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7,600 | Fast Global Convergence via Landscape of Empirical Loss | stat.ML | While optimizing convex objective (loss) functions has been a powerhouse for
machine learning for at least two decades, non-convex loss functions have
attracted fast growing interests recently, due to many desirable properties
such as superior robustness and classification accuracy, compared with their
convex counterpa... | computer science |
7,601 | Barista - a Graphical Tool for Designing and Training Deep Neural
Networks | cs.LG | In recent years, the importance of deep learning has significantly increased
in pattern recognition, computer vision, and artificial intelligence research,
as well as in industry. However, despite the existence of multiple deep
learning frameworks, there is a lack of comprehensible and easy-to-use
high-level tools for ... | computer science |
7,602 | Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions | stat.ML | Learning algorithms that aggregate predictions from an ensemble of diverse
base classifiers consistently outperform individual methods. Many of these
strategies have been developed in a supervised setting, where the accuracy of
each base classifier can be empirically measured and this information is
incorporated in the... | computer science |
7,603 | Neural Relational Inference for Interacting Systems | stat.ML | Interacting systems are prevalent in nature, from dynamical systems in
physics to complex societal dynamics. The interplay of components can give rise
to complex behavior, which can often be explained using a simple model of the
system's constituent parts. In this work, we introduce the neural relational
inference (NRI... | computer science |
7,604 | Attention-based Deep Multiple Instance Learning | cs.LG | Multiple instance learning (MIL) is a variation of supervised learning where
a single class label is assigned to a bag of instances. In this paper, we state
the MIL problem as learning the Bernoulli distribution of the bag label where
the bag label probability is fully parameterized by neural networks.
Furthermore, we ... | computer science |
7,605 | Online Variance Reduction for Stochastic Optimization | stat.ML | Modern stochastic optimization methods often rely on uniform sampling which
is agnostic to the underlying characteristics of the data. This might degrade
the convergence by yielding estimates that suffer from a high variance. A
possible remedy is to employ non-uniform importance sampling techniques, which
take the stru... | computer science |
7,606 | Substation Signal Matching with a Bagged Token Classifier | stat.ML | Currently, engineers at substation service providers match customer data with
the corresponding internally used signal names manually. This paper proposes a
machine learning method to automate this process based on substation signal
mapping data from a repository of executed projects. To this end, a bagged
token classi... | computer science |
7,607 | An Improved Bayesian Framework for Quadrature of Constrained Integrands | cs.LG | Quadrature is the problem of estimating intractable integrals, a problem that
arises in many Bayesian machine learning settings. We present an improved
Bayesian framework for estimating intractable integrals of specific kinds of
constrained integrands. We derive the necessary approximation scheme for a
specific and esp... | computer science |
7,608 | Learning Confidence for Out-of-Distribution Detection in Neural Networks | stat.ML | Modern neural networks are very powerful predictive models, but they are
often incapable of recognizing when their predictions may be wrong. Closely
related to this is the task of out-of-distribution detection, where a network
must determine whether or not an input is outside of the set on which it is
expected to safel... | computer science |
7,609 | SimplE Embedding for Link Prediction in Knowledge Graphs | stat.ML | The aim of knowledge graphs is to gather knowledge about the world and
provide a structured representation of this knowledge. Current knowledge graphs
are far from complete. To address the incompleteness of the knowledge graphs,
link prediction approaches have been developed which make probabilistic
predictions about n... | computer science |
7,610 | GILBO: One Metric to Measure Them All | stat.ML | We propose a simple, tractable lower bound on the mutual information
contained in the joint generative density of any latent variable generative
model: the GILBO (Generative Information Lower BOund). It offers a data
independent measure of the complexity of the learned latent variable
description, giving the log of the... | computer science |
7,611 | Uncertainty Estimation via Stochastic Batch Normalization | stat.ML | In this work, we investigate Batch Normalization technique and propose its
probabilistic interpretation. We propose a probabilistic model and show that
Batch Normalization maximazes the lower bound of its marginalized
log-likelihood. Then, according to the new probabilistic model, we design an
algorithm which acts cons... | computer science |
7,612 | Compressive Sensing with Low Precision Data Representation: Radio
Astronomy and Beyond | stat.ML | Modern scientific instruments produce vast amounts of data, which can
overwhelm the processing ability of computer systems. Lossy compression of data
is an intriguing solution but comes with its own dangers, such as potential
signal loss, and the need for careful parameter optimization. In this work, we
focus on a sett... | computer science |
7,613 | Prophit: Causal inverse classification for multiple continuously valued
treatment policies | cs.LG | Inverse classification uses an induced classifier as a queryable oracle to
guide test instances towards a preferred posterior class label. The result
produced from the process is a set of instance-specific feature perturbations,
or recommendations, that optimally improve the probability of the class label.
