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6,900 | Multi-Task Learning Using Neighborhood Kernels | cs.LG | This paper introduces a new and effective algorithm for learning kernels in a
Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here,
our approach can be easily applied to standard single task learning, as well.
As shown by our empirical results, our algorithm consistently outperforms the
traditio... | computer science |
6,901 | Initialising Kernel Adaptive Filters via Probabilistic Inference | stat.ML | We present a probabilistic framework for both (i) determining the initial
settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive
KAFs whereby in addition to weights and dictionaries, kernel parameters are
learnt sequentially. This is achieved by formulating the estimator as a
probabilistic mode... | computer science |
6,902 | An Introduction to the Practical and Theoretical Aspects of
Mixture-of-Experts Modeling | stat.ML | Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data
arising from complex data generating processes (DGPs). In this article, we
demonstrate how different MoE models can be constructed to approximate the
underlying DGPs of arbitrary types of data. Due to the probabilistic nature of
MoE models, we... | computer science |
6,903 | Estimating the unseen from multiple populations | cs.LG | Given samples from a distribution, how many new elements should we expect to
find if we continue sampling this distribution? This is an important and
actively studied problem, with many applications ranging from unseen species
estimation to genomics. We generalize this extrapolation and related unseen
estimation proble... | computer science |
6,904 | Distral: Robust Multitask Reinforcement Learning | cs.LG | Most deep reinforcement learning algorithms are data inefficient in complex
and rich environments, limiting their applicability to many scenarios. One
direction for improving data efficiency is multitask learning with shared
neural network parameters, where efficiency may be improved through transfer
across related tas... | computer science |
6,905 | Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm
Dictionary | stat.ML | Kernel adaptive filters, a class of adaptive nonlinear time-series models,
are known by their ability to learn expressive autoregressive patterns from
sequential data. However, for trivial monotonic signals, they struggle to
perform accurate predictions and at the same time keep computational complexity
within desired ... | computer science |
6,906 | f-GANs in an Information Geometric Nutshell | cs.LG | Nowozin \textit{et al} showed last year how to extend the GAN
\textit{principle} to all $f$-divergences. The approach is elegant but falls
short of a full description of the supervised game, and says little about the
key player, the generator: for example, what does the generator actually
converge to if solving the GAN... | computer science |
6,907 | Learning linear structural equation models in polynomial time and sample
complexity | cs.LG | The problem of learning structural equation models (SEMs) from data is a
fundamental problem in causal inference. We develop a new algorithm --- which
is computationally and statistically efficient and works in the
high-dimensional regime --- for learning linear SEMs from purely observational
data with arbitrary noise ... | computer science |
6,908 | Deep Learning to Attend to Risk in ICU | cs.LG | Modeling physiological time-series in ICU is of high clinical importance.
However, data collected within ICU are irregular in time and often contain
missing measurements. Since absence of a measure would signify its lack of
importance, the missingness is indeed informative and might reflect the
decision making by the c... | computer science |
6,909 | Comparative Study of Inference Methods for Bayesian Nonnegative Matrix
Factorisation | stat.ML | In this paper, we study the trade-offs of different inference approaches for
Bayesian matrix factorisation methods, which are commonly used for predicting
missing values, and for finding patterns in the data. In particular, we
consider Bayesian nonnegative variants of matrix factorisation and
tri-factorisation, and com... | computer science |
6,910 | Cooperative Hierarchical Dirichlet Processes: Superposition vs.
Maximization | cs.LG | The cooperative hierarchical structure is a common and significant data
structure observed in, or adopted by, many research areas, such as: text mining
(author-paper-word) and multi-label classification (label-instance-feature).
Renowned Bayesian approaches for cooperative hierarchical structure modeling
are mostly bas... | computer science |
6,911 | DeepProbe: Information Directed Sequence Understanding and Chatbot
Design via Recurrent Neural Networks | stat.ML | Information extraction and user intention identification are central topics
in modern query understanding and recommendation systems. In this paper, we
propose DeepProbe, a generic information-directed interaction framework which
is built around an attention-based sequence to sequence (seq2seq) recurrent
neural network... | computer science |
6,912 | Bayesian Nonlinear Support Vector Machines for Big Data | stat.ML | We propose a fast inference method for Bayesian nonlinear support vector
machines that leverages stochastic variational inference and inducing points.
