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6,700 | Masked Autoregressive Flow for Density Estimation | stat.ML | Autoregressive models are among the best performing neural density
estimators. We describe an approach for increasing the flexibility of an
autoregressive model, based on modelling the random numbers that the model uses
internally when generating data. By constructing a stack of autoregressive
models, each modelling th... | computer science |
6,701 | EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD | cs.LG | We present a generic framework for trading off fidelity and cost in computing
stochastic gradients when the costs of acquiring stochastic gradients of
different quality are not known a priori. We consider a mini-batch oracle that
distributes a limited query budget over a number of stochastic gradients and
aggregates th... | computer science |
6,702 | Gradient Estimators for Implicit Models | stat.ML | Implicit models, which allow for the generation of samples but not for
point-wise evaluation of probabilities, are omnipresent in real-world problems
tackled by machine learning and a hot topic of current research. Some examples
include data simulators that are widely used in engineering and scientific
research, genera... | computer science |
6,703 | Relaxed Wasserstein with Applications to GANs | stat.ML | We propose a novel class of statistical divergences called \textit{Relaxed
Wasserstein} (RW) divergence. RW divergence generalizes Wasserstein distance
and is parametrized by strictly convex, differentiable functions. We establish
for RW several key probabilistic properties, which are critical for the success
of Wasser... | computer science |
6,704 | Two-temperature logistic regression based on the Tsallis divergence | cs.LG | We develop a variant of multiclass logistic regression that achieves three
properties: i) We minimize a non-convex surrogate loss which makes the method
robust to outliers, ii) our method allows transitioning between non-convex and
convex losses by the choice of the parameters, iii) the surrogate loss is Bayes
consiste... | computer science |
6,705 | Forward Thinking: Building Deep Random Forests | stat.ML | The success of deep neural networks has inspired many to wonder whether other
learners could benefit from deep, layered architectures. We present a general
framework called forward thinking for deep learning that generalizes the
architectural flexibility and sophistication of deep neural networks while also
allowing fo... | computer science |
6,706 | Instrument-Armed Bandits | stat.ML | We extend the classic multi-armed bandit (MAB) model to the setting of
noncompliance, where the arm pull is a mere instrument and the treatment
applied may differ from it, which gives rise to the instrument-armed bandit
(IAB) problem. The IAB setting is relevant whenever the experimental units are
human since free will... | computer science |
6,707 | Nice latent variable models have log-rank | cs.LG | Matrices of low rank are pervasive in big data, appearing in recommender
systems, movie preferences, topic models, medical records, and genomics. While
there is a vast literature on how to exploit low rank structure in these
datasets, there is less attention on explaining why the low rank structure
appears in the first... | computer science |
6,708 | Annealed Generative Adversarial Networks | stat.ML | We introduce a novel framework for adversarial training where the target
distribution is annealed between the uniform distribution and the data
distribution. We posited a conjecture that learning under continuous annealing
in the nonparametric regime is stable irrespective of the divergence measures
in the objective fu... | computer science |
6,709 | Learning from Complementary Labels | stat.ML | Collecting labeled data is costly and thus a critical bottleneck in
real-world classification tasks. To mitigate this problem, we propose a novel
setting, namely learning from complementary labels for multi-class
classification. A complementary label specifies a class that a pattern does not
belong to. Collecting compl... | computer science |
6,710 | On the diffusion approximation of nonconvex stochastic gradient descent | stat.ML | We study the Stochastic Gradient Descent (SGD) method in nonconvex
optimization problems from the point of view of approximating diffusion
processes. We prove rigorously that the diffusion process can approximate the
SGD algorithm weakly using the weak form of master equation for probability
evolution. In the small ste... | computer science |
6,711 | Multi-output Polynomial Networks and Factorization Machines | stat.ML | Factorization machines and polynomial networks are supervised polynomial
models based on an efficient low-rank decomposition. We extend these models to
the multi-output setting, i.e., for learning vector-valued functions, with
application to multi-class or multi-task problems. We cast this as the problem
of learning a ... | computer science |
6,712 | A Linear-Time Kernel Goodness-of-Fit Test | stat.ML | We propose a novel adaptive test of goodness-of-fit, with computational cost
linear in the number of samples. We learn the test features that best indicate
the differences between observed samples and a reference model, by minimizing
the false negative rate. These features are constructed via Stein's method,
meaning th... | computer science |
6,713 | Dissecting Adam: The Sign, Magnitude and Variance of Stochastic
Gradients | cs.LG | The ADAM optimizer is exceedingly popular in the deep learning community.
Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a
combination of two aspects: for each weight, the update direction is determined
by the sign of stochastic gradients, whereas the update magnitude is determined
by an esti... | computer science |
6,714 | Parallel Stochastic Gradient Descent with Sound Combiners | cs.LG | Stochastic gradient descent (SGD) is a well known method for regression and
classification tasks. However, it is an inherently sequential algorithm at each
step, the processing of the current example depends on the parameters learned
from the previous examples. Prior approaches to parallelizing linear learners
using SG... | computer science |
6,715 | Wasserstein Learning of Deep Generative Point Process Models | cs.LG | Point processes are becoming very popular in modeling asynchronous sequential
data due to their sound mathematical foundation and strength in modeling a
variety of real-world phenomena. Currently, they are often characterized via
intensity function which limits model's expressiveness due to unrealistic
assumptions on i... | computer science |
6,716 | Ambiguity set and learning via Bregman and Wasserstein | stat.ML | Construction of ambiguity set in robust optimization relies on the choice of
divergences between probability distributions. In distribution learning,
choosing appropriate probability distributions based on observed data is
critical for approximating the true distribution. To improve the performance of
machine learning ... | computer science |
6,717 | Consistent Multitask Learning with Nonlinear Output Relations | cs.LG | Key to multitask learning is exploiting relationships between different tasks
to improve prediction performance. If the relations are linear, regularization
approaches can be used successfully. However, in practice assuming the tasks to
be linearly related might be restrictive, and allowing for nonlinear structures
is ... | computer science |
6,718 | What does an LSTM look for in classifying heartbeats? | stat.ML | Long short-term memory (LSTM) recurrent neural networks are renowned for
being uninterpretable "black boxes". In the medical domain where LSTMs have
shown promise, this is specifically concerning because it is imperative to
understand the decisions made by machine learning models in such acute
situations. This study em... | computer science |
6,719 | Learning to Succeed while Teaching to Fail: Privacy in Closed Machine
Learning Systems | stat.ML | Security, privacy, and fairness have become critical in the era of data
science and machine learning. More and more we see that achieving universally
secure, private, and fair systems is practically impossible. We have seen for
example how generative adversarial networks can be used to learn about the
expected private ... | computer science |
6,720 | The Marginal Value of Adaptive Gradient Methods in Machine Learning | stat.ML | Adaptive optimization methods, which perform local optimization with a metric
constructed from the history of iterates, are becoming increasingly popular for
training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We
show that for simple overparameterized problems, adaptive methods often find
drasti... | computer science |
6,721 | Ridesourcing Car Detection by Transfer Learning | cs.LG | Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate b... | computer science |
6,722 | Bayesian Pool-based Active Learning With Abstention Feedbacks | stat.ML | We study pool-based active learning with abstention feedbacks, where a
labeler can abstain from labeling a queried example with some unknown
abstention rate. This is an important problem with many useful applications. We
take a Bayesian approach to the problem and develop two new greedy algorithms
that learn both the c... | computer science |
6,723 | Data-driven Random Fourier Features using Stein Effect | cs.LG | Large-scale kernel approximation is an important problem in machine learning
research. Approaches using random Fourier features have become increasingly
popular [Rahimi and Recht, 2007], where kernel approximation is treated as
empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC)
integration [Yang ... | computer science |
6,724 | Towards Interrogating Discriminative Machine Learning Models | cs.LG | It is oftentimes impossible to understand how machine learning models reach a
decision. While recent research has proposed various technical approaches to
provide some clues as to how a learning model makes individual decisions, they
cannot provide users with ability to inspect a learning model as a complete
entity. In... | computer science |
6,725 | Multi-Task Learning for Contextual Bandits | stat.ML | Contextual bandits are a form of multi-armed bandit in which the agent has
access to predictive side information (known as the context) for each arm at
each time step, and have been used to model personalized news recommendation,
ad placement, and other applications. In this work, we propose a multi-task
learning frame... | computer science |
6,726 | Nonparametric Preference Completion | stat.ML | We consider the task of collaborative preference completion: given a pool of
items, a pool of users and a partially observed item-user rating matrix, the
goal is to recover the personalized ranking of each user over all of the items.
Our approach is nonparametric: we assume that each item $i$ and each user $u$
have uno... | computer science |
6,727 | Towards Understanding the Invertibility of Convolutional Neural Networks | stat.ML | Several recent works have empirically observed that Convolutional Neural Nets
(CNNs) are (approximately) invertible. To understand this approximate
invertibility phenomenon and how to leverage it more effectively, we focus on a
theoretical explanation and develop a mathematical model of sparse signal
recovery that is c... | computer science |
6,728 | Bayesian Compression for Deep Learning | stat.ML | Compression and computational efficiency in deep learning have become a
problem of great significance. In this work, we argue that the most principled
and effective way to attack this problem is by adopting a Bayesian point of
view, where through sparsity inducing priors we prune large parts of the
network. We introduc... | computer science |
6,729 | Non-Stationary Spectral Kernels | stat.ML | We propose non-stationary spectral kernels for Gaussian process regression.
