer science |
12,306 | Gaussian Process Decentralized Data Fusion Meets Transfer Learning in
Large-Scale Distributed Cooperative Perception | stat.ML | This paper presents novel Gaussian process decentralized data fusion
algorithms exploiting the notion of agent-centric support sets for distributed
cooperative perception of large-scale environmental phenomena. To overcome the
limitations of scale in existing works, our proposed algorithms allow every
mobile sensing ag... | computer science |
12,307 | Improving Palliative Care with Deep Learning | cs.CY | Improving the quality of end-of-life care for hospitalized patients is a
priority for healthcare organizations. Studies have shown that physicians tend
to over-estimate prognoses, which in combination with treatment inertia results
in a mismatch between patients wishes and actual care at the end of life. We
describe a ... | computer science |
12,308 | Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein
Step Size | stat.ML | Learning representation from relative similarity comparisons, often called
ordinal embedding, gains rising attention in recent years. Most of the existing
methods are batch methods designed mainly based on the convex optimization,
say, the projected gradient descent method. However, they are generally
time-consuming du... | computer science |
12,309 | How Wrong Am I? - Studying Adversarial Examples and their Impact on
Uncertainty in Gaussian Process Machine Learning Models | cs.CR | Machine learning models are vulnerable to Adversarial Examples: minor
perturbations to input samples intended to deliberately cause
misclassification. Current defenses against adversarial examples, especially
for Deep Neural Networks (DNN), are primarily derived from empirical
developments, and their security guarantee... | computer science |
12,310 | Principal Boundary on Riemannian Manifolds | stat.ML | We revisit the classification problem and focus on nonlinear methods for
classification on manifolds. For multivariate datasets lying on an embedded
nonlinear Riemannian manifold within the higher-dimensional space, our aim is
to acquire a classification boundary between the classes with labels. Motivated
by the princi... | computer science |
12,311 | Techniques for proving Asynchronous Convergence results for Markov Chain
Monte Carlo methods | stat.ML | Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding
widespread use in applied statistics and machine learning. These often lead to
difficult computational problems, which are increasingly being solved on
parallel and distributed systems such as compute clusters. Recent work has
proposed running i... | computer science |
12,312 | Decentralized High-Dimensional Bayesian Optimization with Factor Graphs | stat.ML | This paper presents a novel decentralized high-dimensional Bayesian
optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms,
can exploit the interdependent effects of various input components on the
output of the unknown objective function f for boosting the BO performance and
still preserve scala... | computer science |
12,313 | Estimation Considerations in Contextual Bandits | stat.ML | Although many contextual bandit algorithms have similar theoretical
guarantees, the characteristics of real-world applications oftentimes result in
large performance dissimilarities across algorithms. We study a consideration
for the exploration vs. exploitation framework that does not arise in
non-contextual bandits: ... | computer science |
12,314 | On Convergence of Epanechnikov Mean Shift | stat.ML | Epanechnikov Mean Shift is a simple yet empirically very effective algorithm
for clustering. It localizes the centroids of data clusters via estimating
modes of the probability distribution that generates the data points, using the
`optimal' Epanechnikov kernel density estimator. However, since the procedure
involves n... | computer science |
12,315 | Glitch Classification and Clustering for LIGO with Deep Transfer
Learning | cs.LG | The detection of gravitational waves with LIGO and Virgo requires a detailed
understanding of the response of these instruments in the presence of
environmental and instrumental noise. Of particular interest is the study of
anomalous non-Gaussian noise transients known as glitches, since their high
occurrence rate in L... | computer science |
12,316 | Optimistic Robust Optimization With Applications To Machine Learning | stat.ML | Robust Optimization has traditionally taken a pessimistic, or worst-case
viewpoint of uncertainty which is motivated by a desire to find sets of optimal
policies that maintain feasibility under a variety of operating conditions. In
this paper, we explore an optimistic, or best-case view of uncertainty and show
that it ... | computer science |
12,317 | Hierarchical internal representation of spectral features in deep
convolutional networks trained for EEG decoding | cs.LG | Recently, there is increasing interest and research on the interpretability
of machine learning models, for example how they transform and internally
represent EEG signals in Brain-Computer Interface (BCI) applications. This can
help to understand the limits of the model and how it may be improved, in
addition to possi... | computer science |
12,318 | Training large margin host-pathogen protein-protein interaction
