Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
6,400 | Compressive Sensing via Convolutional Factor Analysis | stat.ML | We solve the compressive sensing problem via convolutional factor analysis,
where the convolutional dictionaries are learned {\em in situ} from the
compressed measurements. An alternating direction method of multipliers (ADMM)
paradigm for compressive sensing inversion based on convolutional factor
analysis is develope... | computer science |
6,401 | Sparse-TDA: Sparse Realization of Topological Data Analysis for
Multi-Way Classification | stat.ML | Topological data analysis (TDA) has emerged as one of the most promising
techniques to reconstruct the unknown shapes of high-dimensional spaces from
observed data samples. TDA, thus, yields key shape descriptors in the form of
persistent topological features that can be used for any supervised or
unsupervised learning... | computer science |
6,402 | Dictionary Learning from Incomplete Data | cs.LG | This paper extends the recently proposed and theoretically justified
iterative thresholding and $K$ residual means algorithm ITKrM to learning
dicionaries from incomplete/masked training data (ITKrMM). It further adapts
the algorithm to the presence of a low rank component in the data and provides
a strategy for recove... | computer science |
6,403 | Truncation-free Hybrid Inference for DPMM | cs.LG | Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
non-parametrics. While these models free from choosing the number of components
a-priori, computationally attractive variational inference often reintroduces
the need to do so, via a truncation on the variational distribution. In this
paper we presen... | computer science |
6,404 | Marked Temporal Dynamics Modeling based on Recurrent Neural Network | cs.LG | We are now witnessing the increasing availability of event stream data, i.e.,
a sequence of events with each event typically being denoted by the time it
occurs and its mark information (e.g., event type). A fundamental problem is to
model and predict such kind of marked temporal dynamics, i.e., when the next
event wil... | computer science |
6,405 | Learning Traffic as Images: A Deep Convolutional Neural Network for
Large-Scale Transportation Network Speed Prediction | cs.LG | This paper proposes a convolutional neural network (CNN)-based method that
learns traffic as images and predicts large-scale, network-wide traffic speed
with a high accuracy. Spatiotemporal traffic dynamics are converted to images
describing the time and space relations of traffic flow via a two-dimensional
time-space ... | computer science |
6,406 | Classification of MRI data using Deep Learning and Gaussian
Process-based Model Selection | cs.LG | The classification of MRI images according to the anatomical field of view is
a necessary task to solve when faced with the increasing quantity of medical
images. In parallel, advances in deep learning makes it a suitable tool for
computer vision problems. Using a common architecture (such as AlexNet)
provides quite go... | computer science |
6,407 | Towards a New Interpretation of Separable Convolutions | cs.LG | In recent times, the use of separable convolutions in deep convolutional
neural network architectures has been explored. Several researchers, most
notably (Chollet, 2016) and (Ghosh, 2017) have used separable convolutions in
their deep architectures and have demonstrated state of the art or close to
state of the art pe... | computer science |
6,408 | On The Construction of Extreme Learning Machine for Online and Offline
One-Class Classification - An Expanded Toolbox | cs.LG | One-Class Classification (OCC) has been prime concern for researchers and
effectively employed in various disciplines. But, traditional methods based
one-class classifiers are very time consuming due to its iterative process and
various parameters tuning. In this paper, we present six OCC methods based on
extreme learn... | computer science |
6,409 | On the Sample Complexity of Graphical Model Selection for Non-Stationary
Processes | cs.LG | We formulate and analyze a graphical model selection method for inferring the
conditional independence graph of a high-dimensional non-stationary Gaussian
random process (time series) from a finite-length observation. The observed
process samples are assumed uncorrelated over time but having different
covariance matric... | computer science |
6,410 | Towards Principled Methods for Training Generative Adversarial Networks | stat.ML | The goal of this paper is not to introduce a single algorithm or method, but
to make theoretical steps towards fully understanding the training dynamics of
generative adversarial networks. In order to substantiate our theoretical
analysis, we perform targeted experiments to verify our assumptions, illustrate
our claims... | computer science |
6,411 | 3D Morphology Prediction of Progressive Spinal Deformities from
Probabilistic Modeling of Discriminant Manifolds | cs.LG | We introduce a novel approach for predicting the progression of adolescent
idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
images. Recent progress in machine learning have allowed to improve
classification and prognosis rates, but lack a probabilistic framework to
measure uncertainty in the ... | computer science |
6,412 | A Machine Learning Alternative to P-values | stat.ML | This paper presents an alternative approach to p-values in regression
settings. This approach, whose origins can be traced to machine learning, is