We propose to model the spectral density of a non-stationary kernel function as
a mixture of input-dependent Gaussian process frequency density surfaces. We
solve the generalised Fourier transform with such a model, and present a family
of non-... | computer science |
6,730 | Train longer, generalize better: closing the generalization gap in large
batch training of neural networks | stat.ML | Background: Deep learning models are typically trained using stochastic
gradient descent or one of its variants. These methods update the weights using
their gradient, estimated from a small fraction of the training data. It has
been observed that when using large batch sizes there is a persistent
degradation in genera... | computer science |
6,731 | Causal Effect Inference with Deep Latent-Variable Models | stat.ML | Learning individual-level causal effects from observational data, such as
inferring the most effective medication for a specific patient, is a problem of
growing importance for policy makers. The most important aspect of inferring
causal effects from observational data is the handling of confounders, factors
that affec... | computer science |
6,732 | Learning with Average Top-k Loss | stat.ML | In this work, we introduce the {\em average top-$k$} (\atk) loss as a new
aggregate loss for supervised learning, which is the average over the $k$
largest individual losses over a training dataset. We show that the \atk loss
is a natural generalization of the two widely used aggregate losses, namely the
average loss a... | computer science |
6,733 | Multi-Level Variational Autoencoder: Learning Disentangled
Representations from Grouped Observations | cs.LG | We would like to learn a representation of the data which decomposes an
observation into factors of variation which we can independently control.
Specifically, we want to use minimal supervision to learn a latent
representation that reflects the semantics behind a specific grouping of the
data, where within a group the... | computer science |
6,734 | Joint Distribution Optimal Transportation for Domain Adaptation | stat.ML | This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function $f$ in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linea... | computer science |
6,735 | Consistent Kernel Density Estimation with Non-Vanishing Bandwidth | stat.ML | Consistency of the kernel density estimator requires that the kernel
bandwidth tends to zero as the sample size grows. In this paper we investigate
the question of whether consistency is possible when the bandwidth is fixed, if
we consider a more general class of weighted KDEs. To answer this question in
the affirmativ... | computer science |
6,736 | Exploring the Regularity of Sparse Structure in Convolutional Neural
Networks | cs.LG | Sparsity helps reduce the computational complexity of deep neural networks by
skipping zeros. Taking advantage of sparsity is listed as a high priority in
next generation DNN accelerators such as TPU. The structure of sparsity, i.e.,
the granularity of pruning, affects the efficiency of hardware accelerator
design as w... | computer science |
6,737 | Approximation and Convergence Properties of Generative Adversarial
Learning | cs.LG | Generative adversarial networks (GAN) approximate a target data distribution
by jointly optimizing an objective function through a "two-player game" between
a generator and a discriminator. Despite their empirical success, however, two
very basic questions on how well they can approximate the target distribution
remain... | computer science |
6,738 | Asynchronous Parallel Bayesian Optimisation via Thompson Sampling | stat.ML | We design and analyse variations of the classical Thompson sampling (TS)
procedure for Bayesian optimisation (BO) in settings where function evaluations
are expensive, but can be performed in parallel. Our theoretical analysis shows
that a direct application of the sequential Thompson sampling algorithm in
either synch... | computer science |
6,739 | Implicit Regularization in Matrix Factorization | stat.ML | We study implicit regularization when optimizing an underdetermined quadratic
objective over a matrix $X$ with gradient descent on a factorization of $X$. We
conjecture and provide empirical and theoretical evidence that with small
enough step sizes and initialization close enough to the origin, gradient
descent on a f... | computer science |
6,740 | Latent Geometry and Memorization in Generative Models | cs.LG | It can be difficult to tell whether a trained generative model has learned to
generate novel examples or has simply memorized a specific set of outputs. In
published work, it is common to attempt to address this visually, for example
by displaying a generated example and its nearest neighbor(s) in the training
set (in,... | computer science |
6,741 | Diagonal Rescaling For Neural Networks | cs.LG | We define a second-order neural network stochastic gradient training
algorithm whose block-diagonal structure effectively amounts to normalizing the
unit activations. Investigating why this algorithm lacks in robustness then
reveals two interesting insights. The first insight suggests a new way to scale
the stepsizes, ... | computer science |
6,742 | Stabilizing Training of Generative Adversarial Networks through
Regularization | cs.LG | Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping s... | computer science |
6,743 | An Efficient Algorithm for Bayesian Nearest Neighbours | cs.LG | K-Nearest Neighbours (k-NN) is a popular classification and regression
