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11,901 | Randomized Kernel Methods for Least-Squares Support Vector Machines | cs.LG | The least-squares support vector machine is a frequently used kernel method
for non-linear regression and classification tasks. Here we discuss several
approximation algorithms for the least-squares support vector machine
classifier. The proposed methods are based on randomized block kernel matrices,
and we show that t... | computer science |
11,902 | Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial
Domains | stat.ML | While modern day web applications aim to create impact at the civilization
level, they have become vulnerable to adversarial activity, where the next
cyber-attack can take any shape and can originate from anywhere. The increasing
scale and sophistication of attacks, has prompted the need for a data driven
solution, wit... | computer science |
11,903 | Biologically inspired protection of deep networks from adversarial
attacks | stat.ML | Inspired by biophysical principles underlying nonlinear dendritic computation
in neural circuits, we develop a scheme to train deep neural networks to make
them robust to adversarial attacks. Our scheme generates highly nonlinear,
saturated neural networks that achieve state of the art performance on gradient
based adv... | computer science |
11,904 | Solving Non-parametric Inverse Problem in Continuous Markov Random Field
using Loopy Belief Propagation | stat.ML | In this paper, we address the inverse problem, or the statistical machine
learning problem, in Markov random fields with a non-parametric pair-wise
energy function with continuous variables. The inverse problem is formulated by
maximum likelihood estimation. The exact treatment of maximum likelihood
estimation is intra... | computer science |
11,905 | Particle Filtering for PLCA model with Application to Music
Transcription | stat.ML | Automatic Music Transcription (AMT) consists in automatically estimating the
notes in an audio recording, through three attributes: onset time, duration and
pitch. Probabilistic Latent Component Analysis (PLCA) has become very popular
for this task. PLCA is a spectrogram factorization method, able to model a
magnitude ... | computer science |
11,906 | Efficient Private ERM for Smooth Objectives | cs.LG | In this paper, we consider efficient differentially private empirical risk
minimization from the viewpoint of optimization algorithms. For strongly convex
and smooth objectives, we prove that gradient descent with output perturbation
not only achieves nearly optimal utility, but also significantly improves the
running ... | computer science |
11,907 | Atomic Convolutional Networks for Predicting Protein-Ligand Binding
Affinity | cs.LG | Empirical scoring functions based on either molecular force fields or
cheminformatics descriptors are widely used, in conjunction with molecular
docking, during the early stages of drug discovery to predict potency and
binding affinity of a drug-like molecule to a given target. These models
require expert-level knowled... | computer science |
11,908 | Near Perfect Protein Multi-Label Classification with Deep Neural
Networks | cs.LG | Artificial neural networks (ANNs) have gained a well-deserved popularity
among machine learning tools upon their recent successful applications in
image- and sound processing and classification problems. ANNs have also been
applied for predicting the family or function of a protein, knowing its residue
sequence. Here w... | computer science |
11,909 | Fundamental Conditions for Low-CP-Rank Tensor Completion | cs.LG | We consider the problem of low canonical polyadic (CP) rank tensor
completion. A completion is a tensor whose entries agree with the observed
entries and its rank matches the given CP rank. We analyze the manifold
structure corresponding to the tensors with the given rank and define a set of
polynomials based on the sa... | computer science |
11,910 | Bi-class classification of humpback whale sound units against complex
background noise with Deep Convolution Neural Network | stat.ML | Automatically detecting sound units of humpback whales in complex
time-varying background noises is a current challenge for scientists. In this
paper, we explore the applicability of Convolution Neural Network (CNN) method
for this task. In the evaluation stage, we present 6 bi-class classification
experimentations of ... | computer science |
11,911 | Comparison of multi-task convolutional neural network (MT-CNN) and a few
other methods for toxicity prediction | cs.LG | Toxicity analysis and prediction are of paramount importance to human health
and environmental protection. Existing computational methods are built from a
wide variety of descriptors and regressors, which makes their performance
analysis difficult. For example, deep neural network (DNN), a successful
approach in many o... | computer science |
11,912 | Provable Inductive Robust PCA via Iterative Hard Thresholding | cs.LG | The robust PCA problem, wherein, given an input data matrix that is the
superposition of a low-rank matrix and a sparse matrix, we aim to separate out
the low-rank and sparse components, is a well-studied problem in machine
learning. One natural question that arises is that, as in the inductive
setting, if features are... | computer science |
11,913 | No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified
Geometric Analysis | cs.LG | In this paper we develop a new framework that captures the common landscape
underlying the common non-convex low-rank matrix problems including matrix
sensing, matrix completion and robust PCA. In particular, we show for all above
problems (including asymmetric cases): 1) all local minima are also globally
optimal; 2) ... | computer science |