Our experiments show that the proposed method is faster than competing Bayesian
approaches and scales easily to millions of data points. It provides additional
features ... | computer science |
6,913 | Global optimization for low-dimensional switching linear regression and
bounded-error estimation | cs.LG | The paper provides global optimization algorithms for two particularly
difficult nonconvex problems raised by hybrid system identification: switching
linear regression and bounded-error estimation. While most works focus on local
optimization heuristics without global optimality guarantees or with guarantees
valid only... | computer science |
6,914 | Latent Gaussian Process Regression | stat.ML | We introduce Latent Gaussian Process Regression which is a latent variable
extension allowing modelling of non-stationary multi-modal processes using GPs.
The approach is built on extending the input space of a regression problem with
a latent variable that is used to modulate the covariance function over the
training ... | computer science |
6,915 | One-Shot Learning in Discriminative Neural Networks | stat.ML | We consider the task of one-shot learning of visual categories. In this paper
we explore a Bayesian procedure for updating a pretrained convnet to classify a
novel image category for which data is limited. We decompose this convnet into
a fixed feature extractor and softmax classifier. We assume that the target
weights... | computer science |
6,916 | Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal
Basis Learning | stat.ML | In this paper we are interested in the problem of learning an over-complete
basis and a methodology such that the reconstruction or inverse problem does
not need optimization. We analyze the optimality of the presented approaches,
their link to popular already known techniques s.a. Artificial Neural
Networks,k-means or... | computer science |
6,917 | Linear Time Complexity Deep Fourier Scattering Network and Extension to
Nonlinear Invariants | stat.ML | In this paper we propose a scalable version of a state-of-the-art
deterministic time-invariant feature extraction approach based on consecutive
changes of basis and nonlinearities, namely, the scattering network. The first
focus of the paper is to extend the scattering network to allow the use of
higher order nonlinear... | computer science |
6,918 | Recovering Latent Signals from a Mixture of Measurements using a
Gaussian Process Prior | stat.ML | In sensing applications, sensors cannot always measure the latent quantity of
interest at the required resolution, sometimes they can only acquire a blurred
version of it due the sensor's transfer function. To recover latent signals
when only noisy mixed measurements of the signal are available, we propose the
Gaussian... | computer science |
6,919 | Self-paced Convolutional Neural Network for Computer Aided Detection in
Medical Imaging Analysis | cs.LG | Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning t... | computer science |
6,920 | Can GAN Learn Topological Features of a Graph? | cs.LG | This paper is first-line research expanding GANs into graph topology
analysis. By leveraging the hierarchical connectivity structure of a graph, we
have demonstrated that generative adversarial networks (GANs) can successfully
capture topological features of any arbitrary graph, and rank edge sets by
different stages a... | computer science |
6,921 | Rates of Uniform Consistency for k-NN Regression | stat.ML | We derive high-probability finite-sample uniform rates of consistency for
$k$-NN regression that are optimal up to logarithmic factors under mild
assumptions. We moreover show that $k$-NN regression adapts to an unknown lower
intrinsic dimension automatically. We then apply the $k$-NN regression rates to
establish new ... | computer science |
6,922 | Causal Transfer Learning | cs.LG | An important goal in both transfer learning and causal inference is to make
accurate predictions when the distribution of the test set and the training
set(s) differ. Such a distribution shift may happen as a result of an external
intervention on the data generating process, causing certain aspects of the
distribution ... | computer science |
6,923 | A Nonlinear Kernel Support Matrix Machine for Matrix Learning | stat.ML | In many problems of supervised tensor learning (STL), real world data such as
face images or MRI scans are naturally represented as matrices, which are also
called as second order tensors. Most existing classifiers based on tensor
representation, such as support tensor machine (STM) need to solve iteratively
which occu... | computer science |
6,924 | A New Family of Near-metrics for Universal Similarity | stat.ML | We propose a family of near-metrics based on local graph diffusion to capture
similarity for a wide class of data sets. These quasi-metametrics, as their
names suggest, dispense with one or two standard axioms of metric spaces,
specifically distinguishability and symmetry, so that similarity between data
points of arbi... | computer science |
6,925 | Dictionary Learning and Sparse Coding-based Denoising for
High-Resolution Task Functional Connectivity MRI Analysis | cs.LG | We propose a novel denoising framework for task functional Magnetic Resonance
Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the
brain functional connectivity via dictionary learning and sparse coding (DLSC).