based on the leave-one-out bootstrap for prediction error. In machine learning
this is called the out-of-bag (OOB) error. To obtain the OOB error for a model,
one draws a bo... | computer science |
6,413 | Multilayer Perceptron Algebra | stat.ML | Artificial Neural Networks(ANN) has been phenomenally successful on various
pattern recognition tasks. However, the design of neural networks rely heavily
on the experience and intuitions of individual developers. In this article, the
author introduces a mathematical structure called MLP algebra on the set of all
Multi... | computer science |
6,414 | Online Structure Learning for Sum-Product Networks with Gaussian Leaves | stat.ML | Sum-product networks have recently emerged as an attractive representation
due to their dual view as a special type of deep neural network with clear
semantics and a special type of probabilistic graphical model for which
inference is always tractable. Those properties follow from some conditions
(i.e., completeness an... | computer science |
6,415 | On the Existence of Kernel Function for Kernel-Trick of k-Means | cs.LG | This paper corrects the proof of the Theorem 2 from the Gower's paper
\cite[page 5]{Gower:1982}. The correction is needed in order to establish the
existence of the kernel function used commonly in the kernel trick e.g. for
$k$-means clustering algorithm, on the grounds of distance matrix. The scope of
correction is ex... | computer science |
6,416 | Variational Dropout Sparsifies Deep Neural Networks | stat.ML | We explore a recently proposed Variational Dropout technique that provided an
elegant Bayesian interpretation to Gaussian Dropout. We extend Variational
Dropout to the case when dropout rates are unbounded, propose a way to reduce
the variance of the gradient estimator and report first experimental results
with individ... | computer science |
6,417 | PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture
Likelihood and Other Modifications | cs.LG | PixelCNNs are a recently proposed class of powerful generative models with
tractable likelihood. Here we discuss our implementation of PixelCNNs which we
make available at https://github.com/openai/pixel-cnn. Our implementation
contains a number of modifications to the original model that both simplify its
structure an... | computer science |
6,418 | Poisson--Gamma Dynamical Systems | stat.ML | We introduce a new dynamical system for sequentially observed multivariate
count data. This model is based on the gamma--Poisson construction---a natural
choice for count data---and relies on a novel Bayesian nonparametric prior that
ties and shrinks the model parameters, thus avoiding overfitting. We present an
effici... | computer science |
6,419 | Rare Disease Physician Targeting: A Factor Graph Approach | stat.ML | In rare disease physician targeting, a major challenge is how to identify
physicians who are treating diagnosed or underdiagnosed rare diseases patients.
Rare diseases have extremely low incidence rate. For a specified rare disease,
only a small number of patients are affected and a fractional of physicians are
involve... | computer science |
6,420 | Comparative study on supervised learning methods for identifying
phytoplankton species | stat.ML | Phytoplankton plays an important role in marine ecosystem. It is defined as a
biological factor to assess marine quality. The identification of phytoplankton
species has a high potential for monitoring environmental, climate changes and
for evaluating water quality. However, phytoplankton species identification is
not ... | computer science |
6,421 | Aggressive Sampling for Multi-class to Binary Reduction with
Applications to Text Classification | stat.ML | We address the problem of multi-class classification in the case where the
number of classes is very large. We propose a double sampling strategy on top
of a multi-class to binary reduction strategy, which transforms the original
multi-class problem into a binary classification problem over pairs of
examples. The aim o... | computer science |
6,422 | Patchwork Kriging for Large-scale Gaussian Process Regression | cs.LG | This paper presents a new approach for Gaussian process (GP) regression for
large datasets. The approach involves partitioning the regression input domain
into multiple local regions with a different local GP model fitted in each
region. Unlike existing local partitioned GP approaches, we introduce a
technique for patc... | computer science |
6,423 | Privileged Multi-label Learning | stat.ML | This paper presents privileged multi-label learning (PrML) to explore and
exploit the relationship between labels in multi-label learning problems. We
suggest that for each individual label, it cannot only be implicitly connected
with other labels via the low-rank constraint over label predictors, but also
its performa... | computer science |
6,424 | k*-Nearest Neighbors: From Global to Local | stat.ML | The weighted k-nearest neighbors algorithm is one of the most fundamental
non-parametric methods in pattern recognition and machine learning. The
question of setting the optimal number of neighbors as well as the optimal
weights has received much attention throughout the years, nevertheless this
problem seems to have r... | computer science |
6,425 | A Convex Similarity Index for Sparse Recovery of Missing Image Samples | cs.LG | This paper investigates the problem of recovering missing samples using
methods based on sparse representation adapted especially for image signals.
Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality
measure is used as the similarity criterion between the original and the
reconstructed images. T... | computer science |
6,426 | Exploiting Convolutional Neural Network for Risk Prediction with Medical
Feature Embedding | cs.LG | The widespread availability of electronic health records (EHRs) promises to
usher in the era of personalized medicine. However, the problem of extracting
useful clinical representations from longitudinal EHR data remains challenging.
In this paper, we explore deep neural network models with learned medical
feature embe... | computer science |
6,427 | Theoretical Foundations of Forward Feature Selection Methods based on
Mutual Information | stat.ML | Feature selection problems arise in a variety of applications, such as
microarray analysis, clinical prediction, text categorization, image
classification and face recognition, multi-label learning, and classification
of internet traffic. Among the various classes of methods, forward feature
selection methods based on ... | computer science |
6,428 | Riemannian-geometry-based modeling and clustering of network-wide
non-stationary time series: The brain-network case | cs.LG | This paper advocates Riemannian multi-manifold modeling in the context of
network-wide non-stationary time-series analysis. Time-series data, collected
sequentially over time and across a network, yield features which are viewed as
points in or close to a union of multiple submanifolds of a Riemannian
manifold, and dis... | computer science |
6,429 | Linear convergence of SDCA in statistical estimation | stat.ML | In this paper, we consider stochastic dual coordinate (SDCA) {\em without}
strongly convex assumption or convex assumption. We show that SDCA converges
linearly under mild conditions termed restricted strong convexity. This covers
a wide array of popular statistical models including Lasso, group Lasso, and
logistic reg... | computer science |
6,430 | Wasserstein GAN | stat.ML | We introduce a new algorithm named WGAN, an alternative to traditional GAN
training. In this new model, we show that we can improve the stability of
learning, get rid of problems like mode collapse, and provide meaningful
learning curves useful for debugging and hyperparameter searches. Furthermore,
we show that the co... | computer science |
6,431 | The Price of Differential Privacy For Online Learning | cs.LG | We design differentially private algorithms for the problem of online linear
optimization in the full information and bandit settings with optimal
$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
results demonstrate that $\epsilon$-differential privacy may be ensured for
free -- in particular, ... | computer science |
6,432 | Model-based Classification and Novelty Detection For Point Pattern Data | cs.LG | Point patterns are sets or multi-sets of unordered elements that can be found
in numerous data sources. However, in data analysis tasks such as
classification and novelty detection, appropriate statistical models for point
pattern data have not received much attention. This paper proposes the
modelling of point pattern... | computer science |
6,433 | Dynamic Task Allocation for Crowdsourcing Settings | cs.LG | We consider the problem of optimal budget allocation for crowdsourcing
problems, allocating users to tasks to maximize our final confidence in the
crowdsourced answers. Such an optimized worker assignment method allows us to
boost the efficacy of any popular crowdsourcing estimation algorithm. We
consider a mutual info... | computer science |
6,434 | Spatial Projection of Multiple Climate Variables using Hierarchical
Multitask Learning | cs.LG | Future projection of climate is typically obtained by combining outputs from
multiple Earth System Models (ESMs) for several climate variables such as
temperature and precipitation. While IPCC has traditionally used a simple model
output average, recent work has illustrated potential advantages of using a
multitask lea... | computer science |
6,435 | Variable selection for clustering with Gaussian mixture models: state of
the art | stat.ML | The mixture models have become widely used in clustering, given its
probabilistic framework in which its based, however, for modern databases that
are characterized by their large size, these models behave disappointingly in
setting out the model, making essential the selection of relevant variables for
this type of cl... | computer science |
6,436 | A Dirichlet Mixture Model of Hawkes Processes for Event Sequence
Clustering | cs.LG | We propose an effective method to solve the event sequence clustering
problems based on a novel Dirichlet mixture model of a special but significant
type of point processes --- Hawkes process. In this model, each event sequence