In this work... | computer science |
7,614 | DVAE++: Discrete Variational Autoencoders with Overlapping
Transformations | cs.LG | Training of discrete latent variable models remains challenging because
passing gradient information through discrete units is difficult. We propose a
new class of smoothing transformations based on a mixture of two overlapping
distributions, and show that the proposed transformation can be used for
training binary lat... | computer science |
7,615 | Edge Attention-based Multi-Relational Graph Convolutional Networks | stat.ML | Graph convolutional network (GCN) is generalization of convolutional neural
network (CNN) to work with arbitrarily structured graphs. A binary adjacency
matrix is commonly used in training a GCN. Recently, the attention mechanism
allows the network to learn a dynamic and adaptive aggregation of the
neighborhood. We pro... | computer science |
7,616 | D2KE: From Distance to Kernel and Embedding | stat.ML | For many machine learning problem settings, particularly with structured
inputs such as sequences or sets of objects, a distance measure between inputs
can be specified more naturally than a feature representation. However, most
standard machine models are designed for inputs with a vector feature
representation. In th... | computer science |
7,617 | Robust Continuous Co-Clustering | cs.LG | Clustering consists of grouping together samples giving their similar
properties. The problem of modeling simultaneously groups of samples and
features is known as Co-Clustering. This paper introduces ROCCO - a Robust
Continuous Co-Clustering algorithm. ROCCO is a scalable, hyperparameter-free,
easy and ready to use al... | computer science |
7,618 | L4: Practical loss-based stepsize adaptation for deep learning | cs.LG | We propose a stepsize adaptation scheme for stochastic gradient descent. It
operates directly with the loss function and rescales the gradient in order to
make fixed predicted progress on the loss. We demonstrate its capabilities by
strongly improving the performance of Adam and Momentum optimizers. The
enhanced optimi... | computer science |
7,619 | On the Blindspots of Convolutional Networks | stat.ML | Deep convolutional network has been the state-of-the-art approach for a wide
variety of tasks over the last few years. Its successes have, in many cases,
turned it into the default model in quite a few domains. In this work we will
demonstrate that convolutional networks have limitations that may, in some
cases, hinder... | computer science |
7,620 | Online Learning for Non-Stationary A/B Tests | cs.LG | The rollout of new versions of a feature in modern applications is a manual
multi-stage process, as the feature is released to ever larger groups of users,
while its performance is carefully monitored. This kind of A/B testing is
ubiquitous, but suboptimal, as the monitoring requires heavy human
intervention, is not gu... | computer science |
7,621 | Multimodal Generative Models for Scalable Weakly-Supervised Learning | cs.LG | Multiple modalities often co-occur when describing natural phenomena.
