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6,500 | McGan: Mean and Covariance Feature Matching GAN | cs.LG | We introduce new families of Integral Probability Metrics (IPM) for training
Generative Adversarial Networks (GAN). Our IPMs are based on matching
statistics of distributions embedded in a finite dimensional feature space.
Mean and covariance feature matching IPMs allow for stable training of GANs,
which we will call M... | computer science |
6,501 | Boundary-Seeking Generative Adversarial Networks | stat.ML | Generative adversarial networks (GANs) are a learning framework that rely on
training a discriminator to estimate a measure of difference between a target
and generated distributions. GANs, as normally formulated, rely on the
generated samples being completely differentiable w.r.t. the generative
parameters, and thus d... | computer science |
6,502 | Semi-parametric Network Structure Discovery Models | cs.LG | We propose a network structure discovery model for continuous observations
that generalizes linear causal models by incorporating a Gaussian process (GP)
prior on a network-independent component, and random sparsity and weight
matrices as the network-dependent parameters. This approach provides flexible
modeling of net... | computer science |
6,503 | Fast Threshold Tests for Detecting Discrimination | stat.ML | Threshold tests have recently been proposed as a useful method for detecting
bias in lending, hiring, and policing decisions. For example, in the case of
credit extensions, these tests aim to estimate the bar for granting loans to
white and minority applicants, with a higher inferred threshold for minorities
indicative... | computer science |
6,504 | Active Learning Using Uncertainty Information | stat.ML | Many active learning methods belong to the retraining-based approaches, which
select one unlabeled instance, add it to the training set with its possible
labels, retrain the classification model, and evaluate the criteria that we
base our selection on. However, since the true label of the selected instance
is unknown, ... | computer science |
6,505 | Diameter-Based Active Learning | cs.LG | To date, the tightest upper and lower-bounds for the active learning of
general concept classes have been in terms of a parameter of the learning
problem called the splitting index. We provide, for the first time, an
efficient algorithm that is able to realize this upper bound, and we
empirically demonstrate its good p... | computer science |
6,506 | Learning Vector Autoregressive Models with Latent Processes | cs.LG | We study the problem of learning the support of transition matrix between
random processes in a Vector Autoregressive (VAR) model from samples when a
subset of the processes are latent. It is well known that ignoring the effect
of the latent processes may lead to very different estimates of the influences
among observe... | computer science |
6,507 | Speeding Up Latent Variable Gaussian Graphical Model Estimation via
Nonconvex Optimizations | stat.ML | We study the estimation of the latent variable Gaussian graphical model
(LVGGM), where the precision matrix is the superposition of a sparse matrix and
a low-rank matrix. In order to speed up the estimation of the sparse plus
low-rank components, we propose a sparsity constrained maximum likelihood
estimator based on m... | computer science |
6,508 | Towards Deeper Understanding of Variational Autoencoding Models | cs.LG | We propose a new family of optimization criteria for variational
auto-encoding models, generalizing the standard evidence lower bound. We
provide conditions under which they recover the data distribution and learn
latent features, and formally show that common issues such as blurry samples
and uninformative latent feat... | computer science |
6,509 | Algorithmic stability and hypothesis complexity | stat.ML | We introduce a notion of algorithmic stability of learning algorithms---that
we term \emph{argument stability}---that captures stability of the hypothesis
output by the learning algorithm in the normed space of functions from which
hypotheses are selected. The main result of the paper bounds the generalization
error of... | computer science |
6,510 | Learning Discrete Representations via Information Maximizing
Self-Augmented Training | stat.ML | Learning discrete representations of data is a central machine learning task
because of the compactness of the representations and ease of interpretation.