predictors | cs.LG | Detection of protein-protein interactions (PPIs) plays a vital role in
molecular biology. Particularly, infections are caused by the interactions of
host and pathogen proteins. It is important to identify host-pathogen
interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI pr... | computer science |
12,319 | SNeCT: Scalable network constrained Tucker decomposition for integrative
multi-platform data analysis | cs.LG | Motivation: How do we integratively analyze large-scale multi-platform
genomic data that are high dimensional and sparse? Furthermore, how can we
incorporate prior knowledge, such as the association between genes, in the
analysis systematically? Method: To solve this problem, we propose a Scalable
Network Constrained T... | computer science |
12,320 | Post-hoc labeling of arbitrary EEG recordings for data-efficient
evaluation of neural decoding methods | cs.LG | Many cognitive, sensory and motor processes have correlates in oscillatory
neural sources, which are embedded as a subspace into the recorded brain
signals. Decoding such processes from noisy
magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the
use of data-driven analysis methods. The objectiv... | computer science |
12,321 | Learning User Preferences to Incentivize Exploration in the Sharing
Economy | cs.LG | We study platforms in the sharing economy and discuss the need for
incentivizing users to explore options that otherwise would not be chosen. For
instance, rental platforms such as Airbnb typically rely on customer reviews to
provide users with relevant information about different options. Yet, often a
large fraction o... | computer science |
12,322 | Relief-Based Feature Selection: Introduction and Review | cs.DS | Feature selection plays a critical role in data mining, driven by increasing
feature dimensionality in target problems and growing interest in advanced but
computationally expensive methodologies able to model complex associations.
Specifically, there is a need for feature selection methods that are
computationally eff... | computer science |
12,323 | Leverage Score Sampling for Faster Accelerated Regression and ERM | stat.ML | Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b
\in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution
to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x
- b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot
s\cdot\log\eps... | computer science |
12,324 | Calibration for the (Computationally-Identifiable) Masses | cs.LG | As algorithms increasingly inform and influence decisions made about
individuals, it becomes increasingly important to address concerns that these
algorithms might be discriminatory. The output of an algorithm can be
discriminatory for many reasons, most notably: (1) the data used to train the
algorithm might be biased... | computer science |
12,325 | Practical Hash Functions for Similarity Estimation and Dimensionality
Reduction | stat.ML | Hashing is a basic tool for dimensionality reduction employed in several
aspects of machine learning. However, the perfomance analysis is often carried
out under the abstract assumption that a truly random unit cost hash function
is used, without concern for which concrete hash function is employed. The
concrete hash f... | computer science |
12,326 | Deep Learning for Real-Time Crime Forecasting and its Ternarization | cs.LG | Real-time crime forecasting is important. However, accurate prediction of
when and where the next crime will happen is difficult. No known physical model
provides a reasonable approximation to such a complex system. Historical crime
data are sparse in both space and time and the signal of interests is weak. In
this wor... | computer science |
12,327 | Critical Learning Periods in Deep Neural Networks | cs.LG | Critical periods are phases in the early development of humans and animals
during which experience can affect the structure of neuronal networks
irreversibly. In this work, we study the effects of visual stimulus deficits on
the training of artificial neural networks (ANNs). Introducing
well-characterized visual defici... | computer science |
12,328 | Long Short-Term Memory (LSTM) networks with jet constituents for boosted
top tagging at the LHC | cs.LG | Multivariate techniques based on engineered features have found wide adoption
in the identification of jets resulting from hadronic top decays at the Large
Hadron Collider (LHC). Recent Deep Learning developments in this area include
the treatment of the calorimeter activation as an image or supplying a list of
jet con... | computer science |
12,329 | Selling to a No-Regret Buyer | cs.GT | We consider the problem of a single seller repeatedly selling a single item
to a single buyer (specifically, the buyer has a value drawn fresh from known
distribution $D$ in every round). Prior work assumes that the buyer is fully
rational and will perfectly reason about how their bids today affect the
seller's decisio... | computer science |
12,330 | A Big Data Analysis Framework Using Apache Spark and Deep Learning | cs.DB | With the spreading prevalence of Big Data, many advances have recently been
made in this field. Frameworks such as Apache Hadoop and Apache Spark have
gained a lot of traction over the past decades and have become massively