belonging to a cluster is generated via the same Hawkes process with specific
parameters, an... | computer science |
6,437 | PCA-Initialized Deep Neural Networks Applied To Document Image Analysis | cs.LG | In this paper, we present a novel approach for initializing deep neural
networks, i.e., by turning PCA into neural layers. Usually, the initialization
of the weights of a deep neural network is done in one of the three following
ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or
as auto-encod... | computer science |
6,438 | Recovering True Classifier Performance in Positive-Unlabeled Learning | stat.ML | A common approach in positive-unlabeled learning is to train a classification
model between labeled and unlabeled data. This strategy is in fact known to
give an optimal classifier under mild conditions; however, it results in biased
empirical estimates of the classifier performance. In this work, we show that
the typi... | computer science |
6,439 | Towards Better Analysis of Machine Learning Models: A Visual Analytics
Perspective | cs.LG | Interactive model analysis, the process of understanding, diagnosing, and
refining a machine learning model with the help of interactive visualization,
is very important for users to efficiently solve real-world artificial
intelligence and data mining problems. Dramatic advances in big data analytics
has led to a wide ... | computer science |
6,440 | Simple to Complex Cross-modal Learning to Rank | cs.LG | The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often esta... | computer science |
6,441 | Network-based methods for outcome prediction in the "sample space" | cs.LG | In this thesis we present the novel semi-supervised network-based algorithm
P-Net, which is able to rank and classify patients with respect to a specific
phenotype or clinical outcome under study. The peculiar and innovative
characteristic of this method is that it builds a network of samples/patients,
where the nodes ... | computer science |
6,442 | Learning similarity preserving representations with neural similarity
encoders | stat.ML | Many dimensionality reduction or manifold learning algorithms optimize for
retaining the pairwise similarities, distances, or local neighborhoods of data
points. Spectral methods like Kernel PCA (kPCA) or isomap achieve this by
computing the singular value decomposition (SVD) of some similarity matrix to
obtain a low d... | computer science |
6,443 | Sparse Algorithm for Robust LSSVM in Primal Space | cs.LG | As enjoying the closed form solution, least squares support vector machine
(LSSVM) has been widely used for classification and regression problems having
the comparable performance with other types of SVMs. However, LSSVM has two
drawbacks: sensitive to outliers and lacking sparseness. Robust LSSVM (R-LSSVM)
overcomes ... | computer science |
6,444 | Gated Multimodal Units for Information Fusion | stat.ML | This paper presents a novel model for multimodal learning based on gated
neural networks. The Gated Multimodal Unit (GMU) model is intended to be used
as an internal unit in a neural network architecture whose purpose is to find
an intermediate representation based on a combination of data from different
modalities. Th... | computer science |
6,445 | Truncated Variational EM for Semi-Supervised Neural Simpletrons | stat.ML | Inference and learning for probabilistic generative networks is often very
challenging and typically prevents scalability to as large networks as used for
deep discriminative approaches. To obtain efficiently trainable, large-scale
and well performing generative networks for semi-supervised learning, we here
combine tw... | computer science |
6,446 | Clustering For Point Pattern Data | cs.LG | Clustering is one of the most common unsupervised learning tasks in machine
learning and data mining. Clustering algorithms have been used in a plethora of
applications across several scientific fields. However, there has been limited
research in the clustering of point patterns - sets or multi-sets of unordered
elemen... | computer science |
6,447 | Learning detectors of malicious web requests for intrusion detection in
network traffic | stat.ML | This paper proposes a generic classification system designed to detect
security threats based on the behavior of malware samples. The system relies on
statistical features computed from proxy log fields to train detectors using a
database of malware samples. The behavior detectors serve as basic reusable
building block... | computer science |
6,448 | A Modified Construction for a Support Vector Classifier to Accommodate
Class Imbalances | stat.ML | Given a training set with binary classification, the Support Vector Machine
identifies the hyperplane maximizing the margin between the two classes of
training data. This general formulation is useful in that it can be applied