Learning a joint representation of these modalities should yield deeper and
more useful representations. Previous work have proposed generative models to
handle multi-modal input. However, these models either do not learn a joint
distribution or requ... | computer science |
7,622 | The Role of Information Complexity and Randomization in Representation
Learning | stat.ML | A grand challenge in representation learning is to learn the different
explanatory factors of variation behind the high dimen- sional data. Encoder
models are often determined to optimize performance on training data when the
real objective is to generalize well to unseen data. Although there is enough
numerical eviden... | computer science |
7,623 | Active Feature Acquisition with Supervised Matrix Completion | cs.LG | Feature missing is a serious problem in many applications, which may lead to
low quality of training data and further significantly degrade the learning
performance. While feature acquisition usually involves special devices or
complex process, it is expensive to acquire all feature values for the whole
dataset. On the... | computer science |
7,624 | Shamap: Shape-based Manifold Learning | cs.LG | For manifold learning, it is assumed that high-dimensional sample/data points
are on an embedded low-dimensional manifold. Usually, distances among samples
are computed to represent the underlying data structure, for a specified
distance measure such as the Euclidean distance or geodesic distance. For
manifold learning... | computer science |
7,625 | Reducing over-clustering via the powered Chinese restaurant process | cs.LG | Dirichlet process mixture (DPM) models tend to produce many small clusters
regardless of whether they are needed to accurately characterize the data -
this is particularly true for large data sets. However, interpretability,
parsimony, data storage and communication costs all are hampered by having
overly many clusters... | computer science |
7,626 | Cost-Effective Training of Deep CNNs with Active Model Adaptation | cs.LG | Deep convolutional neural networks have achieved great success in various
applications. However, training an effective DNN model for a specific task is
rather challenging because it requires a prior knowledge or experience to
design the network architecture, repeated trial-and-error process to tune the
parameters, and ... | computer science |
7,627 | Selecting the Best in GANs Family: a Post Selection Inference Framework | cs.LG | "Which Generative Adversarial Networks (GANs) generates the most plausible
images?" has been a frequently asked question among researchers. To address
this problem, we first propose an \emph{incomplete} U-statistics estimate of
maximum mean discrepancy $\mathrm{MMD}_{inc}$ to measure the distribution
discrepancy betwee... | computer science |
7,628 | On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo | stat.ML | We provide convergence guarantees in Wasserstein distance for a variety of
variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion
and control-variate underdamped Langevin diffusion. We analyze these methods
under a uniform set of assumptions on the log-posterior distribution, assuming
it to be smo... | computer science |
7,629 | Bandit Learning with Positive Externalities | cs.LG | Many platforms are characterized by the fact that future user arrivals are
likely to have preferences similar to users who were satisfied in the past. In
other words, arrivals exhibit {\em positive externalities}. We study multiarmed
bandit (MAB) problems with positive externalities. Our model has a finite
number of ar... | computer science |
7,630 | Fair Clustering Through Fairlets | cs.LG | We study the question of fair clustering under the {\em disparate impact}
doctrine, where each protected class must have approximately equal
representation in every cluster. We formulate the fair clustering problem under
both the $k$-center and the $k$-median objectives, and show that even with two
protected classes th... | computer science |
7,631 | Horovod: fast and easy distributed deep learning in TensorFlow | cs.LG | Training modern deep learning models requires large amounts of computation,
often provided by GPUs. Scaling computation from one GPU to many can enable
much faster training and research progress but entails two complications.
First, the training library must support inter-GPU communication. Depending on
the particular ... | computer science |
7,632 | Distributed Stochastic Optimization via Adaptive Stochastic Gradient
Descent | stat.ML | Stochastic convex optimization algorithms are the most popular way to train
machine learning models on large-scale data. Scaling up the training process of
these models is crucial in many applications, but the most popular algorithm,
Stochastic Gradient Descent (SGD), is a serial algorithm that is surprisingly
hard to ... | computer science |
7,633 | Learning Latent Features with Pairwise Penalties in Matrix Completion | stat.ML | Low-rank matrix completion (MC) has achieved great success in many real-world
data applications. A latent feature model formulation is usually employed and,
to improve prediction performance, the similarities between latent variables
can be exploited by pairwise learning, e.g., the graph regularized matrix
factorizatio... | computer science |
7,634 | Auto-Encoding Total Correlation Explanation | cs.LG | Advances in unsupervised learning enable reconstruction and generation of
samples from complex distributions, but this success is marred by the
inscrutability of the representations learned. We propose an
information-theoretic approach to characterizing disentanglement and dependence
in representation learning using mu... | computer science |
7,635 | Train on Validation: Squeezing the Data Lemon | stat.ML | Model selection on validation data is an essential step in machine learning.
While the mixing of data between training and validation is considered taboo,
practitioners often violate it to increase performance. Here, we offer a
simple, practical method for using the validation set for training, which
allows for a conti... | computer science |
7,636 | Pattern Localization in Time Series through Signal-To-Model Alignment in
Latent Space | cs.LG | In this paper, we study the problem of locating a predefined sequence of
patterns in a time series. In particular, the studied scenario assumes a
theoretical model is available that contains the expected locations of the
patterns. This problem is found in several contexts, and it is commonly solved
by first synthesizin... | computer science |
7,637 | Tensor-based Nonlinear Classifier for High-Order Data Analysis | cs.LG | In this paper we propose a tensor-based nonlinear model for high-order data
classification. The advantages of the proposed scheme are that (i) it
significantly reduces the number of weight parameters, and hence of required
training samples, and (ii) it retains the spatial structure of the input
samples. The proposed mo... | computer science |
7,638 | Disentangling by Factorising | stat.ML | We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We
propose FactorVAE, a method that disentangles by encouraging the distribution
of representations to be factorial and hence independent across the dimensions.