popular, especially in industries. It is becoming increasingly evident that
effective big data a... | computer science |
12,331 | Context-modulation of hippocampal dynamics and deep convolutional
networks | stat.ML | Complex architectures of biological neural circuits, such as parallel
processing pathways, has been behaviorally implicated in many cognitive
studies. However, the theoretical consequences of circuit complexity on neural
computation have only been explored in limited cases. Here, we introduce a
mechanism by which direc... | computer science |
12,332 | Learning from Between-class Examples for Deep Sound Recognition | cs.LG | Deep learning methods have achieved high performance in sound recognition
tasks. Deciding how to feed the training data is important for further
performance improvement. We propose a novel learning method for deep sound
recognition: Between-Class learning (BC learning). Our strategy is to learn a
discriminative feature... | computer science |
12,333 | Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient
Descent | cs.LG | Nesterov's accelerated gradient descent (AGD), an instance of the general
family of "momentum methods", provably achieves faster convergence rate than
gradient descent (GD) in the convex setting. However, whether these methods are
superior to GD in the nonconvex setting remains open. This paper studies a
simple variant... | computer science |
12,334 | On reducing the communication cost of the diffusion LMS algorithm | stat.ML | The rise of digital and mobile communications has recently made the world
more connected and networked, resulting in an unprecedented volume of data
flowing between sources, data centers, or processes. While these data may be
processed in a centralized manner, it is often more suitable to consider
distributed strategie... | computer science |
12,335 | Outlier-robust moment-estimation via sum-of-squares | cs.DS | We develop efficient algorithms for estimating low-degree moments of unknown
distributions in the presence of adversarial outliers. The guarantees of our
algorithms improve in many cases significantly over the best previous ones,
obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al.
We also sho... | computer science |
12,336 | Towards Personalized Modeling of the Female Hormonal Cycle: Experiments
with Mechanistic Models and Gaussian Processes | stat.ML | In this paper, we introduce a novel task for machine learning in healthcare,
namely personalized modeling of the female hormonal cycle. The motivation for
this work is to model the hormonal cycle and predict its phases in time, both
for healthy individuals and for those with disorders of the reproductive
system. Becaus... | computer science |
12,337 | Personalized Gaussian Processes for Future Prediction of Alzheimer's
Disease Progression | cs.LG | In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE,
ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by
learning a population-level model using multi-modal data from previously seen
patients us... | computer science |
12,338 | The reparameterization trick for acquisition functions | stat.ML | Bayesian optimization is a sample-efficient approach to solving global
optimization problems. Along with a surrogate model, this approach relies on
theoretically motivated value heuristics (acquisition functions) to guide the
search process. Maximizing acquisition functions yields the best performance;
unfortunately, t... | computer science |
12,339 | A Neural Stochastic Volatility Model | cs.LG | In this paper, we show that the recent integration of statistical models with
deep recurrent neural networks provides a new way of formulating volatility
(the degree of variation of time series) models that have been widely used in
time series analysis and prediction in finance. The model comprises a pair of
complement... | computer science |
12,340 | Anesthesiologist-level forecasting of hypoxemia with only SpO2 data
using deep learning | cs.LG | We use a deep learning model trained only on a patient's blood oxygenation
data (measurable with an inexpensive fingertip sensor) to predict impending
hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
with access to all the data recorded in a modern operating room. We also
provide a simple way... | computer science |
12,341 | Towards Robust Neural Networks via Random Self-ensemble | cs.LG | Recent studies have revealed the vulnerability of deep neural networks - A
small adversarial perturbation that is imperceptible to human can easily make a
well-trained deep neural network mis-classify. This makes it unsafe to apply
neural networks in security-critical applications. In this paper, we propose a
new defen... | computer science |
12,342 | GANGs: Generative Adversarial Network Games | stat.ML | Generative Adversarial Networks (GAN) have become one of the most successful
frameworks for unsupervised generative modeling. As GANs are difficult to train
much research has focused on this. However, very little of this research has
directly exploited game-theoretic techniques. We introduce Generative
Adversarial Netw... | computer science |
12,343 | Improving Network Robustness against Adversarial Attacks with Compact
Convolution | cs.LG | Though Convolutional Neural Networks (CNNs) have surpassed human-level