without regard to variance differences between the classes. Ignoring these
differences is not... | computer science |
6,449 | Minimax Lower Bounds for Ridge Combinations Including Neural Nets | stat.ML | Estimation of functions of $ d $ variables is considered using ridge
combinations of the form $ \textstyle\sum_{k=1}^m c_{1,k}
\phi(\textstyle\sum_{j=1}^d c_{0,j,k}x_j-b_k) $ where the activation function $
\phi $ is a function with bounded value and derivative. These include
single-hidden layer neural networks, polyno... | computer science |
6,450 | Coordinated Online Learning With Applications to Learning User
Preferences | cs.LG | We study an online multi-task learning setting, in which instances of related
tasks arrive sequentially, and are handled by task-specific online learners. We
consider an algorithmic framework to model the relationship of these tasks via
a set of convex constraints. To exploit this relationship, we design a novel
algori... | computer science |
6,451 | Generative Mixture of Networks | cs.LG | A generative model based on training deep architectures is proposed. The
model consists of K networks that are trained together to learn the underlying
distribution of a given data set. The process starts with dividing the input
data into K clusters and feeding each of them into a separate network. After
few iterations... | computer science |
6,452 | Batch Policy Gradient Methods for Improving Neural Conversation Models | stat.ML | We study reinforcement learning of chatbots with recurrent neural network
architectures when the rewards are noisy and expensive to obtain. For instance,
a chatbot used in automated customer service support can be scored by quality
assurance agents, but this process can be expensive, time consuming and noisy.
Previous ... | computer science |
6,453 | Soft Weight-Sharing for Neural Network Compression | stat.ML | The success of deep learning in numerous application domains created the de-
sire to run and train them on mobile devices. This however, conflicts with
their computationally, memory and energy intense nature, leading to a growing
interest in compression. Recent work by Han et al. (2015a) propose a pipeline
that involve... | computer science |
6,454 | Is a Data-Driven Approach still Better than Random Choice with Naive
Bayes classifiers? | cs.LG | We study the performance of data-driven, a priori and random approaches to
label space partitioning for multi-label classification with a Gaussian Naive
Bayes classifier. Experiments were performed on 12 benchmark data sets and
evaluated on 5 established measures of classification quality: micro and macro
averaged F1 s... | computer science |
6,455 | Practical Learning of Predictive State Representations | stat.ML | Over the past decade there has been considerable interest in spectral
algorithms for learning Predictive State Representations (PSRs). Spectral
algorithms have appealing theoretical guarantees; however, the resulting models
do not always perform well on inference tasks in practice. One reason for this
behavior is the m... | computer science |
6,456 | Small Boxes Big Data: A Deep Learning Approach to Optimize Variable
Sized Bin Packing | cs.LG | Bin Packing problems have been widely studied because of their broad
applications in different domains. Known as a set of NP-hard problems, they
have different vari- ations and many heuristics have been proposed for
obtaining approximate solutions. Specifically, for the 1D variable sized bin
packing problem, the two ke... | computer science |
6,457 | Nearest Labelset Using Double Distances for Multi-label Classification | stat.ML | Multi-label classification is a type of supervised learning where an instance
may belong to multiple labels simultaneously. Predicting each label
independently has been criticized for not exploiting any correlation between
labels. In this paper we propose a novel approach, Nearest Labelset using
Double Distances (NLDD)... | computer science |
6,458 | Dynamic Partition Models | stat.ML | We present a new approach for learning compact and intuitive distributed
representations with binary encoding. Rather than summing up expert votes as in
products of experts, we employ for each variable the opinion of the most
reliable expert. Data points are hence explained through a partitioning of the
variables into ... | computer science |
6,459 | Semi-supervised Learning for Discrete Choice Models | stat.ML | We introduce a semi-supervised discrete choice model to calibrate discrete
choice models when relatively few requests have both choice sets and stated
preferences but the majority only have the choice sets. Two classic
semi-supervised learning algorithms, the expectation maximization algorithm and
the cluster-and-label... | computer science |