11,914 | Polynomial Time and Sample Complexity for Non-Gaussian Component
Analysis: Spectral Methods | cs.LG | The problem of Non-Gaussian Component Analysis (NGCA) is about finding a
maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points
projected onto $E$ follow a non-gaussian distribution. Although this is an
appropriate model for some real world data analysis problems, there has been
little progress on t... | computer science |
11,915 | Homotopy Parametric Simplex Method for Sparse Learning | cs.LG | High dimensional sparse learning has imposed a great computational challenge
to large scale data analysis. In this paper, we are interested in a broad class
of sparse learning approaches formulated as linear programs parametrized by a
{\em regularization factor}, and solve them by the parametric simplex method
(PSM). O... | computer science |
11,916 | On the Unreported-Profile-is-Negative Assumption for Predictive
Cheminformatics | cs.LG | In cheminformatics, compound-target binding profiles has been a main source
of data for research. For data repositories that only provide positive
profiles, a popular assumption is that unreported profiles are all negative. In
this paper, we caution audience not to take this assumption for granted, and
present empirica... | computer science |
11,917 | Revisiting the problem of audio-based hit song prediction using
convolutional neural networks | cs.SD | Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as ... | computer science |
11,918 | On Generalization and Regularization in Deep Learning | stat.ML | Why do large neural network generalize so well on complex tasks such as image
classification or speech recognition? What exactly is the role regularization
for them? These are arguably among the most important open questions in machine
learning today. In a recent and thought provoking paper [C. Zhang et al.]
several au... | computer science |
11,919 | Comparison Based Nearest Neighbor Search | stat.ML | We consider machine learning in a comparison-based setting where we are given
a set of points in a metric space, but we have no access to the actual
distances between the points. Instead, we can only ask an oracle whether the
distance between two points $i$ and $j$ is smaller than the distance between
the points $i$ an... | computer science |
11,920 | Comment on "Biologically inspired protection of deep networks from
adversarial attacks" | stat.ML | A recent paper suggests that Deep Neural Networks can be protected from
gradient-based adversarial perturbations by driving the network activations
into a highly saturated regime. Here we analyse such saturated networks and
show that the attacks fail due to numerical limitations in the gradient
computations. A simple s... | computer science |
11,921 | Nonnegative/binary matrix factorization with a D-Wave quantum annealer | cs.LG | D-Wave quantum annealers represent a novel computational architecture and
have attracted significant interest, but have been used for few real-world
computations. Machine learning has been identified as an area where quantum
annealing may be useful. Here, we show that the D-Wave 2X can be effectively
used as part of an... | computer science |
11,922 | Accelerated Stochastic Quasi-Newton Optimization on Riemann Manifolds | math.OC | We propose an L-BFGS optimization algorithm on Riemannian manifolds using
minibatched stochastic variance reduction techniques for fast convergence with
constant step sizes, without resorting to linesearch methods designed to
satisfy Wolfe conditions. We provide a new convergence proof for strongly
convex functions wit... | computer science |
11,923 | Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks | cs.CR | Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the... | computer science |
11,924 | Jet Constituents for Deep Neural Network Based Top Quark Tagging | cs.LG | Recent literature on deep neural networks for tagging of highly energetic
jets resulting from top quark decays has focused on image based techniques or
multivariate approaches using high-level jet substructure variables. Here, a
sequential approach to this task is taken by using an ordered sequence of jet
constituents ... | computer science |
11,925 | Deep Reinforcement Learning framework for Autonomous Driving | stat.ML | Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes. Despite its perceived utility, it has not yet been
successfully applied in automotive applications. Motivated by the successful
demonstrations of... | computer science |
11,926 | On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel
Methods and Neural Networks | cs.CC | Empirical risk minimization (ERM) is ubiquitous in machine learning and
underlies most supervised learning methods. While there has been a large body
of work on algorithms for various ERM problems, the exact computational
complexity of ERM is still not understood. We address this issue for multiple
popular ERM problems... | computer science |
11,927 | A probabilistic data-driven model for planar pushing | cs.RO | This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture... | computer science |
11,928 | struc2vec: Learning Node Representations from Structural Identity | cs.SI | Structural identity is a concept of symmetry in which network nodes are
identified according to the network structure and their relationship to other
nodes. Structural identity has been studied in theory and practice over the
past decades, but only recently has it been addressed with representational
learning technique... | computer science |
11,929 | The Space of Transferable Adversarial Examples | stat.ML | Adversarial examples are maliciously perturbed inputs designed to mislead
machine learning (ML) models at test-time. They often transfer: the same
adversarial example fools more than one model.