performance on tasks such as object classification and face verification, they
can easily be fooled by adversarial attacks. These attacks add a small
perturbation to the input image that causes the network to mis-classify the
sample. In this paper, ... | computer science |
12,344 | SHINE: Signed Heterogeneous Information Network Embedding for Sentiment
Link Prediction | stat.ML | In online social networks people often express attitudes towards others,
which forms massive sentiment links among users. Predicting the sign of
sentiment links is a fundamental task in many areas such as personal
advertising and public opinion analysis. Previous works mainly focus on textual
sentiment classification, ... | computer science |
12,345 | Tensor Train Neighborhood Preserving Embedding | cs.LG | In this paper, we propose a Tensor Train Neighborhood Preserving Embedding
(TTNPE) to embed multi-dimensional tensor data into low dimensional tensor
subspace. Novel approaches to solve the optimization problem in TTNPE are
proposed. For this embedding, we evaluate novel trade-off gain among
classification, computation... | computer science |
12,346 | Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection | cs.CR | In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected t... | computer science |
12,347 | Statistical Inference for Incomplete Ranking Data: The Case of
Rank-Dependent Coarsening | stat.ML | We consider the problem of statistical inference for ranking data,
specifically rank aggregation, under the assumption that samples are incomplete
in the sense of not comprising all choice alternatives. In contrast to most
existing methods, we explicitly model the process of turning a full ranking
into an incomplete on... | computer science |
12,348 | Linearly-Recurrent Autoencoder Networks for Learning Dynamics | math.DS | This paper describes a method for learning low-dimensional approximations of
nonlinear dynamical systems, based on neural-network approximations of the
underlying Koopman operator. Extended Dynamic Mode Decomposition (EDMD)
provides a useful data-driven approximation of the Koopman operator for
analyzing dynamical syst... | computer science |
12,349 | Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert
Spaces | math.DS | Transfer operators such as the Perron-Frobenius or Koopman operator play an
important role in the global analysis of complex dynamical systems. The
eigenfunctions of these operators can be used to detect metastable sets, to
project the dynamics onto the dominant slow processes, or to separate
superimposed signals. We e... | computer science |
12,350 | Attention based convolutional neural network for predicting RNA-protein
binding sites | cs.LG | RNA-binding proteins (RBPs) play crucial roles in many biological processes,
e.g. gene regulation. Computational identification of RBP binding sites on RNAs
are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence
motifs. Thus, fast locating those motifs on RNA sequences is crucial and
time-efficie... | computer science |
12,351 | How consistent is my model with the data? Information-Theoretic Model
Check | stat.ML | The choice of model class is fundamental in statistical learning and system
identification, no matter whether the class is derived from physical principles
or is a generic black-box. We develop a method to evaluate the specified model
class by assessing its capability of reproducing data that is similar to the
observed... | computer science |
12,352 | Blind Multi-class Ensemble Learning with Unequally Reliable Classifiers | stat.ML | The rising interest in pattern recognition and data analytics has spurred the
development of innovative machine learning algorithms and tools. However, as
each algorithm has its strengths and limitations, one is motivated to
judiciously fuse multiple algorithms in order to find the "best" performing
one, for a given da... | computer science |
12,353 | PacGAN: The power of two samples in generative adversarial networks | cs.LG | Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little divers... | computer science |
12,354 | Stochastic Particle Gradient Descent for Infinite Ensembles | stat.ML | The superior performance of ensemble methods with infinite models are well
known. Most of these methods are based on optimization problems in
infinite-dimensional spaces with some regularization, for instance, boosting
methods and convex neural networks use $L^1$-regularization with the
non-negative constraint. However... | computer science |
12,355 | Automatic Music Highlight Extraction using Convolutional Recurrent
Attention Networks | cs.LG | Music highlights are valuable contents for music services. Most methods
focused on low-level signal features. We propose a method for extracting
highlights using high-level features from convolutional recurrent attention
networks (CRAN). CRAN utilizes convolution and recurrent layers for sequential
learning with an att... | computer science |
12,356 | Avoiding Synchronization in First-Order Methods for Sparse Convex
Optimization | cs.DC | Parallel computing has played an important role in speeding up convex
optimization methods for big data analytics and large-scale machine learning
(ML). However, the scalability of these optimization methods is inhibited by
the cost of communicating and synchronizing processors in a parallel setting.