6,460 | The Simulator: Understanding Adaptive Sampling in the
Moderate-Confidence Regime | cs.LG | We propose a novel technique for analyzing adaptive sampling called the {\em
Simulator}. Our approach differs from the existing methods by considering not
how much information could be gathered by any fixed sampling strategy, but how
difficult it is to distinguish a good sampling strategy from a bad one given
the limit... | computer science |
6,461 | Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure
Prediction | cs.LG | Developing a Brain-Computer Interface~(BCI) for seizure prediction can help
epileptic patients have a better quality of life. However, there are many
difficulties and challenges in developing such a system as a real-life support
for patients. Because of the nonstationary nature of EEG signals, normal and
seizure patter... | computer science |
6,462 | Dataset Augmentation in Feature Space | stat.ML | Dataset augmentation, the practice of applying a wide array of
domain-specific transformations to synthetically expand a training set, is a
standard tool in supervised learning. While effective in tasks such as visual
recognition, the set of transformations must be carefully designed,
implemented, and tested for every ... | computer science |
6,463 | Revisiting Perceptron: Efficient and Label-Optimal Learning of
Halfspaces | cs.LG | It has been a long-standing problem to efficiently learn a halfspace using as
few labels as possible in the presence of noise. In this work, we propose an
efficient Perceptron-based algorithm for actively learning homogeneous
halfspaces under the uniform distribution over the unit sphere. Under the
bounded noise condit... | computer science |
6,464 | Compressive Embedding and Visualization using Graphs | cs.LG | Visualizing high-dimensional data has been a focus in data analysis
communities for decades, which has led to the design of many algorithms, some
of which are now considered references (such as t-SNE for example). In our era
of overwhelming data volumes, the scalability of such methods have become more
and more importa... | computer science |
6,465 | An Improved Parametrization and Analysis of the EXP3++ Algorithm for
Stochastic and Adversarial Bandits | cs.LG | We present a new strategy for gap estimation in randomized algorithms for
multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and
Slivkins (2014). In the stochastic regime the strategy reduces dependence of
regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an
additive factor of o... | computer science |
6,466 | Convolution Aware Initialization | cs.LG | Initialization of parameters in deep neural networks has been shown to have a
big impact on the performance of the networks (Mishkin & Matas, 2015). The
initialization scheme devised by He et al, allowed convolution activations to
carry a constrained mean which allowed deep networks to be trained effectively
(He et al.... | computer science |
6,467 | Interpreting Outliers: Localized Logistic Regression for Density Ratio
Estimation | stat.ML | We propose an inlier-based outlier detection method capable of both
identifying the outliers and explaining why they are outliers, by identifying
the outlier-specific features. Specifically, we employ an inlier-based outlier
detection criterion, which uses the ratio of inlier and test probability
densities as a measure... | computer science |
6,468 | Causal Inference on Multivariate and Mixed-Type Data | stat.ML | Given data over the joint distribution of two random variables $X$ and $Y$,
we consider the problem of inferring the most likely causal direction between
$X$ and $Y$. In particular, we consider the general case where both $X$ and $Y$
may be univariate or multivariate, and of the same or mixed data types. We take
an inf... | computer science |
6,469 | A Unified Optimization View on Generalized Matching Pursuit and
Frank-Wolfe | cs.LG | Two of the most fundamental prototypes of greedy optimization are the
matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
view on both classes of methods, leading to the first explicit convergence
rates of matching pursuit methods in an optimization sense, for general sets of
atoms. We derive ... | computer science |
6,470 | Stochastic Canonical Correlation Analysis | cs.LG | We tightly analyze the sample complexity of CCA, provide a learning algorithm
that achieves optimal statistical performance in time linear in the required
number of samples (up to log factors), as well as a streaming algorithm with
similar guarantees. | computer science |
6,471 | Exemplar-Centered Supervised Shallow Parametric Data Embedding | cs.LG | Metric learning methods for dimensionality reduction in combination with
k-Nearest Neighbors (kNN) have been extensively deployed in many
classification, data embedding, and information retrieval applications.