In this work, we propose novel methods for estimating the previously unknown
dimensionality of the space of adversarial inpu... | computer science |
11,930 | Sampling-based speech parameter generation using moment-matching
networks | cs.SD | This paper presents sampling-based speech parameter generation using
moment-matching networks for Deep Neural Network (DNN)-based speech synthesis.
Although people never produce exactly the same speech even if we try to express
the same linguistic and para-linguistic information, typical statistical speech
synthesis pr... | computer science |
11,931 | Investigation on the use of Hidden-Markov Models in automatic
transcription of music | stat.ML | Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
and have been widely used in two main tasks of Automatic Music Transcription
(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
estimation, and sequential post-processing, i.e. correcting note segmentation
using tr... | computer science |
11,932 | A Proof of Orthogonal Double Machine Learning with $Z$-Estimators | stat.ML | We consider two stage estimation with a non-parametric first stage and a
generalized method of moments second stage, in a simpler setting than
(Chernozhukov et al. 2016). We give an alternative proof of the theorem given
in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample
splitting and $n^{1/4}$-... | computer science |
11,933 | ZigZag: A new approach to adaptive online learning | cs.LG | We develop a novel family of algorithms for the online learning setting with
regret against any data sequence bounded by the empirical Rademacher complexity
of that sequence. To develop a general theory of when this type of adaptive
regret bound is achievable we establish a connection to the theory of
decoupling inequa... | computer science |
11,934 | 3D Deep Learning for Biological Function Prediction from Physical Fields | cs.LG | Predicting the biological function of molecules, be it proteins or drug-like
compounds, from their atomic structure is an important and long-standing
problem. Function is dictated by structure, since it is by spatial interactions
that molecules interact with each other, both in terms of steric
complementarity, as well ... | computer science |
11,935 | Adaptive Neighboring Selection Algorithm Based on Curvature Prediction
in Manifold Learning | stat.ME | Recently manifold learning algorithm for dimensionality reduction attracts
more and more interests, and various linear and nonlinear, global and local
algorithms are proposed. The key step of manifold learning algorithm is the
neighboring region selection. However, so far for the references we know, few
of which propos... | computer science |
11,936 | Cross-media Similarity Metric Learning with Unified Deep Networks | cs.MM | As a highlighting research topic in the multimedia area, cross-media
retrieval aims to capture the complex correlations among multiple media types.
Learning better shared representation and distance metric for multimedia data
is important to boost the cross-media retrieval. Motivated by the strong
ability of deep neura... | computer science |
11,937 | Deep Learning Based Regression and Multi-class Models for Acute Oral
Toxicity Prediction with Automatic Chemical Feature Extraction | stat.ML | For quantitative structure-property relationship (QSPR) studies in
chemoinformatics, it is important to get interpretable relationship between
chemical properties and chemical features. However, the predictive power and
interpretability of QSPR models are usually two different objectives that are
difficult to achieve s... | computer science |
11,938 | Semi-supervised classification for dynamic Android malware detection | cs.CR | A growing number of threats to Android phones creates challenges for malware
detection. Manually labeling the samples into benign or different malicious
families requires tremendous human efforts, while it is comparably easy and
cheap to obtain a large amount of unlabeled APKs from various sources.