The task includes clustering and hash learning as special cases. Deep neural
networks are promising to be used because they can model the non-linearity of
data and s... | computer science |
6,511 | Central Moment Discrepancy (CMD) for Domain-Invariant Representation
Learning | stat.ML | The learning of domain-invariant representations in the context of domain
adaptation with neural networks is considered. We propose a new regularization
method that minimizes the discrepancy between domain-specific latent feature
representations directly in the hidden activation space. Although some standard
distributi... | computer science |
6,512 | Deep Forest: Towards An Alternative to Deep Neural Networks | cs.LG | In this paper, we propose gcForest, a decision tree ensemble approach with
performance highly competitive to deep neural networks. In contrast to deep
neural networks which require great effort in hyper-parameter tuning, gcForest
is much easier to train. Actually, even when gcForest is applied to different
data from di... | computer science |
6,513 | Iterative Bayesian Learning for Crowdsourced Regression | cs.LG | Crowdsourcing platforms emerged as popular venues for purchasing human
intelligence at low cost for large volumes of tasks. As many low-paid workers
are prone to give noisy answers, one of the fundamental questions is how to
identify more reliable workers and exploit this heterogeneity to infer the true
answers accurat... | computer science |
6,514 | Lipschitz Optimisation for Lipschitz Interpolation | cs.LG | Techniques known as Nonlinear Set Membership prediction, Kinky Inference or
Lipschitz Interpolation are fast and numerically robust approaches to
nonparametric machine learning that have been proposed to be utilised in the
context of system identification and learning-based control. They utilise
presupposed Lipschitz p... | computer science |
6,515 | A description length approach to determining the number of k-means
clusters | stat.ML | We present an asymptotic criterion to determine the optimal number of
clusters in k-means. We consider k-means as data compression, and propose to
adopt the number of clusters that minimizes the estimated description length
after compression. Here we report two types of compression ratio based on two
ways to quantify t... | computer science |
6,516 | Achieving non-discrimination in prediction | cs.LG | Discrimination-aware classification is receiving an increasing attention in
the data mining and machine learning fields. The data preprocessing methods for
constructing a discrimination-free classifier remove discrimination from the
training data, and learn the classifier from the cleaned data. However, there
lacks of ... | computer science |
6,517 | Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to
Non-smooth Concave Maximization | cs.LG | Iterative Hard Thresholding (IHT) is a class of projected gradient descent
methods for optimizing sparsity-constrained minimization models, with the best
known efficiency and scalability in practice. As far as we know, the existing
IHT-style methods are designed for sparse minimization in primal form. It
remains open t... | computer science |
6,518 | Modular Representation of Layered Neural Networks | stat.ML | Layered neural networks have greatly improved the performance of various
applications including image processing, speech recognition, natural language
processing, and bioinformatics. However, it is still difficult to discover or
interpret knowledge from the inference provided by a layered neural network,
since its inte... | computer science |
6,519 | L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines | stat.ML | For their ability to capture non-linearities in the data and to scale to
large training sets, local Support Vector Machines (SVMs) have received a
special attention during the past decade. In this paper, we introduce a new
local SVM method, called L$^3$-SVMs, which clusters the input space, carries
out dimensionality r... | computer science |
6,520 | Detecting Adversarial Samples from Artifacts | stat.ML | Deep neural networks (DNNs) are powerful nonlinear architectures that are
known to be robust to random perturbations of the input. However, these models
are vulnerable to adversarial perturbations--small input changes crafted
explicitly to fool the model. In this paper, we ask whether a DNN can
distinguish adversarial ... | computer science |
6,521 | Human Interaction with Recommendation Systems: On Bias and Exploration | stat.ML | Recommendation systems rely on historical user data to provide suggestions.
We propose an explicit and simple model for the interaction between users and
recommendations provided by a platform, and relate this model to the
multi-armed bandit literature. First, we show that this interaction leads to a
bias in naive esti... | computer science |
6,522 | Active Learning for Accurate Estimation of Linear Models | stat.ML | We explore the sequential decision making problem where the goal is to
estimate uniformly well a number of linear models, given a shared budget of
random contexts independently sampled from a known distribution. The decision
maker must query one of the linear models for each incoming context, and
receives an observatio... | computer science |
6,523 | Positive-Unlabeled Learning with Non-Negative Risk Estimator | cs.LG | From only positive (P) and unlabeled (U) data, a binary classifier could be
trained with PU learning, in which the state of the art is unbiased PU
learning. However, if its model is very flexible, empirical risks on training
data will go negative, and we will suffer from serious overfitting. In this
paper, we propose a... | computer science |
6,524 | A Unifying View of Explicit and Implicit Feature Maps for Structured
Data: Systematic Studies of Graph Kernels | cs.LG | Non-linear kernel methods can be approximated by fast linear ones using
suitable explicit feature maps allowing their application to large scale
problems. To this end, explicit feature maps of kernels for vectorial data have
been extensively studied. As many real-world data is structured, various
kernels for complex da... | computer science |
6,525 | Meta Networks | cs.LG | Neural networks have been successfully applied in applications with a large
amount of labeled data. However, the task of rapid generalization on new
concepts with small training data while preserving performances on previously
learned ones still presents a significant challenge to neural network models.