However, most of these approaches involve pairwise training data comparisons,
and thus have quadratic computat... | computer science |
6,472 | Counterfactual Control for Free from Generative Models | cs.LG | We introduce a method by which a generative model learning the joint
distribution between actions and future states can be used to automatically
infer a control scheme for any desired reward function, which may be altered on
the fly without retraining the model. In this method, the problem of action
selection is reduce... | computer science |
6,473 | Stochastic Approximation for Canonical Correlation Analysis | cs.LG | We propose novel first-order stochastic approximation algorithms for
canonical correlation analysis (CCA). Algorithms presented are instances of
inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated
gradient (MEG), and achieve $\epsilon$-suboptimality in the population
objective in $\operatorname{po... | computer science |
6,474 | Adversarial examples for generative models | stat.ML | We explore methods of producing adversarial examples on deep generative
models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
architectures are known to be vulnerable to adversarial examples, but previous
work has focused on the application of adversarial examples to classification
tasks. Deep... | computer science |
6,475 | liquidSVM: A Fast and Versatile SVM package | stat.ML | liquidSVM is a package written in C++ that provides SVM-type solvers for
various classification and regression tasks. Because of a fully integrated
hyper-parameter selection, very carefully implemented solvers, multi-threading
and GPU support, and several built-in data decomposition strategies it provides
unprecedented... | computer science |
6,476 | Heavy-Tailed Analogues of the Covariance Matrix for ICA | cs.LG | Independent Component Analysis (ICA) is the problem of learning a square
matrix $A$, given samples of $X=AS$, where $S$ is a random vector with
independent coordinates. Most existing algorithms are provably efficient only
when each $S_i$ has finite and moderately valued fourth moment. However, there
are practical appli... | computer science |
6,477 | One Size Fits Many: Column Bundle for Multi-X Learning | stat.ML | Much recent machine learning research has been directed towards leveraging
shared statistics among labels, instances and data views, commonly referred to
as multi-label, multi-instance and multi-view learning. The underlying premises
are that there exist correlations among input parts and among output targets,
and the ... | computer science |
6,478 | Consistent On-Line Off-Policy Evaluation | stat.ML | The problem of on-line off-policy evaluation (OPE) has been actively studied
in the last decade due to its importance both as a stand-alone problem and as a
module in a policy improvement scheme. However, most Temporal Difference (TD)
based solutions ignore the discrepancy between the stationary distribution of
the beh... | computer science |
6,479 | Automatic Representation for Lifetime Value Recommender Systems | stat.ML | Many modern commercial sites employ recommender systems to propose relevant
content to users. While most systems are focused on maximizing the immediate
gain (clicks, purchases or ratings), a better notion of success would be the
lifetime value (LTV) of the user-system interaction. The LTV approach considers
the future... | computer science |
6,480 | Rotting Bandits | stat.ML | The Multi-Armed Bandits (MAB) framework highlights the tension between
acquiring new knowledge (Exploration) and leveraging available knowledge
(Exploitation). In the classical MAB problem, a decision maker must choose an
arm at each time step, upon which she receives a reward. The decision maker's
objective is to maxi... | computer science |
6,481 | Online Multiclass Boosting | stat.ML | Recent work has extended the theoretical analysis of boosting algorithms to
multiclass problems and to online settings. However, the multiclass extension
is in the batch setting and the online extensions only consider binary
classification. We fill this gap in the literature by defining, and justifying,
a weak learning... | computer science |
6,482 | Causal Discovery Using Proxy Variables | stat.ML | Discovering causal relations is fundamental to reasoning and intelligence. In
particular, observational causal discovery algorithms estimate the cause-effect
relation between two random entities $X$ and $Y$, given $n$ samples from
$P(X,Y)$.
In this paper, we develop a framework to estimate the cause-effect relation
b... | computer science |
6,483 | Neural Decision Trees | stat.ML | In this paper we propose a synergistic melting of neural networks and
decision trees (DT) we call neural decision trees (NDT). NDT is an architecture
a la decision tree where each splitting node is an independent multilayer
perceptron allowing oblique decision functions or arbritrary nonlinear decision
function if more... | computer science |
6,484 | Sequence Modeling via Segmentations | stat.ML | Segmental structure is a common pattern in many types of sequences such as
phrases in human languages. In this paper, we present a probabilistic model for
sequences via their segmentations. The probability of a segmented sequence is
calculated as the product of the probabilities of all its segments, where each
segment ... | computer science |
6,485 | Learning Rates for Kernel-Based Expectile Regression | stat.ML | Conditional expectiles are becoming an increasingly important tool in finance
as well as in other areas of applications. We analyse a support vector machine
type approach for estimating conditional expectiles and establish learning
rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF
kernels are u... | computer science |