We show tha... | computer science |
7,639 | Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and
Theoretical Analysis | cs.LG | Distance metric learning (DML), which learns a distance metric from labeled
"similar" and "dissimilar" data pairs, is widely utilized. Recently, several
works investigate orthogonality-promoting regularization (OPR), which
encourages the projection vectors in DML to be close to being orthogonal, to
achieve three effect... | computer science |
7,640 | Policy Evaluation and Optimization with Continuous Treatments | stat.ML | We study the problem of policy evaluation and learning from batched
contextual bandit data when treatments are continuous, going beyond previous
work on discrete treatments. Previous work for discrete treatment/action spaces
focuses on inverse probability weighting (IPW) and doubly robust (DR) methods
that use a reject... | computer science |
7,641 | CapsuleGAN: Generative Adversarial Capsule Network | stat.ML | We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional
neural networks (CNNs) as discriminators within the generative adversarial
network (GAN) setting, while modeling image data. We provide guidelines for
designing CapsNet... | computer science |
7,642 | Efficient GAN-Based Anomaly Detection | cs.LG | Generative adversarial networks (GANs) are able to model the complex
highdimensional distributions of real-world data, which suggests they could be
effective for anomaly detection. However, few works have explored the use of
GANs for the anomaly detection task. We leverage recently developed GAN models
for anomaly dete... | computer science |
7,643 | Exact and Robust Conformal Inference Methods for Predictive Machine
Learning With Dependent Data | stat.ML | We extend conformal inference to general settings that allow for time series
data. Our proposal is developed as a randomization method and accounts for
potential serial dependence by including block structures in the permutation
scheme. As a result, the proposed method retains the exact, model-free validity
when the da... | computer science |
7,644 | Learning Adversarially Fair and Transferable Representations | cs.LG | In this work, we advocate for representation learning as the key to
mitigating unfair prediction outcomes downstream. We envision a scenario where
learned representations may be handed off to other entities with unknown
objectives. We propose and explore adversarial representation learning as a
natural method of ensuri... | computer science |
7,645 | Efficient Gaussian Process Classification Using Polya-Gamma Data
Augmentation | stat.ML | We propose an efficient stochastic variational approach to GP classification
building on Polya- Gamma data augmentation and inducing points, which is based
on closed-form updates of natural gradients. We evaluate the algorithm on
real-world datasets containing up to 11 million data points and demonstrate
that it is up ... | computer science |
7,646 | Training Big Random Forests with Little Resources | cs.LG | Without access to large compute clusters, building random forests on large
datasets is still a challenging problem. This is, in particular, the case if
fully-grown trees are desired. We propose a simple yet effective framework that
allows to efficiently construct ensembles of huge trees for hundreds of
millions or even... | computer science |
7,647 | Towards Ultra-High Performance and Energy Efficiency of Deep Learning
Systems: An Algorithm-Hardware Co-Optimization Framework | cs.LG | Hardware accelerations of deep learning systems have been extensively
investigated in industry and academia. The aim of this paper is to achieve
ultra-high energy efficiency and performance for hardware implementations of
deep neural networks (DNNs). An algorithm-hardware co-optimization framework is
developed, which i... | computer science |
7,648 | RadialGAN: Leveraging multiple datasets to improve target-specific
predictive models using Generative Adversarial Networks | cs.LG | Training complex machine learning models for prediction often requires a
large amount of data that is not always readily available. Leveraging these
external datasets from related but different sources is therefore an important
task if good predictive models are to be built for deployment in settings where
data can be ... | computer science |
7,649 | Music Genre Classification using Masked Conditional Neural Networks | cs.LG | The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural
Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The
CLNN captures the conditional temporal influence between the frames in a window
and the mask in the MCLNN enforces a systematic sparseness that follows a
filterbank-lik... | computer science |
7,650 | A Generative Modeling Approach to Limited Channel ECG Classification | stat.ML | Processing temporal sequences is central to a variety of applications in
health care, and in particular multi-channel Electrocardiogram (ECG) is a
highly prevalent diagnostic modality that relies on robust sequence modeling.