In this work, w... | computer science |
6,526 | Encrypted accelerated least squares regression | stat.ML | Information that is stored in an encrypted format is, by definition, usually
not amenable to statistical analysis or machine learning methods. In this paper
we present detailed analysis of coordinate and accelerated gradient descent
algorithms which are capable of fitting least squares and penalised ridge
regression mo... | computer science |
6,527 | Learning the Structure of Generative Models without Labeled Data | cs.LG | Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but select... | computer science |
6,528 | Self-Paced Multitask Learning with Shared Knowledge | stat.ML | This paper introduces self-paced task selection to multitask learning, where
instances from more closely related tasks are selected in a progression of
easier-to-harder tasks, to emulate an effective human education strategy, but
applied to multitask machine learning. We develop the mathematical foundation
for the appr... | computer science |
6,529 | Optimization of distributions differences for classification | cs.LG | In this paper we introduce a new classification algorithm called Optimization
of Distributions Differences (ODD). The algorithm aims to find a transformation
from the feature space to a new space where the instances in the same class are
as close as possible to one another while the gravity centers of these classes
are... | computer science |
6,530 | Co-Clustering for Multitask Learning | stat.ML | This paper presents a new multitask learning framework that learns a shared
representation among the tasks, incorporating both task and feature clusters.
The jointly-induced clusters yield a shared latent subspace where task
relationships are learned more effectively and more generally than in
state-of-the-art multitas... | computer science |
6,531 | Active Learning for Cost-Sensitive Classification | cs.LG | We design an active learning algorithm for cost-sensitive multiclass
classification: problems where different errors have different costs. Our
algorithm, COAL, makes predictions by regressing to each label's cost and
predicting the smallest. On a new example, it uses a set of regressors that
perform well on past data t... | computer science |
6,532 | Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and
Sample Complexity | cs.LG | Learning the directed acyclic graph (DAG) structure of a Bayesian network
from observational data is a notoriously difficult problem for which many
hardness results are known. In this paper we propose a provably polynomial-time
algorithm for learning sparse Gaussian Bayesian networks with equal noise
variance --- a cla... | computer science |
6,533 | Machine Learning on Sequential Data Using a Recurrent Weighted Average | stat.ML | Recurrent Neural Networks (RNN) are a type of statistical model designed to
handle sequential data. The model reads a sequence one symbol at a time. Each
symbol is processed based on information collected from the previous symbols.
With existing RNN architectures, each symbol is processed using only
information from th... | computer science |
6,534 | A Theory of Output-Side Unsupervised Domain Adaptation | cs.LG | When learning a mapping from an input space to an output space, the
assumption that the sample distribution of the training data is the same as
that of the test data is often violated. Unsupervised domain shift methods
adapt the learned function in order to correct for this shift. Previous work
has focused on utilizing... | computer science |
6,535 | Improving Regret Bounds for Combinatorial Semi-Bandits with
Probabilistically Triggered Arms and Its Applications | cs.LG | We study combinatorial multi-armed bandit with probabilistically triggered
arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior
CMAB-T studies where the regret bounds contain a possibly exponentially large
factor of $1/p^*$, where $p^*$ is the minimum positive probability that an arm
is trigg... | computer science |
6,536 | Measuring Sample Quality with Kernels | stat.ML | Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics
fail to detect these biases, researchers have developed computable Stein
discrepancy measures that provably determine the convergence of a sample to its
target dist... | computer science |
6,537 | Orthogonalized ALS: A Theoretically Principled Tensor Decomposition
Algorithm for Practical Use | cs.LG | The popular Alternating Least Squares (ALS) algorithm for tensor
decomposition is efficient and easy to implement, but often converges to poor
local optima---particularly when the weights of the factors are non-uniform. We
propose a modification of the ALS approach that is as efficient as standard
ALS, but provably rec... | computer science |
6,538 | Multiplicative Normalizing Flows for Variational Bayesian Neural
Networks | stat.ML | We reinterpret multiplicative noise in neural networks as auxiliary random
variables that augment the approximate posterior in a variational setting for
Bayesian neural networks. We show that through this interpretation it is both
efficient and straightforward to improve the approximation by employing
normalizing flows... | computer science |
6,539 | Neural Episodic Control | cs.LG | Deep reinforcement learning methods attain super-human performance in a wide
range of environments. Such methods are grossly inefficient, often taking
orders of magnitudes more data than humans to achieve reasonable performance.