Iterative ML metho... | computer science |
12,357 | Wasserstein Distributional Robustness and Regularization in Statistical
Learning | cs.LG | A central question in statistical learning is to design algorithms that not
only perform well on training data, but also generalize to new and unseen data.
In this paper, we tackle this question by formulating a distributionally robust
stochastic optimization (DRSO) problem, which seeks a solution that minimizes
the wo... | computer science |
12,358 | Generating and designing DNA with deep generative models | cs.LG | We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure wh... | computer science |
12,359 | Misspecified Nonconvex Statistical Optimization for Phase Retrieval | stat.ML | Existing nonconvex statistical optimization theory and methods crucially rely
on the correct specification of the underlying "true" statistical models. To
address this issue, we take a first step towards taming model misspecification
by studying the high-dimensional sparse phase retrieval problem with
misspecified link... | computer science |
12,360 | Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems
over Large Graphs | math.OC | A regularized optimization problem over a large unstructured graph is
studied, where the regularization term is tied to the graph geometry. Typical
regularization examples include the total variation and the Laplacian
regularizations over the graph. When applying the proximal gradient algorithm
to solve this problem, t... | computer science |
12,361 | Development and evaluation of a deep learning model for protein-ligand
binding affinity prediction | stat.ML | Structure based ligand discovery is one of the most successful approaches for
augmenting the drug discovery process. Currently, there is a notable shift
towards machine learning (ML) methodologies to aid such procedures. Deep
learning has recently gained considerable attention as it allows the model to
"learn" to extra... | computer science |
12,362 | Fusing Multifaceted Transaction Data for User Modeling and Demographic
Prediction | cs.SI | Inferring user characteristics such as demographic attributes is of the
utmost importance in many user-centric applications. Demographic data is an
enabler of personalization, identity security, and other applications. Despite
that, this data is sensitive and often hard to obtain. Previous work has shown
that purchase ... | computer science |
12,363 | A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with
the Trace Norm | cs.DC | We consider the problem of learning a high-dimensional but low-rank matrix
from a large-scale dataset distributed over several machines, where
low-rankness is enforced by a convex trace norm constraint. We propose
DFW-Trace, a distributed Frank-Wolfe algorithm which leverages the low-rank
structure of its updates to ac... | computer science |
12,364 | Differentially Private Federated Learning: A Client Level Perspective | cs.CR | Federated learning is a recent advance in privacy protection. In this
context, a trusted curator aggregates parameters optimized in decentralized
fashion by multiple clients. The resulting model is then distributed back to
all clients, ultimately converging to a joint representative model without
explicitly having to s... | computer science |
12,365 | Multi-dimensional Graph Fourier Transform | stat.ME | Many signals on Cartesian product graphs appear in the real world, such as
digital images, sensor observation time series, and movie ratings on Netflix.