While Recurrent Neural Networks (RNNs) have led to significant advances in
automated diagnosis ... | computer science |
7,651 | Local Geometry of One-Hidden-Layer Neural Networks for Logistic
Regression | stat.ML | We study the local geometry of a one-hidden-layer fully-connected neural
network where the training samples are generated from a multi-neuron logistic
regression model. We prove that under Gaussian input, the empirical risk
function employing quadratic loss exhibits strong convexity and smoothness
uniformly in a local ... | computer science |
7,652 | Heron Inference for Bayesian Graphical Models | cs.LG | Bayesian graphical models have been shown to be a powerful tool for
discovering uncertainty and causal structure from real-world data in many
application fields. Current inference methods primarily follow different kinds
of trade-offs between computational complexity and predictive accuracy. At one
end of the spectrum,... | computer science |
7,653 | Are Generative Classifiers More Robust to Adversarial Attacks? | cs.LG | There is a rising interest in studying the robustness of deep neural network
classifiers against adversaries, with both advanced attack and defence
techniques being actively developed. However, most recent work focuses on
discriminative classifiers which only models the conditional distribution of
the labels given the ... | computer science |
7,654 | Finding Influential Training Samples for Gradient Boosted Decision Trees | cs.LG | We address the problem of finding influential training samples for a
particular case of tree ensemble-based models, e.g., Random Forest (RF) or
Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this
problem is studying how the model's predictions change upon leave-one-out
retraining, leaving out each... | computer science |
7,655 | Degeneration in VAE: in the Light of Fisher Information Loss | stat.ML | Variational Autoencoder (VAE) is one of the most popular generative models,
and enormous advances have been explored in recent years. Due to the increasing
complexity of the raw data and the model architecture, deep networks are needed
in VAE models while few works discuss their impacts. According to our
observation, V... | computer science |
7,656 | Interpretable VAEs for nonlinear group factor analysis | cs.LG | Deep generative models have recently yielded encouraging results in producing
subjectively realistic samples of complex data. Far less attention has been
paid to making these generative models interpretable. In many scenarios,
ranging from scientific applications to finance, the observed variables have a
natural groupi... | computer science |
7,657 | Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes | stat.ML | Scientific and engineering processes produce massive high-dimensional data
sets that are generated as highly non-linear transformations of an initial
state and few process parameters. Mapping such data to a low-dimensional
manifold can facilitate better understanding of the underlying process, and
ultimately their opti... | computer science |
7,658 | Distribution Matching in Variational Inference | stat.ML | The difficulties in matching the latent posterior to the prior, balancing
powerful posteriors with computational efficiency, and the reduced flexibility
of data likelihoods are the biggest challenges in the advancement of
Variational Autoencoders. We show that these issues arise due to struggles in
marginal divergence ... | computer science |
7,659 | LSALSA: efficient sparse coding in single and multiple dictionary
settings | cs.LG | We propose an efficient sparse coding (SC) framework for obtaining sparse
representation of data. The proposed framework is very general and applies to
both the single dictionary setting, where each data point is represented as a
sparse combination of the columns of one dictionary matrix, as well as the
multiple dictio... | computer science |
7,660 | Estimator of Prediction Error Based on Approximate Message Passing for
Penalized Linear Regression | stat.ML | We propose an estimator of prediction error using an approximate message
passing (AMP) algorithm that can be applied to a broad range of sparse
penalties. Following Stein's lemma, the estimator of the generalized degrees of
freedom, which is a key quantity for the construction of the estimator of the
prediction error, ... | computer science |
7,661 | DeepThin: A Self-Compressing Library for Deep Neural Networks | cs.LG | As the industry deploys increasingly large and complex neural networks to
mobile devices, more pressure is put on the memory and compute resources of
those devices. Deep compression, or compression of deep neural network weight
matrices, is a technique to stretch resources for such scenarios. Existing
compression metho... | computer science |
7,662 | High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning
Framework for Network-Scale Traffic Learning and Forecasting | cs.LG | Traffic forecasting is a challenging task, due to the complicated spatial
dependencies on roadway networks and the time-varying traffic patterns. To
address this challenge, we learn the traffic network as a graph and propose a
novel deep learning framework, High-Order Graph Convolutional Long Short-Term
Memory Neural N... | computer science |
7,663 | Learning to Abstain via Curve Optimization | stat.ML | In practical applications of machine learning, it is often desirable to
identify and abstain on examples where the a model's predictions are likely to
be incorrect. We consider the problem of selecting a budget-constrained subset
of test examples to abstain on, with the goal of maximizing performance on the
remaining e... | computer science |
7,664 | High-Dimensional Bayesian Optimization via Additive Models with
Overlapping Groups | cs.LG | Bayesian optimization (BO) is a popular technique for sequential black-box
function optimization, with applications including parameter tuning, robotics,
environmental monitoring, and more. One of the most important challenges in BO
is the development of algorithms that scale to high dimensions, which remains a
key ope... | computer science |
7,665 | How to Tackle an Extremely Hard Learning Problem: Learning Causal
Structures from Non-Experimental Data without the Faithfulness Assumption or
the Like | stat.ML | Most methods for learning causal structures from non-experimental data rely
on some assumptions of simplicity, the most famous of which is known as the
Faithfulness condition. Without assuming such conditions to begin with, we
develop a learning theory for inferring the structure of a causal Bayesian
network, and we us... | computer science |
7,666 | Learning of Optimal Forecast Aggregation in Partial Evidence
Environments | cs.LG | We consider the forecast aggregation problem in repeated settings, where the
forecasts are done on a binary event. At each period multiple experts provide
forecasts about an event. The goal of the aggregator is to aggregate those
forecasts into a subjective accurate forecast. We assume that experts are
Bayesian; namely... | computer science |
7,667 | Out-distribution training confers robustness to deep neural networks | cs.LG | The easiness at which adversarial instances can be generated in deep neural
networks raises some fundamental questions on their functioning and concerns on
their use in critical systems. In this paper, we draw a connection between
over-generalization and adversaries: a possible cause of adversaries lies in
models desig... | computer science |
7,668 | On Estimating Multi-Attribute Choice Preferences using Private Signals
and Matrix Factorization | stat.ML | Revealed preference theory studies the possibility of modeling an agent's
revealed preferences and the construction of a consistent utility function.
However, modeling agent's choices over preference orderings is not always
practical and demands strong assumptions on human rationality and
data-acquisition abilities. Th... | computer science |
7,669 | Adaptive Sampling for Coarse Ranking | cs.LG | We consider the problem of active coarse ranking, where the goal is to sort
items according to their means into clusters of pre-specified sizes, by
adaptively sampling from their reward distributions. This setting is useful in
many social science applications involving human raters and the approximate
rank of every ite... | computer science |
7,670 | Neural Architecture Search with Bayesian Optimisation and Optimal
Transport | cs.LG | Bayesian Optimisation (BO) refers to a class of methods for global
optimisation of a function $f$ which is only accessible via point evaluations.
It is typically used in settings where $f$ is expensive to evaluate. A common
use case for BO in machine learning is model selection, where it is not
possible to analytically... | computer science |
7,671 | AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian
Optimization with Structured Kernel Learning | cs.LG | Clinical prognostic models derived from largescale healthcare data can inform
critical diagnostic and therapeutic decisions. To enable off-theshelf usage of
machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a
system for automating the design of predictive modeling pipelines tailored for
clinical... | computer science |
7,672 | Attack Strength vs. Detectability Dilemma in Adversarial Machine
Learning | stat.ML | As the prevalence and everyday use of machine learning algorithms, along with
our reliance on these algorithms grow dramatically, so do the efforts to attack
and undermine these algorithms with malicious intent, resulting in a growing
interest in adversarial machine learning. A number of approaches have been
developed ... | computer science |
7,673 | Bayesian Incremental Learning for Deep Neural Networks | stat.ML | In industrial machine learning pipelines, data often arrive in parts.