In order to address the limitations of the unsupervised DLSC-based fMRI
studies, we u... | computer science |
6,926 | Adversarial Variational Optimization of Non-Differentiable Simulators | stat.ML | Complex computer simulators are increasingly used across fields of science as
generative models tying parameters of an underlying theory to experimental
observations. Inference in this setup is often difficult, as simulators rarely
admit a tractable density or likelihood function. We introduce Adversarial
Variational O... | computer science |
6,927 | Sketched Subspace Clustering | stat.ML | The immense amount of daily generated and communicated data presents unique
challenges in their processing. Clustering, the grouping of data without the
presence of ground-truth labels, is an important tool for drawing inferences
from data. Subspace clustering (SC) is a relatively recent method that is able
to successf... | computer science |
6,928 | Learning uncertainty in regression tasks by artificial neural networks | stat.ML | We suggest a general approach to quantification of different forms of
uncertainty in regression tasks performed by artificial neural networks. It is
based on the simultaneous training of two neural networks with a joint loss
function. One of the networks performs predictions and the other simultaneously
quantifies the ... | computer science |
6,929 | Big Data Regression Using Tree Based Segmentation | stat.ML | Scaling regression to large datasets is a common problem in many application
areas. We propose a two step approach to scaling regression to large datasets.
Using a regression tree (CART) to segment the large dataset constitutes the
first step of this approach. The second step of this approach is to develop a
suitable r... | computer science |
6,930 | Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms:
A Case with Bounded Regret | cs.LG | In this paper, we study the combinatorial multi-armed bandit problem (CMAB)
with probabilistically triggered arms (PTAs). Under the assumption that the arm
triggering probabilities (ATPs) are positive for all arms, we prove that a
class of upper confidence bound (UCB) policies, named Combinatorial UCB with
exploration ... | computer science |
6,931 | Exploring Outliers in Crowdsourced Ranking for QoE | stat.ML | Outlier detection is a crucial part of robust evaluation for crowdsourceable
assessment of Quality of Experience (QoE) and has attracted much attention in
recent years. In this paper, we propose some simple and fast algorithms for
outlier detection and robust QoE evaluation based on the nonconvex optimization
principle... | computer science |
6,932 | Interpreting Classifiers through Attribute Interactions in Datasets | stat.ML | In this work we present the novel ASTRID method for investigating which
attribute interactions classifiers exploit when making predictions. Attribute
interactions in classification tasks mean that two or more attributes together
provide stronger evidence for a particular class label. Knowledge of such
interactions make... | computer science |
6,933 | Per-instance Differential Privacy and the Adaptivity of Posterior
Sampling in Linear and Ridge regression | stat.ML | Differential privacy (DP), ever since its advent, has been a controversial
object. On the one hand, it provides strong provable protection of individuals
in a data set, on the other hand, it has been heavily criticized for being not
practical, partially due to its complete independence to the actual data set it
tries t... | computer science |
6,934 | Stochastic Gradient Descent for Relational Logistic Regression via
Partial Network Crawls | stat.ML | Research in statistical relational learning has produced a number of methods
for learning relational models from large-scale network data. While these
methods have been successfully applied in various domains, they have been
developed under the unrealistic assumption of full data access. In practice,
however, the data ... | computer science |
6,935 | Comparing Aggregators for Relational Probabilistic Models | stat.ML | Relational probabilistic models have the challenge of aggregation, where one
variable depends on a population of other variables. Consider the problem of
predicting gender from movie ratings; this is challenging because the number of
movies per user and users per movie can vary greatly. Surprisingly, aggregation
is not... | computer science |
6,936 | Concept Drift Detection and Adaptation with Hierarchical Hypothesis
Testing | stat.ML | In a streaming environment, there is often a need for statistical prediction
models to detect and adapt to concept drifts (i.e., changes in the joint
distribution between predictor and response variables) so as to mitigate
deteriorating predictive performance over time. Various concept drift detection
approaches have b... | computer science |
6,937 | Linear Discriminant Generative Adversarial Networks | stat.ML | We develop a novel method for training of GANs for unsupervised and class
conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
discriminator of an LD-GAN is trained to maximize the linear separability
between distributions of hidden representations of generated and targeted
samples, while the ... | computer science |
6,938 | Error Bounds for Piecewise Smooth and Switching Regression | stat.ML | The paper deals with regression problems, in which the nonsmooth target is
assumed to switch between different operating modes. Specifically, piecewise
smooth (PWS) regression considers target functions switching deterministically
via a partition of the input space, while switching regression considers
arbitrary switch... | computer science |
6,939 | Towards Evolutional Compression | stat.ML | Compressing convolutional neural networks (CNNs) is essential for
transferring the success of CNNs to a wide variety of applications to mobile
devices. In contrast to directly recognizing subtle weights or filters as
redundant in a given CNN, this paper presents an evolutionary method to
automatically eliminate redunda... | computer science |
6,940 | Asymmetric Deep Supervised Hashing | cs.LG | Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a ... | computer science |
6,941 | General Latent Feature Modeling for Data Exploration Tasks | stat.ML | This paper introduces a general Bayesian non- parametric latent feature model
suitable to per- form automatic exploratory analysis of heterogeneous datasets,
where the attributes describing each object can be either discrete, continuous
or mixed variables. The proposed model presents several important properties.
First... | computer science |
6,942 | Max K-armed bandit: On the ExtremeHunter algorithm and beyond | stat.ML | This paper is devoted to the study of the max K-armed bandit problem, which
consists in sequentially allocating resources in order to detect extreme
values. Our contribution is twofold. We first significantly refine the analysis
of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and
next propose... | computer science |
6,943 | Efficient Algorithms for Non-convex Isotonic Regression through
Submodular Optimization | cs.LG | We consider the minimization of submodular functions subject to ordering
constraints. We show that this optimization problem can be cast as a convex
optimization problem on a space of uni-dimensional measures, with ordering
constraints corresponding to first-order stochastic dominance. We propose new
discretization sch... | computer science |
6,944 | Generator Reversal | stat.ML | We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we propose instead to use more flexible code distributions. These
distributions are e... | computer science |
6,945 | Human in the Loop: Interactive Passive Automata Learning via
Evidence-Driven State-Merging Algorithms | stat.ML | We present an interactive version of an evidence-driven state-merging (EDSM)
algorithm for learning variants of finite state automata. Learning these
automata often amounts to recovering or reverse engineering the model
generating the data despite noisy, incomplete, or imperfectly sampled data
sources rather than optim... | computer science |
6,946 | Orthogonal Recurrent Neural Networks with Scaled Cayley Transform | stat.ML | Recurrent Neural Networks (RNNs) are designed to handle sequential data but
suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent
Neural Networks (uRNNs) have been used to address this issue and in some cases,
exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose
a simp... | computer science |
6,947 | Towards Visual Explanations for Convolutional Neural Networks via Input
Resampling | cs.LG | The predictive power of neural networks often costs model interpretability.
Several techniques have been developed for explaining model outputs in terms of
input features; however, it is difficult to translate such interpretations into
actionable insight. Here, we propose a framework to analyze predictions in
terms of ... | computer science |
6,948 | Taming Non-stationary Bandits: A Bayesian Approach | stat.ML | We consider the multi armed bandit problem in non-stationary environments.
Based on the Bayesian method, we propose a variant of Thompson Sampling which
can be used in both rested and restless bandit scenarios. Applying discounting
to the parameters of prior distribution, we describe a way to systematically
reduce the ... | computer science |
6,949 | Interpretable Active Learning | stat.ML | Active learning has long been a topic of study in machine learning. However,
as increasingly complex and opaque models have become standard practice, the
process of active learning, too, has become more opaque. There has been little
investigation into interpreting what specific trends and patterns an active
learning st... | computer science |
6,950 | Deep Asymmetric Multi-task Feature Learning | cs.LG | We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can
learn deep representations shared across multiple tasks while effectively
preventing negative transfer that may happen in the feature sharing process.