These signals are "multi-dimensional" and have directional characteristics
along each factor graph. However, the existing graph Fourier transform does not
distinguish ... | computer science |
12,366 | Non-convex Optimization for Machine Learning | stat.ML | A vast majority of machine learning algorithms train their models and perform
inference by solving optimization problems. In order to capture the learning
and prediction problems accurately, structural constraints such as sparsity or
low rank are frequently imposed or else the objective itself is designed to be
a non-c... | computer science |
12,367 | Multiview Deep Learning for Predicting Twitter Users' Location | cs.LG | The problem of predicting the location of users on large social networks like
Twitter has emerged from real-life applications such as social unrest detection
and online marketing. Twitter user geolocation is a difficult and active
research topic with a vast literature. Most of the proposed methods follow
either a conte... | computer science |
12,368 | Profit Driven Decision Trees for Churn Prediction | stat.ML | Customer retention campaigns increasingly rely on predictive models to detect
potential churners in a vast customer base. From the perspective of machine
learning, the task of predicting customer churn can be presented as a binary
classification problem. Using data on historic behavior, classification
algorithms are bu... | computer science |
12,369 | How Well Can Generative Adversarial Networks Learn Densities: A
Nonparametric View | stat.ML | We study in this paper the rate of convergence for learning densities under
the Generative Adversarial Networks (GAN) framework, borrowing insights from
nonparametric statistics. We introduce an improved GAN estimator that achieves
a faster rate, through simultaneously leveraging the level of smoothness in the
target d... | computer science |
12,370 | True Asymptotic Natural Gradient Optimization | stat.ML | We introduce a simple algorithm, True Asymptotic Natural Gradient
Optimization (TANGO), that converges to a true natural gradient descent in the
limit of small learning rates, without explicit Fisher matrix estimation.
For quadratic models the algorithm is also an instance of averaged stochastic
gradient, where the p... | computer science |
12,371 | Variational Autoencoders for Learning Latent Representations of Speech
Emotion | cs.SD | Latent representation of data in unsupervised fashion is a very interesting
process. It provides more relevant features that can enhance the performance of
a classifier. For speech emotion recognition tasks generating effective
features is very crucial. Recently, deep generative models such as Variational
Autoencoders ... | computer science |
12,372 | Query-limited Black-box Attacks to Classifiers | cs.CR | We study black-box attacks on machine learning classifiers where each query
to the model incurs some cost or risk of detection to the adversary. We focus
explicitly on minimizing the number of queries as a major objective.
Specifically, we consider the problem of attacking machine learning classifiers
subject to a budg... | computer science |
12,373 | Bayesian Nonparametric Causal Inference: Information Rates and Learning
Algorithms | stat.ME | We investigate the problem of estimating the causal effect of a treatment on
individual subjects from observational data, this is a central problem in
various application domains, including healthcare, social sciences, and online
advertising. Within the Neyman Rubin potential outcomes model, we use the
Kullback Leibler... | computer science |
12,374 | Stochastic Multi-armed Bandits in Constant Space | cs.DS | We consider the stochastic bandit problem in the sublinear space setting,
where one cannot record the win-loss record for all $K$ arms. We give an
algorithm using $O(1)$ words of space with regret \[
\sum_{i=1}^{K}\frac{1}{\Delta_i}\log \frac{\Delta_i}{\Delta}\log T \] where
$\Delta_i$ is the gap between the best arm... | computer science |
12,375 | Collaborative Autoencoder for Recommender Systems | cs.LG | In recent years, deep neural networks have yielded state-of-the-art
performance on several tasks. Although some recent works have focused on
combining deep learning with recommendation, we highlight three issues of
existing works. First, most works perform deep content feature learning and
resort to matrix factorizatio... | computer science |
12,376 | SAGA: A Submodular Greedy Algorithm For Group Recommendation | cs.IR | In this paper, we propose a unified framework and an algorithm for the
problem of group recommendation where a fixed number of items or alternatives
can be recommended to a group of users. The problem of group recommendation
arises naturally in many real world contexts, and is closely related to the
budgeted social cho... | computer science |
12,377 | Algorithmic Regularization in Over-parameterized Matrix Sensing and
Neural Networks with Quadratic Activations | cs.LG | We show that the gradient descent algorithm provides an implicit