We propose Neural Episodic Control: a deep reinforcement learning agent that is
able to rap... | computer science |
6,540 | Revisiting stochastic off-policy action-value gradients | stat.ML | Off-policy stochastic actor-critic methods rely on approximating the
stochastic policy gradient in order to derive an optimal policy. One may also
derive the optimal policy by approximating the action-value gradient. The use
of action-value gradients is desirable as policy improvement occurs along the
direction of stee... | computer science |
6,541 | Classification and clustering for samples of event time data using
non-homogeneous Poisson process models | cs.LG | Data of the form of event times arise in various applications. A simple model
for such data is a non-homogeneous Poisson process (NHPP) which is specified by
a rate function that depends on time. We consider the problem of having access
to multiple independent samples of event time data, observed on a common
interval, ... | computer science |
6,542 | Model-Based Multiple Instance Learning | stat.ML | While Multiple Instance (MI) data are point patterns -- sets or multi-sets of
unordered points -- appropriate statistical point pattern models have not been
used in MI learning. This article proposes a framework for model-based MI
learning using point process theory. Likelihood functions for point pattern
data derived ... | computer science |
6,543 | Global Weisfeiler-Lehman Graph Kernels | cs.LG | Most state-of-the-art graph kernels only take local graph properties into
account, i.e., the kernel is computed with regard to properties of the
neighborhood of vertices or other small substructures. On the other hand,
kernels that do take global graph propertiesinto account may not scale well to
large graph databases.... | computer science |
6,544 | On Structured Prediction Theory with Calibrated Convex Surrogate Losses | cs.LG | We provide novel theoretical insights on structured prediction in the context
of efficient convex surrogate loss minimization with consistency guarantees.
For any task loss, we construct a convex surrogate that can be optimized via
stochastic gradient descent and we prove tight bounds on the so-called
"calibration func... | computer science |
6,545 | An investigation into machine learning approaches for forecasting
spatio-temporal demand in ride-hailing service | cs.LG | In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the metho... | computer science |
6,546 | Online Multilinear Dictionary Learning for Sequential Compressive
Sensing | cs.LG | A method for online tensor dictionary learning is proposed. With the
assumption of separable dictionaries, tensor contraction is used to diminish a
$N$-way model of $\mathcal{O}\left(L^N\right)$ into a simple matrix equation of
$\mathcal{O}\left(NL^2\right)$ with a real-time capability. To avoid numerical
instability d... | computer science |
6,547 | Online Learning to Rank in Stochastic Click Models | cs.LG | Online learning to rank is a core problem in information retrieval and
machine learning. Many provably efficient algorithms have been recently
proposed for this problem in specific click models. The click model is a model
of how the user interacts with a list of documents. Though these results are
significant, their im... | computer science |
6,548 | Stopping GAN Violence: Generative Unadversarial Networks | stat.ML | While the costs of human violence have attracted a great deal of attention
from the research community, the effects of the network-on-network (NoN)
violence popularised by Generative Adversarial Networks have yet to be
addressed. In this work, we quantify the financial, social, spiritual,
cultural, grammatical and derm... | computer science |
6,549 | Regularising Non-linear Models Using Feature Side-information | cs.LG | Very often features come with their own vectorial descriptions which provide
detailed information about their properties. We refer to these vectorial
descriptions as feature side-information. In the standard learning scenario,
input is represented as a vector of features and the feature side-information
is most often i... | computer science |
6,550 | Online Convex Optimization with Unconstrained Domains and Losses | cs.LG | We propose an online convex optimization algorithm (RescaledExp) that
achieves optimal regret in the unconstrained setting without prior knowledge of
any bounds on the loss functions. We prove a lower bound showing an exponential
separation between the regret of existing algorithms that require a known bound
on the los... | computer science |
6,551 | Online Learning Without Prior Information | cs.LG | The vast majority of optimization and online learning algorithms today
require some prior information about the data (often in the form of bounds on
gradients or on the optimal parameter value). When this information is not
available, these algorithms require laborious manual tuning of various
hyperparameters, motivati... | computer science |
6,552 | Don't Fear the Bit Flips: Optimized Coding Strategies for Binary