Specifically, we introduce an asymmetric autoencoder term that allows reliable
predictors fo... | computer science |
6,951 | Using millions of emoji occurrences to learn any-domain representations
for detecting sentiment, emotion and sarcasm | stat.ML | NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy la... | computer science |
6,952 | Streaming kernel regression with provably adaptive mean, variance, and
regularization | stat.ML | We consider the problem of streaming kernel regression, when the observations
arrive sequentially and the goal is to recover the underlying mean function,
assumed to belong to an RKHS. The variance of the noise is not assumed to be
known. In this context, we tackle the problem of tuning the regularization
parameter ada... | computer science |
6,953 | Training Deep AutoEncoders for Collaborative Filtering | stat.ML | This paper proposes a novel model for the rating prediction task in
recommender systems which significantly outperforms previous state-of-the art
models on a time-split Netflix data set. Our model is based on deep autoencoder
with 6 layers and is trained end-to-end without any layer-wise pre-training. We
empirically de... | computer science |
6,954 | Efficient Contextual Bandits in Non-stationary Worlds | cs.LG | Most contextual bandit algorithms minimize regret against the best fixed
policy, a questionable benchmark for non-stationary environments that are
ubiquitous in applications. In this work, we develop several efficient
contextual bandit algorithms for non-stationary environments by equipping
existing methods for i.i.d. ... | computer science |
6,955 | Probabilistic Generative Adversarial Networks | cs.LG | We introduce the Probabilistic Generative Adversarial Network (PGAN), a new
GAN variant based on a new kind of objective function. The central idea is to
integrate a probabilistic model (a Gaussian Mixture Model, in our case) into
the GAN framework which supports a new kind of loss function (based on
likelihood rather ... | computer science |
6,956 | Learning Theory of Distributed Regression with Bias Corrected
Regularization Kernel Network | cs.LG | Distributed learning is an effective way to analyze big data. In distributed
regression, a typical approach is to divide the big data into multiple blocks,
apply a base regression algorithm on each of them, and then simply average the
output functions learnt from these blocks. Since the average process will
decrease th... | computer science |
6,957 | Why Adaptively Collected Data Have Negative Bias and How to Correct for
It | stat.ML | From scientific experiments to online A/B testing, the previously observed
data often affects how future experiments are performed, which in turn affects
which data will be collected. Such adaptivity introduces complex correlations
between the data and the collection procedure. In this paper, we prove that
when the dat... | computer science |
6,958 | Nonconvex Sparse Logistic Regression with Weakly Convex Regularization | cs.LG | In this work we propose to fit a sparse logistic regression model by a weakly
convex regularized nonconvex optimization problem. The idea is based on the
finding that a weakly convex function as an approximation of the $\ell_0$
pseudo norm is able to better induce sparsity than the commonly used $\ell_1$
norm. For a cl... | computer science |
6,959 | Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian
Prior Models | cs.LG | The problem of low rank matrix completion is considered in this paper. To
exploit the underlying low-rank structure of the data matrix, we propose a
hierarchical Gaussian prior model, where columns of the low-rank matrix are
assumed to follow a Gaussian distribution with zero mean and a common precision
matrix, and a W... | computer science |
6,960 | Parametric Adversarial Divergences are Good Task Losses for Generative
Modeling | cs.LG | Generative modeling of high dimensional data like images is a notoriously
difficult and ill-defined problem. In particular, how to evaluate a learned
generative model is unclear. In this paper, we argue that *adversarial
learning*, pioneered with generative adversarial networks (GANs), provides an
interesting framework... | computer science |
6,961 | Cascade Adversarial Machine Learning Regularized with a Unified
Embedding | stat.ML | Injecting adversarial examples during training, known as adversarial
training, can improve robustness against one-step attacks, but not for unknown