regularization effect in the learning of over-parameterized matrix
factorization models and one-hidden-layer neural networks with quadratic
activations. Concretely, we show that given $\tilde{O}(dr^{2})$ random linear
measurements of a rank $r$ positive s... | computer science |
12,378 | Sketching for Kronecker Product Regression and P-splines | cs.DS | TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later
used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was
shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace
embedding, and therefore can be used for canonical correlation analysis, low
rank approximation... | computer science |
12,379 | Deep learning for universal linear embeddings of nonlinear dynamics | math.DS | Identifying coordinate transformations that make strongly nonlinear dynamics
approximately linear is a central challenge in modern dynamical systems. These
transformations have the potential to enable prediction, estimation, and
control of nonlinear systems using standard linear theory. The Koopman operator
has emerged... | computer science |
12,380 | Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning
Architectures | cs.LG | Objective: A clinical decision support tool that automatically interprets
EEGs can reduce time to diagnosis and enhance real-time applications such as
ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
specificity below 5% was the minimum requirement for clinical acceptance. We
propose a highper... | computer science |
12,381 | Deep Architectures for Automated Seizure Detection in Scalp EEGs | cs.LG | Automated seizure detection using clinical electroencephalograms is a
challenging machine learning problem because the multichannel signal often has
an extremely low signal to noise ratio. Events of interest such as seizures are
easily confused with signal artifacts (e.g, eye movements) or benign variants
(e.g., slowin... | computer science |
12,382 | Machine Learning for Partial Identification: Example of Bracketed Data | stat.ML | Partially identified models occur commonly in economic applications. A common
problem in this literature is a regression problem with bracketed
(interval-censored) outcome variable Y, which creates a set-identified
parameter of interest. The recent studies have only considered
finite-dimensional linear regression in su... | computer science |
12,383 | Objective evaluation metrics for automatic classification of EEG events | cs.LG | The evaluation of machine learning algorithms in biomedical fields for
applications involving sequential data lacks standardization. Common
quantitative scalar evaluation metrics such as sensitivity and specificity can
often be misleading depending on the requirements of the application.
Evaluation metrics must ultimat... | computer science |
12,384 | CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer
Electromagnetic Calorimeters with Generative Adversarial Networks | cs.LG | The precise modeling of subatomic particle interactions and propagation
through matter is paramount for the advancement of nuclear and particle physics
searches and precision measurements. The most computationally expensive step in
the simulation pipeline of a typical experiment at the Large Hadron Collider
(LHC) is th... | computer science |
12,385 | A Deep Belief Network Based Machine Learning System for Risky Host
Detection | cs.CR | To assure cyber security of an enterprise, typically SIEM (Security
Information and Event Management) system is in place to normalize security
event from different preventive technologies and flag alerts. Analysts in the
security operation center (SOC) investigate the alerts to decide if it is truly
malicious or not. H... | computer science |
12,386 | Learning Relevant Features of Data with Multi-scale Tensor Networks | stat.ML | Inspired by coarse-graining approaches used in physics, we show how similar
algorithms can be adapted for data. The resulting algorithms are based on
layered tree tensor networks and scale linearly with both the dimension of the
input and the training set size. Computing most of the layers with an
unsupervised algorith... | computer science |
12,387 | MVG Mechanism: Differential Privacy under Matrix-Valued Query | cs.CR | Differential privacy mechanism design has traditionally been tailored for a
scalar-valued query function. Although many mechanisms such as the Laplace and
Gaussian mechanisms can be extended to a matrix-valued query function by adding
i.i.d. noise to each element of the matrix, this method is often suboptimal as
it for... | computer science |
12,388 | Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using
Deep Learning Neural Network | cs.LG | Deep Learning can significantly benefit cancer proteomics and genomics. In
this study, we attempt to determine a set of critical proteins that are
associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia
patients. A Deep Learning network consisting of autoencoders forming a
hierarchical model fro... | computer science |
12,389 | Negative Binomial Matrix Factorization for Recommender Systems | cs.LG | We introduce negative binomial matrix factorization (NBMF), a matrix