Classification | stat.ML | After being trained, classifiers must often operate on data that has been
corrupted by noise. In this paper, we consider the impact of such noise on the
features of binary classifiers. Inspired by tools for classifier robustness, we
introduce the same classification probability (SCP) to measure the resulting
distortion... | computer science |
6,553 | Structural Data Recognition with Graph Model Boosting | cs.LG | This paper presents a novel method for structural data recognition using a
large number of graph models. In general, prevalent methods for structural data
recognition have two shortcomings: 1) Only a single model is used to capture
structural variation. 2) Naive recognition methods are used, such as the
nearest neighbo... | computer science |
6,554 | Dropout Inference in Bayesian Neural Networks with Alpha-divergences | cs.LG | To obtain uncertainty estimates with real-world Bayesian deep learning
models, practical inference approximations are needed. Dropout variational
inference (VI) for example has been used for machine vision and medical
applications, but VI can severely underestimates model uncertainty.
Alpha-divergences are alternative ... | computer science |
6,555 | Unsupervised Ensemble Regression | stat.ML | Consider a regression problem where there is no labeled data and the only
observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many
samples $x_j$. With no knowledge on the accuracy of the experts, is it still
possible to accurately estimate the unknown responses $y_{j}$? Can one still
detect the leas... | computer science |
6,556 | Spectral Graph Convolutions for Population-based Disease Prediction | stat.ML | Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph... | computer science |
6,557 | Parallel Implementation of Efficient Search Schemes for the Inference of
Cancer Progression Models | cs.LG | The emergence and development of cancer is a consequence of the accumulation
over time of genomic mutations involving a specific set of genes, which
provides the cancer clones with a functional selective advantage. In this work,
we model the order of accumulation of such mutations during the progression,
which eventual... | computer science |
6,558 | A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm | cs.LG | In this paper, we present an algorithm for the sparse signal recovery problem
that incorporates damped Gaussian generalized approximate message passing
(GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning
(SBL). In particular, GGAMP is used to implement the E-step in SBL in place of
matrix inversio... | computer science |
6,559 | Sample Efficient Feature Selection for Factored MDPs | cs.LG | In reinforcement learning, the state of the real world is often represented
by feature vectors. However, not all of the features may be pertinent for
solving the current task. We propose Feature Selection Explore and Exploit
(FS-EE), an algorithm that automatically selects the necessary features while
learning a Factor... | computer science |
6,560 | Deep Learning in Customer Churn Prediction: Unsupervised Feature
Learning on Abstract Company Independent Feature Vectors | cs.LG | As companies increase their efforts in retaining customers, being able to
predict accurately ahead of time, whether a customer will churn in the
foreseeable future is an extremely powerful tool for any marketing team. The
paper describes in depth the application of Deep Learning in the problem of
churn prediction. Usin... | computer science |
6,561 | Sequential Local Learning for Latent Graphical Models | cs.LG | Learning parameters of latent graphical models (GM) is inherently much harder
than that of no-latent ones since the latent variables make the corresponding
log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
popularly used in practice, but they are typically stuck in local optima. In
the rece... | computer science |
6,562 | Multiscale Hierarchical Convolutional Networks | cs.LG | Deep neural network algorithms are difficult to analyze because they lack
structure allowing to understand the properties of underlying transforms and
invariants. Multiscale hierarchical convolutional networks are structured deep
convolutional networks where layers are indexed by progressively higher
dimensional attrib... | computer science |
6,563 | Langevin Dynamics with Continuous Tempering for Training Deep Neural
Networks | cs.LG | Minimizing non-convex and high-dimensional objective functions is
challenging, especially when training modern deep neural networks. In this
paper, a novel approach is proposed which divides the training process into two
consecutive phases to obtain better generalization performance: Bayesian
sampling and stochastic op... | computer science |
6,564 | Online Learning Rate Adaptation with Hypergradient Descent | cs.LG | We introduce a general method for improving the convergence rate of
gradient-based optimizers that is easy to implement and works well in practice.