Moreover, the fast-p... | computer science |
11,939 | Retrospective Higher-Order Markov Processes for User Trails | cs.SI | Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such se... | computer science |
11,940 | Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks | cs.LG | Matrix completion models are among the most common formulations of
recommender systems. Recent works have showed a boost of performance of these
techniques when introducing the pairwise relationships between users/items in
the form of graphs, and imposing smoothness priors on these graphs. However,
such techniques do n... | computer science |
11,941 | Linear Convergence of Accelerated Stochastic Gradient Descent for
Nonconvex Nonsmooth Optimization | math.OC | In this paper, we study the stochastic gradient descent (SGD) method for the
nonconvex nonsmooth optimization, and propose an accelerated SGD method by
combining the variance reduction technique with Nesterov's extrapolation
technique. Moreover, based on the local error bound condition, we establish the
linear converge... | computer science |
11,942 | Spectral Ergodicity in Deep Learning Architectures via Surrogate Random
Matrices | stat.ML | In this work a novel method to quantify spectral ergodicity for random
matrices is presented. The new methodology combines approaches rooted in the
metrics of Thirumalai-Mountain (TM) and Kullbach-Leibler (KL) divergence. The
method is applied to a general study of deep and recurrent neural networks via
the analysis of... | computer science |
11,943 | Optimal client recommendation for market makers in illiquid financial
products | cs.LG | The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather ... | computer science |
11,944 | Complex spectrogram enhancement by convolutional neural network with
multi-metrics learning | stat.ML | This paper aims to address two issues existing in the current speech
enhancement methods: 1) the difficulty of phase estimations; 2) a single
objective function cannot consider multiple metrics simultaneously. To solve
the first problem, we propose a novel convolutional neural network (CNN) model
for complex spectrogra... | computer science |
11,945 | Matrix Completion and Related Problems via Strong Duality | cs.DS | This work studies the strong duality of non-convex matrix factorization
problems: we show that under certain dual conditions, these problems and its
dual have the same optimum. This has been well understood for convex
optimization, but little was known for non-convex problems. We propose a novel
analytical framework an... | computer science |
11,946 | A Network Perspective on Stratification of Multi-Label Data | stat.ML | In the recent years, we have witnessed the development of multi-label
classification methods which utilize the structure of the label space in a
divide and conquer approach to improve classification performance and allow
large data sets to be classified efficiently. Yet most of the available data
sets have been provide... | computer science |
11,947 | Deep Feature Learning for Graphs | stat.ML | This paper presents a general graph representation learning framework called
DeepGL for learning deep node and edge representations from large (attributed)
graphs. In particular, DeepGL begins by deriving a set of base features (e.g.,
graphlet features) and automatically learns a multi-layered hierarchical graph
repres... | computer science |
11,948 | Learning with Changing Features | cs.LG | In this paper we study the setting where features are added or change
interpretation over time, which has applications in multiple domains such as
retail, manufacturing, finance. In particular, we propose an approach to
provably determine the time instant from which the new/changed features start
becoming relevant with... | computer science |
11,949 | Redundancy in active paths of deep networks: a random active path model | cs.LG | Deep learning has become a powerful and popular tool for a variety of machine
learning tasks. However, it is extremely challenging to understand the
mechanism of deep learning from a theoretical perspective. In this work, we
study robustness of a deep network in its generalization capability against
removal of a certai... | computer science |
11,950 | Nonlinear Information Bottleneck | cs.IT | Information bottleneck [IB] is a technique for extracting information in some
`input' random variable that is relevant for predicting some different 'output'
random variable. IB works by encoding the input in a compressed 'bottleneck
variable' from which the output can then be accurately decoded. IB can be
difficult to... | computer science |
11,951 | Classification and Representation via Separable Subspaces: Performance
Limits and Algorithms | cs.IT | We study the classification performance of Kronecker-structured models in two
asymptotic regimes and developed an algorithm for separable, fast and compact
K-S dictionary learning for better classification and representation of
multidimensional signals by exploiting the structure in the signal. First, we
study the clas... | computer science |
11,952 | Learning of Gaussian Processes in Distributed and Communication Limited
Systems | stat.ML | It is of fundamental importance to find algorithms obtaining optimal
performance for learning of statistical models in distributed and communication
limited systems. Aiming at characterizing the optimal strategies, we consider
learning of Gaussian Processes (GPs) in distributed systems as a pivotal
example. We first ad... | computer science |
11,953 | Frequentist Consistency of Variational Bayes | stat.ML | A key challenge for modern Bayesian statistics is how to perform scalable
inference of posterior distributions. To address this challenge, VB methods
have emerged as a popular alternative to the classical MCMC methods. VB methods
tend to be faster while achieving comparable predictive performance. However,
there are fe... | computer science |
11,954 | Spatial Random Sampling: A Structure-Preserving Data Sketching Tool | cs.LG | Random column sampling is not guaranteed to yield data sketches that preserve
the underlying structures of the data and may not sample sufficiently from
less-populated data clusters. Also, adaptive sampling can often provide
accurate low rank approximations, yet may fall short of producing descriptive
data sketches, es... | computer science |
11,955 | Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines:
A Majorization--Minimization Algorithm Approach | stat.CO | Support vector machines (SVMs) are an important tool in modern data analysis.