6,486 | Changing Model Behavior at Test-Time Using Reinforcement Learning | stat.ML | Machine learning models are often used at test-time subject to constraints
and trade-offs not present at training-time. For example, a computer vision
model operating on an embedded device may need to perform real-time inference,
or a translation model operating on a cell phone may wish to bound its average
compute tim... | computer science |
6,487 | Activation Ensembles for Deep Neural Networks | stat.ML | Many activation functions have been proposed in the past, but selecting an
adequate one requires trial and error. We propose a new methodology of
designing activation functions within a neural network at each layer. We call
this technique an "activation ensemble" because it allows the use of multiple
activation functio... | computer science |
6,488 | Rank-to-engage: New Listwise Approaches to Maximize Engagement | stat.ML | For many internet businesses, presenting a given list of items in an order
that maximizes a certain metric of interest (e.g., click-through-rate, average
engagement time etc.) is crucial. We approach the aforementioned task from a
learning-to-rank perspective which reveals a new problem setup. In traditional
learning-t... | computer science |
6,489 | Efficient Learning of Mixed Membership Models | cs.LG | We present an efficient algorithm for learning mixed membership models when
the number of variables $p$ is much larger than the number of hidden components
$k$. This algorithm reduces the computational complexity of state-of-the-art
tensor methods, which require decomposing an $O\left(p^3\right)$ tensor, to
factorizing... | computer science |
6,490 | Generative Adversarial Active Learning | cs.LG | We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the
resulting algorithm adaptively synthesizes training instances for querying to
increase learning speed. We generate queries according to the uncertainty
principle, but our... | computer science |
6,491 | Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$
Regret | cs.LG | We present an efficient second-order algorithm with
$\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass
problem. The regret bound holds simultaneously with respect to a family of loss
functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm
of the competitor. The family of... | computer science |
6,492 | Ratio Utility and Cost Analysis for Privacy Preserving Subspace
Projection | stat.ML | With a rapidly increasing number of devices connected to the internet, big
data has been applied to various domains of human life. Nevertheless, it has
also opened new venues for breaching users' privacy. Hence it is highly
required to develop techniques that enable data owners to privatize their data
while keeping it ... | computer science |
6,493 | Kiefer Wolfowitz Algorithm is Asymptotically Optimal for a Class of
Non-Stationary Bandit Problems | stat.ML | We consider the problem of designing an allocation rule or an "online
learning algorithm" for a class of bandit problems in which the set of control
actions available at each time $s$ is a convex, compact subset of
$\mathbb{R}^d$. Upon choosing an action $x$ at time $s$, the algorithm obtains
a noisy value of the unkno... | computer science |
6,494 | Support vector machine and its bias correction in high-dimension,
low-sample-size settings | stat.ML | In this paper, we consider asymptotic properties of the support vector
machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that
the hard-margin linear SVM holds a consistency property in which
misclassification rates tend to zero as the dimension goes to infinity under
certain severe conditions. ... | computer science |
6,495 | Variational Inference using Implicit Distributions | stat.ML | Generative adversarial networks (GANs) have given us a great tool to fit
implicit generative models to data. Implicit distributions are ones we can
sample from easily, and take derivatives of samples with respect to model
parameters. These models are highly expressive and we argue they can prove just
as useful for vari... | computer science |
6,496 | Uniform Deviation Bounds for Unbounded Loss Functions like k-Means | stat.ML | Uniform deviation bounds limit the difference between a model's expected loss
and its loss on an empirical sample uniformly for all models in a learning
problem. As such, they are a critical component to empirical risk minimization.
In this paper, we provide a novel framework to obtain uniform deviation bounds
for loss... | computer science |
6,497 | Approximate Inference with Amortised MCMC | stat.ML | We propose a novel approximate inference algorithm that approximates a target
distribution by amortising the dynamics of a user-selected MCMC sampler. The
idea is to initialise MCMC using samples from an approximation network, apply
the MCMC operator to improve these samples, and finally use the samples to
update the a... | computer science |
6,498 | Dynamic Word Embeddings | stat.ML | We present a probabilistic language model for time-stamped text data which
tracks the semantic evolution of individual words over time. The model
represents words and contexts by latent trajectories in an embedding space. At
each moment in time, the embedding vectors are inferred from a probabilistic
version of word2ve... | computer science |
6,499 | Learning Hierarchical Features from Generative Models | cs.LG | Deep neural networks have been shown to be very successful at learning
feature hierarchies in supervised learning tasks. Generative models, on the
other hand, have benefited less from hierarchical models with multiple layers
of latent variables. In this paper, we prove that hierarchical latent variable
models do not ta... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.