We demonstrate the effectiveness of the method in a range of optimization
problems by applying it to stochastic gradient descent, stochastic gradient
descent with Nesterov ... | computer science |
6,565 | Classification in biological networks with hypergraphlet kernels | stat.ML | Biological and cellular systems are often modeled as graphs in which vertices
represent objects of interest (genes, proteins, drugs) and edges represent
relational ties among these objects (binds-to, interacts-with, regulates). This
approach has been highly successful owing to the theory, methodology and
software that ... | computer science |
6,566 | Prototypical Networks for Few-shot Learning | cs.LG | We propose prototypical networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each new class. Prototypical networks
learn a metric space in which classification can be performed by computing
distances... | computer science |
6,567 | Aggregation of Classifiers: A Justifiable Information Granularity
Approach | cs.LG | In this study, we introduce a new approach to combine multi-classifiers in an
ensemble system. Instead of using numeric membership values encountered in
fixed combining rules, we construct interval membership values associated with
each class prediction at the level of meta-data of observation by using
concepts of info... | computer science |
6,568 | Cost-complexity pruning of random forests | stat.ML | Random forests perform bootstrap-aggregation by sampling the training samples
with replacement. This enables the evaluation of out-of-bag error which serves
as a internal cross-validation mechanism. Our motivation lies in using the
unsampled training samples to improve each decision tree in the ensemble. We
study the e... | computer science |
6,569 | Shift Aggregate Extract Networks | cs.LG | We introduce an architecture based on deep hierarchical decompositions to
learn effective representations of large graphs. Our framework extends classic
R-decompositions used in kernel methods, enabling nested "part-of-part"
relations. Unlike recursive neural networks, which unroll a template on input
graphs directly, ... | computer science |
6,570 | End-to-End Learning for Structured Prediction Energy Networks | stat.ML | Structured Prediction Energy Networks (SPENs) are a simple, yet expressive
family of structured prediction models (Belanger and McCallum, 2016). An energy
function over candidate structured outputs is given by a deep network, and
predictions are formed by gradient-based optimization. This paper presents
end-to-end lear... | computer science |
6,571 | Conditional Accelerated Lazy Stochastic Gradient Descent | cs.LG | In this work we introduce a conditional accelerated lazy stochastic gradient
descent algorithm with optimal number of calls to a stochastic first-order
oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
over the projection-free, Online Frank-Wolfe based stochastic gradient descent
of Hazan an... | computer science |
6,572 | Nonconvex One-bit Single-label Multi-label Learning | stat.ML | We study an extreme scenario in multi-label learning where each training
instance is endowed with a single one-bit label out of multiple labels. We
formulate this problem as a non-trivial special case of one-bit rank-one matrix
sensing and develop an efficient non-convex algorithm based on alternating
power iteration. ... | computer science |
6,573 | Deep Sets | cs.LG | In this paper, we study the problem of designing objective functions for
machine learning problems defined on finite \emph{sets}. In contrast to
traditional objective functions defined for machine learning problems operating
on finite dimensional vectors, the new objective functions we propose are
operating on finite s... | computer science |
6,574 | Bernoulli Rank-$1$ Bandits for Click Feedback | cs.LG | The probability that a user will click a search result depends both on its
relevance and its position on the results page. The position based model
explains this behavior by ascribing to every item an attraction probability,
and to every position an examination probability. To be clicked, a result must
be both attracti... | computer science |
6,575 | Efficient variational Bayesian neural network ensembles for outlier
detection | stat.ML | In this work we perform outlier detection using ensembles of neural networks