Traditionally, support vector machines have been fitted via quadratic
programming, either using purpose-built or off-the-shelf algorithms. We present
an alternative approach to SVM fitting via the majorization--minimization (MM)
paradigm. Alg... | computer science |
11,956 | Extending Defensive Distillation | cs.LG | Machine learning is vulnerable to adversarial examples: inputs carefully
modified to force misclassification. Designing defenses against such inputs
remains largely an open problem. In this work, we revisit defensive
distillation---which is one of the mechanisms proposed to mitigate adversarial
examples---to address it... | computer science |
11,957 | Distributed Statistical Machine Learning in Adversarial Settings:
Byzantine Gradient Descent | cs.DC | We consider the problem of distributed statistical machine learning in
adversarial settings, where some unknown and time-varying subset of working
machines may be compromised and behave arbitrarily to prevent an accurate model
from being learned. This setting captures the potential adversarial attacks
faced by Federate... | computer science |
11,958 | Learning Edge Representations via Low-Rank Asymmetric Projections | cs.LG | We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine lear... | computer science |
11,959 | Sub-sampled Cubic Regularization for Non-convex Optimization | cs.LG | We consider the minimization of non-convex functions that typically arise in
machine learning. Specifically, we focus our attention on a variant of trust
region methods known as cubic regularization. This approach is particularly
attractive because it escapes strict saddle points and it provides stronger
convergence gu... | computer science |
11,960 | An Investigation of Newton-Sketch and Subsampled Newton Methods | math.OC | The concepts of sketching and subsampling have recently received much
attention by the optimization and statistics communities. In this paper, we
study Newton-Sketch and Subsampled Newton (SSN) methods for the finite-sum
optimization problem. We consider practical versions of the two methods in
which the Newton equatio... | computer science |
11,961 | A Unified Framework for Stochastic Matrix Factorization via Variance
Reduction | stat.ML | We propose a unified framework to speed up the existing stochastic matrix
factorization (SMF) algorithms via variance reduction. Our framework is general
and it subsumes several well-known SMF formulations in the literature. We
perform a non-asymptotic convergence analysis of our framework and derive
computational and ... | computer science |
11,962 | Practical Algorithms for Best-K Identification in Multi-Armed Bandits | cs.LG | In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
stochastic bandit arms with unknown reward distributions. Our goal is to
identify the $K$ arms with the largest means with high confidence, by drawing
samples from the arms adaptively. This problem is motivated by various
practical applications and... | computer science |
11,963 | CDS Rate Construction Methods by Machine Learning Techniques | cs.LG | Regulators require financial institutions to estimate counterparty default
risks from liquid CDS quotes for the valuation and risk management of OTC
derivatives. However, the vast majority of counterparties do not have liquid
CDS quotes and need proxy CDS rates. Existing methods cannot account for
counterparty-specific... | computer science |
11,964 | The Landscape of Deep Learning Algorithms | stat.ML | This paper studies the landscape of empirical risk of deep neural networks by
theoretically analyzing its convergence behavior to the population risk as well