iterative attacks. To address this challenge, we first show iteratively
generated adversarial images easily transfer between networks trained with the
same strategy. Inspir... | computer science |
6,962 | Gradient-enhanced kriging for high-dimensional problems | cs.LG | Surrogate models provide a low computational cost alternative to evaluating
expensive functions. The construction of accurate surrogate models with large
numbers of independent variables is currently prohibitive because it requires a
large number of function evaluations. Gradient-enhanced kriging has the
potential to r... | computer science |
6,963 | Proceedings of the 2017 ICML Workshop on Human Interpretability in
Machine Learning (WHI 2017) | stat.ML | This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag. | computer science |
6,964 | Non-stationary Stochastic Optimization with Local Spatial and Temporal
Changes | stat.ML | We consider a non-stationary sequential stochastic optimization problem, in
which the underlying cost functions change over time under a variation budget
constraint. We propose an $L_{p,q}$-variation functional to quantify the
change, which captures local spatial and temporal variations of the sequence of
functions. Un... | computer science |
6,965 | Time Series Anomaly Detection; Detection of anomalous drops with limited
features and sparse examples in noisy highly periodic data | stat.ML | Google uses continuous streams of data from industry partners in order to
deliver accurate results to users. Unexpected drops in traffic can be an
indication of an underlying issue and may be an early warning that remedial
action may be necessary. Detecting such drops is non-trivial because streams
are variable and noi... | computer science |
6,966 | OpenML Benchmarking Suites and the OpenML100 | stat.ML | We advocate the use of curated, comprehensive benchmark suites of machine
learning datasets, backed by standardized OpenML-based interfaces and
complementary software toolkits written in Python, Java and R. Major
distinguishing features of OpenML benchmark suites are (a) ease of use through
standardized data formats, A... | computer science |
6,967 | Rocket Launching: A Universal and Efficient Framework for Training
Well-performing Light Net | stat.ML | Models applied on real time response task, like click-through rate (CTR)
prediction model, require high accuracy and rigorous response time. Therefore,
top-performing deep models of high depth and complexity are not well suited for
these applications with the limitations on the inference time. In order to
further impro... | computer science |
6,968 | Collaborative Filtering using Denoising Auto-Encoders for Market Basket
Data | stat.ML | Recommender systems (RS) help users navigate large sets of items in the
search for "interesting" ones. One approach to RS is Collaborative Filtering
(CF), which is based on the idea that similar users are interested in similar
items. Most model-based approaches to CF seek to train a
machine-learning/data-mining model b... | computer science |
6,969 | Actively Learning what makes a Discrete Sequence Valid | stat.ML | Deep learning techniques have been hugely successful for traditional
supervised and unsupervised machine learning problems. In large part, these
techniques solve continuous optimization problems. Recently however, discrete
generative deep learning models have been successfully used to efficiently
search high-dimensiona... | computer science |
6,970 | Machine Learning for Survival Analysis: A Survey | cs.LG | Accurately predicting the time of occurrence of an event of interest is a
critical problem in longitudinal data analysis. One of the main challenges in
this context is the presence of instances whose event outcomes become
unobservable after a certain time point or when some instances do not
experience any event during ... | computer science |
6,971 | Racing Thompson: an Efficient Algorithm for Thompson Sampling with
Non-conjugate Priors | cs.LG | Thompson sampling has impressive empirical performance for many multi-armed
bandit problems. But current algorithms for Thompson sampling only work for the
case of conjugate priors since these algorithms require to infer the posterior,
which is often computationally intractable when the prior is not conjugate. In
this ... | computer science |
6,972 | BitNet: Bit-Regularized Deep Neural Networks | cs.LG | We present a novel regularization scheme for training deep neural networks.