Particularly in the case of deep neural networks, it may be too expensive to
train the model from scratch each time, so one would rather use a previously
learned model and the new data to improve performance. However, deep neural
networks are prone t... | computer science |
7,674 | On the Statistical Challenges of Echo State Networks and Some Potential
Remedies | stat.ML | Echo state networks are powerful recurrent neural networks. However, they are
often unstable and shaky, making the process of finding an good ESN for a
specific dataset quite hard. Obtaining a superb accuracy by using the Echo
State Network is a challenging task. We create, develop and implement a family
of predictably... | computer science |
7,675 | A Study into the similarity in generator and discriminator in GAN
architecture | cs.LG | One popular generative model that has high-quality results is the Generative
Adversarial Networks(GAN). This type of architecture consists of two separate
networks that play against each other. The generator creates an output from the
input noise that is given to it. The discriminator has the task of determining
if the... | computer science |
7,676 | The Many Faces of Exponential Weights in Online Learning | stat.ML | A standard introduction to online learning might place Online Gradient
Descent at its center and then proceed to develop generalizations and
extensions like Online Mirror Descent and second-order methods. Here we explore
the alternative approach of putting exponential weights (EW) first. We show
that many standard meth... | computer science |
7,677 | Information Theoretic Co-Training | cs.LG | This paper introduces an information theoretic co-training objective for
unsupervised learning. We consider the problem of predicting the future. Rather
than predict future sensations (image pixels or sound waves) we predict
"hypotheses" to be confirmed by future sensations. More formally, we assume a
population distri... | computer science |
7,678 | Detecting Learning vs Memorization in Deep Neural Networks using Shared
Structure Validation Sets | stat.ML | The roles played by learning and memorization represent an important topic in
deep learning research. Recent work on this subject has shown that the
optimization behavior of DNNs trained on shuffled labels is qualitatively
different from DNNs trained with real labels. Here, we propose a novel
permutation approach that ... | computer science |
7,679 | Determining the best classifier for predicting the value of a boolean
field on a blood donor database | stat.ML | Motivation: Thanks to digitization, we often have access to large databases,
consisting of various fields of information, ranging from numbers to texts and
even boolean values. Such databases lend themselves especially well to machine
learning, classification and big data analysis tasks. We are able to train
classifier... | computer science |
7,680 | Regional Multi-Armed Bandits | cs.LG | We consider a variant of the classic multi-armed bandit problem where the
expected reward of each arm is a function of an unknown parameter. The arms are
divided into different groups, each of which has a common parameter. Therefore,
when the player selects an arm at each time slot, information of other arms in
the sam... | computer science |
7,681 | Iterate averaging as regularization for stochastic gradient descent | cs.LG | We propose and analyze a variant of the classic Polyak-Ruppert averaging
scheme, broadly used in stochastic gradient methods. Rather than a uniform
average of the iterates, we consider a weighted average, with weights decaying
in a geometric fashion. In the context of linear least squares regression, we
show that this ... | computer science |
7,682 | Learning Topic Models by Neighborhood Aggregation | stat.ML | Topic models are one of the most frequently used models in machine learning
due to its high interpretability and modular structure. However extending the
model to include supervisory signal, incorporate pre-trained word embedding
vectors and add nonlinear output function to the model is not an easy task
because one has... | computer science |
7,683 | Characterizing Implicit Bias in Terms of Optimization Geometry | stat.ML | We study the bias of generic optimization methods, including Mirror Descent,
Natural Gradient Descent and Steepest Descent with respect to different
potentials and norms, when optimizing underdetermined linear regression or
separable linear classification problems. We ask the question of whether the
global minimum (amo... | computer science |
7,684 | Overcoming Catastrophic Forgetting in Convolutional Neural Networks by
Selective Network Augmentation | cs.LG | Lifelong learning aims to develop machine learning systems that can learn new
tasks while preserving the performance on previous tasks. This approach can be
applied, for example, to prevent accident on autonomous vehicles by applying
the knowledge learned on previous situations. In this paper we present a method
to ove... | computer science |
7,685 | On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems | stat.ML | We study the non-stationary stochastic multiarmed bandit (MAB) problem and
propose two generic algorithms, namely, the limited memory deterministic
sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window
Upper Confidence Bound# (SW-UCB#). We rigorously analyze these algorithms in
abruptly-changing a... | computer science |
7,686 | Solving Linear Inverse Problems Using GAN Priors: An Algorithm with
Provable Guarantees | stat.ML | In recent works, both sparsity-based methods as well as learning-based
methods have proven to be successful in solving several challenging linear
inverse problems. However, sparsity priors for natural signals and images
suffer from poor discriminative capability, while learning-based methods seldom
provide concrete the... | computer science |
7,687 | The Weighted Kendall and High-order Kernels for Permutations | stat.ML | We propose new positive definite kernels for permutations. First we introduce
a weighted version of the Kendall kernel, which allows to weight unequally the
contributions of different item pairs in the permutations depending on their
ranks. Like the Kendall kernel, we show that the weighted version is invariant
to rela... | computer science |
7,688 | Empirical Risk Minimization under Fairness Constraints | stat.ML | We address the problem of algorithmic fairness: ensuring that sensitive
variables do not unfairly influence the outcome of a classifier. We present an
approach based on empirical risk minimization, which incorporates a fairness
constraint into the learning problem. It encourages the conditional risk of the
learned clas... | computer science |
7,689 | Learning Latent Permutations with Gumbel-Sinkhorn Networks | stat.ML | Permutations and matchings are core building blocks in a variety of latent
variable models, as they allow us to align, canonicalize, and sort data.