factorization technique specially designed for analyzing over-dispersed count
data. It can be viewed as an extension of Poisson matrix factorization (PF)
perturbed by a multiplicative term which models exposure. This term brings a
degree of freedom fo... | computer science |
12,390 | Learning Tree-based Deep Model for Recommender Systems | stat.ML | Model-based methods for recommender systems have been studied to provide more
precise results. In systems with large corpus, the amount of calculation for
learnt model to predict all user-item pairs' preferences is tremendous, which
makes the model difficult to be directly employed in recommendation candidate
generatio... | computer science |
12,391 | An Analysis of Two Common Reference Points for EEGs | eess.SP | Clinical electroencephalographic (EEG) data varies significantly depending on
a number of operational conditions (e.g., the type and placement of electrodes,
the type of electrical grounding used). This investigation explores the
statistical differences present in two different referential montages: Linked
Ear (LE) and... | computer science |
12,392 | Semi-automated Annotation of Signal Events in Clinical EEG Data | eess.SP | To be effective, state of the art machine learning technology needs large
amounts of annotated data. There are numerous compelling applications in
healthcare that can benefit from high performance automated decision support
systems provided by deep learning technology, but they lack the comprehensive
data resources req... | computer science |
12,393 | Sequential Preference-Based Optimization | cs.LG | Many real-world engineering problems rely on human preferences to guide their
design and optimization. We present PrefOpt, an open source package to simplify
sequential optimization tasks that incorporate human preference feedback. Our
approach extends an existing latent variable model for binary preferences to
allow f... | computer science |
12,394 | Less is More: Culling the Training Set to Improve Robustness of Deep
Neural Networks | cs.CR | Deep neural networks are vulnerable to adversarial examples. Prior defenses
attempted to make deep networks more robust by either improving the network
architecture or adding adversarial examples into the training set, with their
respective limitations. We propose a new direction. Motivated by recent
research that show... | computer science |
12,395 | Adversarial Deep Learning for Robust Detection of Binary Encoded Malware | cs.CR | Malware is constantly adapting in order to avoid detection. Model based
malware detectors, such as SVM and neural networks, are vulnerable to so-called
adversarial examples which are modest changes to detectable malware that allows
the resulting malware to evade detection. Continuous-valued methods that are
robust to a... | computer science |
12,396 | How To Make the Gradients Small Stochastically | cs.LG | In convex stochastic optimization, convergence rates in terms of minimizing
the objective have been well-established. However, in terms of making the
gradients small, the best known convergence rate was $O(\varepsilon^{-8/3})$
and it was left open how to improve it.
In this paper, we improve this rate to $\tilde{O}(\... | computer science |
12,397 | Selection Problems in the Presence of Implicit Bias | cs.CY | Over the past two decades, the notion of implicit bias has come to serve as
an important component in our understanding of discrimination in activities
such as hiring, promotion, and school admissions. Research on implicit bias
posits that when people evaluate others -- for example, in a hiring context --
their unconsc... | computer science |
12,398 | Improved asynchronous parallel optimization analysis for stochastic
incremental methods | math.OC | As datasets continue to increase in size and multi-core computer
architectures are developed, asynchronous parallel optimization algorithms
become more and more essential to the field of Machine Learning. Unfortunately,
conducting the theoretical analysis asynchronous methods is difficult, notably
due to the introducti... | computer science |
12,399 | Asynchronous Stochastic Variational Inference | stat.ML | Stochastic variational inference (SVI) employs stochastic optimization to
scale up Bayesian computation to massive data. Since SVI is at its core a
stochastic gradient-based algorithm, horizontal parallelism can be harnessed to
allow larger scale inference. We propose a lock-free parallel implementation
for SVI which a... | computer science |
12,400 | Generalization Error Bounds for Noisy, Iterative Algorithms | cs.LG | In statistical learning theory, generalization error is used to quantify the
degree to which a supervised machine learning algorithm may overfit to training
data. Recent work [Xu and Raginsky (2017)] has established a bound on the
generalization error of empirical risk minimization based on the mutual
information $I(S;... | computer science |
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