as its stationary points and properties. For an $l$-layer linear neural
network, we prove its empirical risk uniformly converges to its population risk
at the rat... | computer science |
11,965 | Linear regression without correspondence | cs.LG | This article considers algorithmic and statistical aspects of linear
regression when the correspondence between the covariates and the responses is
unknown. First, a fully polynomial-time approximation scheme is given for the
natural least squares optimization problem in any constant dimension. Next, in
an average-case... | computer science |
11,966 | Deep adversarial neural decoding | cs.LG | Here, we present a novel approach to solve the problem of reconstructing
perceived stimuli from brain responses by combining probabilistic inference
with deep learning. Our approach first inverts the linear transformation from
latent features to brain responses with maximum a posteriori estimation and
then inverts the ... | computer science |
11,967 | Ensemble Adversarial Training: Attacks and Defenses | stat.ML | Adversarial examples are perturbed inputs designed to fool machine learning
models. Adversarial training injects such examples into training data to
increase robustness. To scale this technique to large datasets, perturbations
are crafted using fast single-step methods that maximize a linear approximation
of the model'... | computer science |
11,968 | Stochastic Recursive Gradient Algorithm for Nonconvex Optimization | stat.ML | In this paper, we study and analyze the mini-batch version of StochAstic
Recursive grAdient algoritHm (SARAH), a method employing the stochastic
recursive gradient, for solving empirical loss minimization for the case of
nonconvex losses. We provide a sublinear convergence rate (to stationary
points) for general noncon... | computer science |
11,969 | $\left( β, \varpi \right)$-stability for cross-validation and the
choice of the number of folds | stat.ML | In this paper, we introduce a new concept of stability for cross-validation,
called the $\left( \beta, \varpi \right)$-stability, and use it as a new
perspective to build the general theory for cross-validation. The $\left(
\beta, \varpi \right)$-stability mathematically connects the generalization
ability and the stab... | computer science |
11,970 | Balanced Policy Evaluation and Learning | stat.ML | We present a new approach to the problems of evaluating and learning
personalized decision policies from observational data of past contexts,
decisions, and outcomes. Only the outcome of the enacted decision is available
and the historical policy is unknown. These problems arise in personalized
medicine using electroni... | computer science |
11,971 | Parallel Streaming Wasserstein Barycenters | cs.LG | Efficiently aggregating data from different sources is a challenging problem,
particularly when samples from each source are distributed differently. These
differences can be inherent to the inference task or present for other reasons:
sensors in a sensor network may be placed far apart, affecting their individual
meas... | computer science |
11,972 | Infrastructure for Usable Machine Learning: The Stanford DAWN Project | cs.LG | Despite incredible recent advances in machine learning, building machine
learning applications remains prohibitively time-consuming and expensive for
all but the best-trained, best-funded engineering organizations. This expense
comes not from a need for new and improved statistical models but instead from
a lack of sys... | computer science |
11,973 | Classification Using Proximity Catch Digraphs (Technical Report) | cs.LG | We employ random geometric digraphs to construct semi-parametric classifiers.