The parameters of neural networks are usually unconstrained and have a dynamic
range dispersed over the real line. Our key idea is to control the expressive
power of the network by dynamically quantizing the range and set of values that
the par... | computer science |
6,973 | Adaptive Threshold Sampling and Estimation | stat.ML | Sampling is a fundamental problem in both computer science and statistics. A
number of issues arise when designing a method based on sampling. These include
statistical considerations such as constructing a good sampling design and
ensuring there are good, tractable estimators for the quantities of interest as
well as ... | computer science |
6,974 | Corrupt Bandits for Preserving Local Privacy | cs.LG | We study a variant of the stochastic multi-armed bandit (MAB) problem in
which the rewards are corrupted. In this framework, motivated by privacy
preservation in online recommender systems, the goal is to maximize the sum of
the (unobserved) rewards, based on the observation of transformation of these
rewards through a... | computer science |
6,975 | Deep & Cross Network for Ad Click Predictions | cs.LG | Feature engineering has been the key to the success of many prediction
models. However, the process is non-trivial and often requires manual feature
engineering or exhaustive searching. DNNs are able to automatically learn
feature interactions; however, they generate all the interactions implicitly,
and are not necessa... | computer science |
6,976 | Robust Contextual Bandit via the Capped-$\ell_{2}$ norm | cs.LG | This paper considers the actor-critic contextual bandit for the mobile health
(mHealth) intervention. The state-of-the-art decision-making methods in mHealth
generally assume that the noise in the dynamic system follows the Gaussian
distribution. Those methods use the least-square-based algorithm to estimate
the expect... | computer science |
6,977 | Statistical Latent Space Approach for Mixed Data Modelling and
Applications | cs.LG | The analysis of mixed data has been raising challenges in statistics and
machine learning. One of two most prominent challenges is to develop new
statistical techniques and methodologies to effectively handle mixed data by
making the data less heterogeneous with minimum loss of information. The other
challenge is that ... | computer science |
6,978 | Semi-supervised Conditional GANs | stat.ML | We introduce a new model for building conditional generative models in a
semi-supervised setting to conditionally generate data given attributes by
adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model
uses a pair of stacked discriminators to learn the marginal distribution of the
data, and the co... | computer science |
6,979 | Accelerating Kernel Classifiers Through Borders Mapping | stat.ML | Support vector machines (SVM) and other kernel techniques represent a family
of powerful statistical classification methods with high accuracy and broad
applicability. Because they use all or a significant portion of the training
data, however, they can be slow, especially for large problems. Piecewise
linear classifie... | computer science |
6,980 | Explaining Anomalies in Groups with Characterizing Subspace Rules | cs.LG | Anomaly detection has numerous applications and has been studied vastly. We
consider a complementary problem that has a much sparser literature: anomaly
description. Interpretation of anomalies is crucial for practitioners for
sense-making, troubleshooting, and planning actions. To this end, we present a
new approach c... | computer science |
6,981 | Improving Deep Learning using Generic Data Augmentation | cs.LG | Deep artificial neural networks require a large corpus of training data in
order to effectively learn, where collection of such training data is often
expensive and laborious. Data augmentation overcomes this issue by artificially
inflating the training set with label preserving transformations. Recently
there has been... | computer science |
6,982 | General Backpropagation Algorithm for Training Second-order Neural
Networks | cs.LG | The artificial neural network is a popular framework in machine learning. To
empower individual neurons, we recently suggested that the current type of
neurons could be upgraded to 2nd order counterparts, in which the linear
operation between inputs to a neuron and the associated weights is replaced
with a nonlinear qu... | computer science |
6,983 | Deep vs. Diverse Architectures for Classification Problems | stat.ML | This study compares various superlearner and deep learning architectures
(machine-learning-based and neural-network-based) for classification problems
across several simulated and industrial datasets to assess performance and
computational efficiency, as both methods have nice theoretical convergence
properties. Superl... | computer science |
6,984 | Sum-Product Graphical Models | stat.ML | This paper introduces a new probabilistic architecture called Sum-Product
Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs)
and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference
using a class of models that incorporate context specific independence. Like
GMs, SPGMs p... | computer science |
6,985 | Stacked transfer learning for tropical cyclone intensity prediction | cs.LG | Tropical cyclone wind-intensity prediction is a challenging task considering
drastic changes climate patterns over the last few decades. In order to develop
robust prediction models, one needs to consider different characteristics of
cyclones in terms of spatial and temporal characteristics. Transfer learning
incorpora... | computer science |
6,986 | Learning Combinations of Sigmoids Through Gradient Estimation | stat.ML | We develop a new approach to learn the parameters of regression models with
hidden variables. In a nutshell, we estimate the gradient of the regression
function at a set of random points, and cluster the estimated gradients. The
centers of the clusters are used as estimates for the parameters of hidden
units. We justif... | computer science |
6,987 | Twin Networks: Matching the Future for Sequence Generation | cs.LG | We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a "backward" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and play... | computer science |
6,988 | Dynamic Input Structure and Network Assembly for Few-Shot Learning | cs.LG | The ability to learn from a small number of examples has been a difficult
problem in machine learning since its inception. While methods have succeeded
with large amounts of training data, research has been underway in how to
accomplish similar performance with fewer examples, known as one-shot or more
generally few-sh... | computer science |
6,989 | Scale-invariant unconstrained online learning | cs.LG | We consider a variant of online convex optimization in which both the
instances (input vectors) and the comparator (weight vector) are unconstrained.