obtained by variational approximation of the posterior in a Bayesian neural
network setting. The variational parameters are obtained by sampling from the
true posterior by gradient descent. We show our outlier detection results are
comparable ... | computer science |
6,576 | On the Use of Default Parameter Settings in the Empirical Evaluation of
Classification Algorithms | cs.LG | We demonstrate that, for a range of state-of-the-art machine learning
algorithms, the differences in generalisation performance obtained using
default parameter settings and using parameters tuned via cross-validation can
be similar in magnitude to the differences in performance observed between
state-of-the-art and un... | computer science |
6,577 | Application of backpropagation neural networks to both stages of
fingerprinting based WIPS | stat.ML | We propose a scheme to employ backpropagation neural networks (BPNNs) for
both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal
strengths (FWIPS): radio map construction during the offline stage, and
localization during the online stage. Given a training radio map (TRM), i.e., a
set of coordinat... | computer science |
6,578 | Learning to Generate Samples from Noise through Infusion Training | stat.ML | In this work, we investigate a novel training procedure to learn a generative
model as the transition operator of a Markov chain, such that, when applied
repeatedly on an unstructured random noise sample, it will denoise it into a
sample that matches the target distribution from the training set. The novel
training pro... | computer science |
6,579 | Metalearning for Feature Selection | cs.LG | A general formulation of optimization problems in which various candidate
solutions may use different feature-sets is presented, encompassing supervised
classification, automated program learning and other cases. A novel
characterization of the concept of a "good quality feature" for such an
optimization problem is pro... | computer science |
6,580 | Nonparametric Variational Auto-encoders for Hierarchical Representation
Learning | cs.LG | The recently developed variational autoencoders (VAEs) have proved to be an
effective confluence of the rich representational power of neural networks with
Bayesian methods. However, most work on VAEs use a rather simple prior over the
latent variables such as standard normal distribution, thereby restricting its
appli... | computer science |
6,581 | On The Projection Operator to A Three-view Cardinality Constrained Set | cs.LG | The cardinality constraint is an intrinsic way to restrict the solution
structure in many domains, for example, sparse learning, feature selection, and
compressed sensing. To solve a cardinality constrained problem, the key
challenge is to solve the projection onto the cardinality constraint set, which
is NP-hard in ge... | computer science |
6,582 | REBAR: Low-variance, unbiased gradient estimates for discrete latent
variable models | cs.LG | Learning in models with discrete latent variables is challenging due to high
variance gradient estimators. Generally, approaches have relied on control
variates to reduce the variance of the REINFORCE estimator. Recent work (Jang
et al. 2016, Maddison et al. 2016) has taken a different approach, introducing
a continuou... | computer science |
6,583 | LogitBoost autoregressive networks | stat.ML | Multivariate binary distributions can be decomposed into products of
univariate conditional distributions. Recently popular approaches have modeled
these conditionals through neural networks with sophisticated weight-sharing
structures. It is shown that state-of-the-art performance on several standard
benchmark dataset... | computer science |
6,584 | Multitask Learning and Benchmarking with Clinical Time Series Data | stat.ML | Health care is one of the most exciting frontiers in data mining and machine
learning. Successful adoption of electronic health records (EHRs) created an
explosion in digital clinical data available for analysis, but progress in
machine learning for healthcare research has been difficult to measure because
of the absen... | computer science |
6,585 | Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor
Classification in Dynamic Time Warping Spaces | cs.LG | The nearest neighbor method together with the dynamic time warping (DTW)
distance is one of the most popular approaches in time series classification.