These data-random digraphs are from parametrized random digraph families called
proximity catch digraphs (PCDs). A related geometric digraph family, class
cover catch digraph (CCCD), has been used to solve the class cover problem by
using its... | computer science |
11,974 | Training Deep Networks without Learning Rates Through Coin Betting | cs.LG | Deep learning methods achieve state-of-the-art performance in many
application scenarios. Yet, these methods require a significant amount of
hyperparameters tuning in order to achieve the best results. In particular,
tuning the learning rates in the stochastic optimization process is still one
of the main bottlenecks. ... | computer science |
11,975 | Information-theoretic analysis of generalization capability of learning
algorithms | cs.LG | We derive upper bounds on the generalization error of a learning algorithm in
terms of the mutual information between its input and output. The bounds
provide an information-theoretic understanding of generalization in learning
problems, and give theoretical guidelines for striking the right balance
between data fit an... | computer science |
11,976 | Online Factorization and Partition of Complex Networks From Random Walks | cs.LG | Finding the reduced-dimensional structure is critical to understanding
complex networks. Existing approaches such as spectral clustering are
applicable only when the full network is explicitly observed. In this paper, we
focus on the online factorization and partition of implicit large-scale
networks based on observati... | computer science |
11,977 | Large Scale Empirical Risk Minimization via Truncated Adaptive Newton
Method | math.OC | We consider large scale empirical risk minimization (ERM) problems, where
both the problem dimension and variable size is large. In these cases, most
second order methods are infeasible due to the high cost in both computing the
Hessian over all samples and computing its inverse in high dimensions. In this
paper, we pr... | computer science |
11,978 | Efficient and principled score estimation with Nyström kernel
exponential families | stat.ML | We propose a fast method with statistical guarantees for learning an
exponential family density model where the natural parameter is in a
reproducing kernel Hilbert space, and may be infinite-dimensional. The model is
learned by fitting the derivative of the log density, the score, thus avoiding
the need to compute a n... | computer science |
11,979 | Audio-replay attack detection countermeasures | cs.SD | This paper presents the Speech Technology Center (STC) replay attack
detection systems proposed for Automatic Speaker Verification Spoofing and
Countermeasures Challenge 2017. In this study we focused on comparison of
different spoofing detection approaches. These were GMM based methods, high
level features extraction ... | computer science |
11,980 | Anti-spoofing Methods for Automatic SpeakerVerification System | cs.SD | Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces... | computer science |
11,981 | Proximity Variational Inference | stat.ML | Variational inference is a powerful approach for approximate posterior
inference. However, it is sensitive to initialization and can be subject to
poor local optima. In this paper, we develop proximity variational inference
(PVI). PVI is a new method for optimizing the variational objective that
constrains subsequent i... | computer science |
11,982 | Can Decentralized Algorithms Outperform Centralized Algorithms? A Case
Study for Decentralized Parallel Stochastic Gradient Descent | math.OC | Most distributed machine learning systems nowadays, including TensorFlow and
CNTK, are built in a centralized fashion. One bottleneck of centralized
algorithms lies on high communication cost on the central node. Motivated by
this, we ask, can decentralized algorithms be faster than its centralized
counterpart?
Altho... | computer science |
11,983 | Investigation of Using VAE for i-Vector Speaker Verification | cs.SD | New system for i-vector speaker recognition based on variational autoencoder
(VAE) is investigated. VAE is a promising approach for developing accurate deep
nonlinear generative models of complex data. Experiments show that VAE provides
speaker embedding and can be effectively trained in an unsupervised manner. LLR
est... | computer science |
11,984 | Online Auctions and Multi-scale Online Learning | cs.GT | We consider revenue maximization in online auctions and pricing. A seller
sells an identical item in each period to a new buyer, or a new set of buyers.
For the online posted pricing problem, we show regret bounds that scale with
the best fixed price, rather than the range of the values. We also show regret
bounds that... | computer science |
11,985 | Dimensionality reduction for acoustic vehicle classification with
spectral embedding | stat.ML | We propose a method for recognizing moving vehicles, using data from roadside
audio sensors. This problem has applications ranging widely, from traffic
analysis to surveillance. We extract a frequency signature from the audio
signal using a short-time Fourier transform, and treat each time window as an
individual data ... | computer science |
11,986 | Fast learning rate of deep learning via a kernel perspective | math.ST | We develop a new theoretical framework to analyze the generalization error of
deep learning, and derive a new fast learning rate for two representative
algorithms: empirical risk minimization and Bayesian deep learning. The series
of theoretical analyses of deep learning has revealed its high expressive power
and unive... | computer science |
11,987 | Gradient Descent Can Take Exponential Time to Escape Saddle Points | math.OC | Although gradient descent (GD) almost always escapes saddle points
asymptotically [Lee et al., 2016], this paper shows that even with fairly
natural random initialization schemes and non-pathological functions, GD can be
significantly slowed down by saddle points, taking exponential time to escape.