We exploit a natural scale invariance symmetry in our unconstrained setting:
the predictions of the optimal comparator are invariant under any linear
transformation of th... | computer science |
6,990 | Massively-Parallel Feature Selection for Big Data | cs.LG | We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for
feature selection (FS) in Big Data settings (high dimensionality and/or sample
size). To tackle the challenges of Big Data FS PFBP partitions the data matrix
both in terms of rows (samples, training examples) as well as columns
(features). By e... | computer science |
6,991 | Accurate parameter estimation for Bayesian Network Classifiers using
Hierarchical Dirichlet Processes | cs.LG | This paper introduces a novel parameter estimation method for the probability
tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet
processes (HDPs). The main result of this paper is to show that improved
parameter estimation allows BNCs to outperform leading learning methods such as
Random Forest... | computer science |
6,992 | Joint Structured Learning and Predictions under Logical Constraints in
Conditional Random Fields | stat.ML | This paper is concerned with structured machine learning, in a supervised
machine learning context. It discusses how to make joint structured learning on
interdependent objects of different nature, as well as how to enforce logical
con-straints when predicting labels. We explain how this need arose in a
Document Unders... | computer science |
6,993 | Active Expansion Sampling for Learning Feasible Domains in an Unbounded
Input Space | cs.LG | Many engineering problems require identifying feasible domains under implicit
constraints. One example is finding acceptable car body styling designs based
on constraints like aesthetics and functionality. Current active-learning based
methods learn feasible domains for bounded input spaces. However, we usually
lack pr... | computer science |
6,994 | Sales Forecast in E-commerce using Convolutional Neural Network | cs.LG | Sales forecast is an essential task in E-commerce and has a crucial impact on
making informed business decisions. It can help us to manage the workforce,
cash flow and resources such as optimizing the supply chain of manufacturers
etc. Sales forecast is a challenging problem in that sales is affected by many
factors in... | computer science |
6,995 | Anomaly Detection in Wireless Sensor Networks | cs.LG | Wireless sensor networks usually comprise a large number of sensors
monitoring changes in variables. These changes in variables represent changes
in physical quantities. The changes can occur for various reasons; these
reasons are highlighted in this work. Outliers are unusual measurements.
Outliers are important; they... | computer science |
6,996 | Efficient Decision Trees for Multi-class Support Vector Machines Using
Entropy and Generalization Error Estimation | cs.LG | We propose new methods for Support Vector Machines (SVMs) using tree
architecture for multi-class classi- fication. In each node of the tree, we
select an appropriate binary classifier using entropy and generalization error
estimation, then group the examples into positive and negative classes based on
the selected cla... | computer science |
6,997 | EC3: Combining Clustering and Classification for Ensemble Learning | cs.LG | Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform wel... | computer science |
6,998 | Gradual Learning of Deep Recurrent Neural Networks | stat.ML | Deep Recurrent Neural Networks (RNNs) achieve state-of-the-art results in
many sequence-to-sequence tasks. However, deep RNNs are difficult to train and
suffer from overfitting. We introduce a training method that trains the network
gradually, and treats each layer individually, to achieve improved results in
language ... | computer science |
6,999 | Clustering Patients with Tensor Decomposition | stat.ML | In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as ... | computer science |
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