This method suffers from high storage and computation requirements for large
training sets. As a solution to both drawbacks, this article extends learning
vector quantiz... | computer science |
6,586 | Exploration--Exploitation in MDPs with Options | cs.LG | While a large body of empirical results show that temporally-extended actions
and options may significantly affect the learning performance of an agent, the
theoretical understanding of how and when options can be beneficial in online
reinforcement learning is relatively limited. In this paper, we derive an upper
and l... | computer science |
6,587 | Uncertainty quantification in graph-based classification of high
dimensional data | cs.LG | Classification of high dimensional data finds wide-ranging applications. In
many of these applications equipping the resulting classification with a
measure of uncertainty may be as important as the classification itself. In
this paper we introduce, develop algorithms for, and investigate the properties
of, a variety o... | computer science |
6,588 | Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
Variational Inference | stat.ML | We propose a simple and general variant of the standard reparameterized
gradient estimator for the variational evidence lower bound. Specifically, we
remove a part of the total derivative with respect to the variational
parameters that corresponds to the score function. Removing this term produces
an unbiased gradient ... | computer science |
6,589 | Factoring Exogenous State for Model-Free Monte Carlo | cs.LG | Policy analysts wish to visualize a range of policies for large
simulator-defined Markov Decision Processes (MDPs). One visualization approach
is to invoke the simulator to generate on-policy trajectories and then
visualize those trajectories. When the simulator is expensive, this is not
practical, and some method is r... | computer science |
6,590 | Fast Optimization of Wildfire Suppression Policies with SMAC | cs.LG | Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into... | computer science |
6,591 | Simulated Data Experiments for Time Series Classification Part 1:
Accuracy Comparison with Default Settings | cs.LG | There are now a broad range of time series classification (TSC) algorithms
designed to exploit different representations of the data. These have been
evaluated on a range of problems hosted at the UCR-UEA TSC Archive
(www.timeseriesclassification.com), and there have been extensive comparative
studies. However, our und... | computer science |
6,592 | Early Stopping without a Validation Set | cs.LG | Early stopping is a widely used technique to prevent poor generalization
performance when training an over-expressive model by means of gradient-based
optimization. To find a good point to halt the optimizer, a common practice is
to split the dataset into a training and a smaller validation set to obtain an
ongoing est... | computer science |
6,593 | Hybrid Clustering based on Content and Connection Structure using Joint
Nonnegative Matrix Factorization | cs.LG | We present a hybrid method for latent information discovery on the data sets
containing both text content and connection structure based on constrained low
rank approximation. The new method jointly optimizes the Nonnegative Matrix
Factorization (NMF) objective function for text clustering and the Symmetric
NMF (SymNMF... | computer science |
6,594 | Unifying the Stochastic Spectral Descent for Restricted Boltzmann
Machines with Bernoulli or Gaussian Inputs | stat.ML | Stochastic gradient descent based algorithms are typically used as the
general optimization tools for most deep learning models. A Restricted
Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked
to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean
algorithm such as st... | computer science |
6,595 | Inverse Risk-Sensitive Reinforcement Learning | cs.LG | We address the problem of inverse reinforcement learning in Markov decision
processes where the agent is risk-sensitive. In particular, we model
risk-sensitivity in a reinforcement learning framework by making use of models
of human decision-making having their origins in behavioral psychology,
behavioral economics, an... | computer science |
6,596 | Marginal likelihood based model comparison in Fuzzy Bayesian Learning | stat.ML | In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL)
paradigm where expert opinions can be encoded in the form of fuzzy rule bases
and the hyper-parameters of the fuzzy sets can be learned from data using a
Bayesian approach. The present paper extends this work for selecting the most
appropriate rule b... | computer science |
6,597 | Probabilistic Line Searches for Stochastic Optimization | cs.LG | In deterministic optimization, line searches are a standard tool ensuring
stability and efficiency. Where only stochastic gradients are available, no
direct equivalent has so far been formulated, because uncertain gradients do
not allow for a strict sequence of decisions collapsing the search space. We
construct a prob... | computer science |
6,598 | From Deep to Shallow: Transformations of Deep Rectifier Networks | cs.LG | In this paper, we introduce transformations of deep rectifier networks,
enabling the conversion of deep rectifier networks into shallow rectifier
networks. We subsequently prove that any rectifier net of any depth can be
represented by a maximum of a number of functions that can be realized by a
shallow network with a ... | computer science |
6,599 | On Fundamental Limits of Robust Learning | cs.LG | We consider the problems of robust PAC learning from distributed and
streaming data, which may contain malicious errors and outliers, and analyze
their fundamental complexity questions. In particular, we establish lower
bounds on the communication complexity for distributed robust learning
performed on multiple machine... | computer science |
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