On the other hand, g... | computer science |
11,988 | Zonotope hit-and-run for efficient sampling from projection DPPs | stat.ML | Determinantal point processes (DPPs) are distributions over sets of items
that model diversity using kernels. Their applications in machine learning
include summary extraction and recommendation systems. Yet, the cost of
sampling from a DPP is prohibitive in large-scale applications, which has
triggered an effort towar... | computer science |
11,989 | Online to Offline Conversions, Universality and Adaptive Minibatch Sizes | cs.LG | We present an approach towards convex optimization that relies on a novel
scheme which converts online adaptive algorithms into offline methods. In the
offline optimization setting, our derived methods are shown to obtain
favourable adaptive guarantees which depend on the harmonic sum of the queried
gradients. We furth... | computer science |
11,990 | Surface Networks | stat.ML | We study data-driven representations for three-dimensional triangle meshes,
which are one of the prevalent objects used to represent 3D geometry. Recent
works have developed models that exploit the intrinsic geometry of manifolds
and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants,
which learn... | computer science |
11,991 | Optimization of Tree Ensembles | math.OC | Tree ensemble models such as random forests and boosted trees are among the
most widely used and practically successful predictive models in applied
machine learning and business analytics. Although such models have been used to
make predictions based on exogenous, uncontrollable independent variables, they
are increas... | computer science |
11,992 | Sparse and low-rank approximations of large symmetric matrices using
biharmonic interpolation | stat.ML | Symmetric matrices are widely used in machine learning problems such as
kernel machines and manifold learning. Using large datasets often requires
computing low-rank approximations of these symmetric matrices so that they fit
in memory. In this paper, we present a novel method based on biharmonic
interpolation for low-... | computer science |
11,993 | Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization | stat.ML | In this paper, we consider the problem of sequentially optimizing a black-box
function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
process ... | computer science |
11,994 | Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine
Learning | cs.LG | Solving symmetric positive definite linear problems is a fundamental
computational task in machine learning. The exact solution, famously, is
cubicly expensive in the size of the matrix. To alleviate this problem, several
linear-time approximations, such as spectral and inducing-point methods, have
been suggested and a... | computer science |
11,995 | Supervised Quantile Normalisation | stat.ML | Quantile normalisation is a popular normalisation method for data subject to
unwanted variations such as images, speech, or genomic data. It applies a
monotonic transformation to the feature values of each sample to ensure that
after normalisation, they follow the same target distribution for each sample.
Choosing a "g... | computer science |
11,996 | Deep Learning: A Bayesian Perspective | stat.ML | Deep learning is a form of machine learning for nonlinear high dimensional
pattern matching and prediction. By taking a Bayesian probabilistic
perspective, we provide a number of insights into more efficient algorithms for
optimisation and hyper-parameter tuning. Traditional high-dimensional data
reduction techniques, ... | computer science |
11,997 | The Mixing method: coordinate descent for low-rank semidefinite
programming | math.OC | In this paper, we propose a coordinate descent approach to low-rank
structured semidefinite programming. The approach, which we call the Mixing
method, is extremely simple to implement, has no free parameters, and typically
attains an order of magnitude or better improvement in optimization performance
over the current... | computer science |
11,998 | Bias-Variance Tradeoff of Graph Laplacian Regularizer | stat.ML | This paper presents a bias-variance tradeoff of graph Laplacian regularizer,
which is widely used in graph signal processing and semi-supervised learning
tasks. The scaling law of the optimal regularization parameter is specified in
terms of the spectral graph properties and a novel signal-to-noise ratio
parameter, whi... | computer science |
11,999 | Parameter identification in Markov chain choice models | math.ST | This work studies the parameter identification problem for the Markov chain
choice model of Blanchet, Gallego, and Goyal used in assortment planning. In
this model, the product selected by a customer is determined by a Markov chain
over the products, where the products in the offered assortment are absorbing
states. Th... | computer science |
12,000 | Multiple Kernel Learning and Automatic Subspace Relevance Determination
for High-dimensional Neuroimaging Data | cs.LG | Alzheimer's disease is a major cause of dementia. Its diagnosis requires
accurate biomarkers that are sensitive to disease stages. In this respect, we
regard probabilistic classification as a method of designing a probabilistic
biomarker for disease staging. Probabilistic biomarkers naturally support the
interpretation... | computer science |
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