Learning in such models is difficult, however, because exact marginalization
over these combinatorial objects is intractable. In response, this paper
introduces a collectio... | computer science |
7,690 | Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction | cs.LG | Taxi demand prediction is an important building block to enabling intelligent
transportation systems in a smart city. An accurate prediction model can help
the city pre-allocate resources to meet travel demand and to reduce empty taxis
on streets which waste energy and worsen the traffic congestion. With the
increasing... | computer science |
7,691 | Diffusion Maps meet Nyström | stat.ML | Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. T... | computer science |
7,692 | Is Generator Conditioning Causally Related to GAN Performance? | stat.ML | Recent work (Pennington et al, 2017) suggests that controlling the entire
distribution of Jacobian singular values is an important design consideration
in deep learning. Motivated by this, we study the distribution of singular
values of the Jacobian of the generator in Generative Adversarial Networks
(GANs). We find th... | computer science |
7,693 | A Walk with SGD | stat.ML | Exploring why stochastic gradient descent (SGD) based optimization methods
train deep neural networks (DNNs) that generalize well has become an active
area of research. Towards this end, we empirically study the dynamics of SGD
when training over-parametrized DNNs. Specifically we study the DNN loss
surface along the t... | computer science |
7,694 | Extremely Fast Decision Tree | cs.LG | We introduce a novel incremental decision tree learning algorithm, Hoeffding
Anytime Tree, that is statistically more efficient than the current
state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of
Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification
to the MOA implementat... | computer science |
7,695 | Product Kernel Interpolation for Scalable Gaussian Processes | cs.LG | Recent work shows that inference for Gaussian processes can be performed
efficiently using iterative methods that rely only on matrix-vector
multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these
techniques by deriving approximate kernels with very fast MVMs. Unfortunately,
such strategies suffer ... | computer science |
7,696 | Dynamic Bidding for Advance Commitments in Truckload Brokerage Markets | stat.ML | Truckload brokerages, a $100 billion/year industry in the U.S., plays the
critical role of matching shippers with carriers, often to move loads several
days into the future. Brokerages not only have to find companies that will
agree to move a load, the brokerage often has to find a price that both the
shipper and carri... | computer science |
7,697 | Functional Gradient Boosting based on Residual Network Perception | stat.ML | Residual Networks (ResNets) have become state-of-the-art models in deep
learning and several theoretical studies have been devoted to understanding why
ResNet works so well. One attractive viewpoint on ResNet is that it is
optimizing the risk in a functional space by combining an ensemble of effective
features. In this... | computer science |
7,698 | Time Series Analysis via Matrix Estimation | cs.LG | We consider the task of interpolating and forecasting a time series in the
presence of noise and missing data. As the main contribution of this work, we
introduce an algorithm that transforms the observed time series into a matrix,
utilizes singular value thresholding to simultaneously recover missing values
and de-noi... | computer science |
7,699 | Active Learning with Logged Data | cs.LG | We consider active learning with logged data, where labeled examples are
drawn conditioned on a predetermined logging policy, and the goal is to learn a
classifier on the entire population, not just conditioned on the logging
policy.
Prior work addresses this problem either when only logged data is available,
or pure... | computer science |
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