algorithm, yet one of its main limitations is the difficulty in choosing the
number of neighbours. We present a Bayesian algorithm to compute the posterior
probability distribution for k given a target point within a data-set,
efficiently and withou... | computer science |
6,744 | Combinatorial Multi-Armed Bandits with Filtered Feedback | cs.LG | Motivated by problems in search and detection we present a solution to a
Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward
distributions and a new class of feedback, filtered semibandit feedback. In a
CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in
each round, g... | computer science |
6,745 | Discriminative Metric Learning with Deep Forest | stat.ML | A Discriminative Deep Forest (DisDF) as a metric learning algorithm is
proposed in the paper. It is based on the Deep Forest or gcForest proposed by
Zhou and Feng and can be viewed as a gcForest modification. The case of the
fully supervised learning is studied when the class labels of individual
training examples are ... | computer science |
6,746 | Fisher GAN | cs.LG | Generative Adversarial Networks (GANs) are powerful models for learning
complex distributions. Stable training of GANs has been addressed in many
recent works which explore different metrics between distributions. In this
paper we introduce Fisher GAN which fits within the Integral Probability
Metrics (IPM) framework f... | computer science |
6,747 | Lifelong Generative Modeling | stat.ML | Lifelong learning is the problem of learning multiple consecutive tasks in a
sequential manner where knowledge gained from previous tasks is retained and
used for future learning. It is essential towards the development of
intelligent machines that can adapt to their surroundings. In this work we
focus on a lifelong le... | computer science |
6,748 | Efficient Modeling of Latent Information in Supervised Learning using
Gaussian Processes | stat.ML | Often in machine learning, data are collected as a combination of multiple
conditions, e.g., the voice recordings of multiple persons, each labeled with
an ID. How could we build a model that captures the latent information related
to these conditions and generalize to a new one with few data? We present a new
model ca... | computer science |
6,749 | Learning Data Manifolds with a Cutting Plane Method | cs.LG | We consider the problem of classifying data manifolds where each manifold
represents invariances that are parameterized by continuous degrees of freedom.
Conventional data augmentation methods rely upon sampling large numbers of
training examples from these manifolds; instead, we propose an iterative
algorithm called M... | computer science |
6,750 | Improving the Expected Improvement Algorithm | cs.LG | The expected improvement (EI) algorithm is a popular strategy for information
collection in optimization under uncertainty. The algorithm is widely known to
be too greedy, but nevertheless enjoys wide use due to its simplicity and
ability to handle uncertainty and noise in a coherent decision theoretic
framework. To pr... | computer science |
6,751 | Distributed Convolutional Sparse Coding | cs.LG | We consider the problem of building shift-invariant representations for long
signals in the context of distributed processing. We propose an asynchronous
algorithm based on coordinate descent called DICOD to efficiently solve the
$\ell_1$-minimization problems involved in convolutional sparse coding. This
algorithm lev... | computer science |
6,752 | Coreset Construction via Randomized Matrix Multiplication | stat.ML | Coresets are small sets of points that approximate the properties of a larger
point-set. For example, given a compact set $\mathcal{S} \subseteq
\mathbb{R}^d$, a coreset could be defined as a (weighted) subset of
$\mathcal{S}$ that approximates the sum of squared distances from $\mathcal{S}$
to every linear subspace of... | computer science |
6,753 | Adaptive Classification for Prediction Under a Budget | stat.ML | We propose a novel adaptive approximation approach for test-time
resource-constrained prediction. Given an input instance at test-time, a gating
function identifies a prediction model for the input among a collection of
models. Our objective is to minimize overall average cost without sacrificing
accuracy. We learn gat... | computer science |
6,754 | Deep Learning for Patient-Specific Kidney Graft Survival Analysis | cs.LG | An accurate model of patient-specific kidney graft survival distributions can
help to improve shared-decision making in the treatment and care of patients.
In this paper, we propose a deep learning method that directly models the
survival function instead of estimating the hazard function to predict survival
times for ... | computer science |
6,755 | The Principle of Logit Separation | stat.ML | We consider neural network training, in applications in which there are many
possible classes, but at test-time, the task is to identify only whether the
given example belongs to a specific class, which can be different in different
applications of the classifier. For instance, this is the case in an image
search engin... | computer science |
6,756 | Boltzmann Exploration Done Right | cs.LG | Boltzmann exploration is a classic strategy for sequential decision-making
under uncertainty, and is one of the most standard tools in Reinforcement
Learning (RL). Despite its widespread use, there is virtually no theoretical
understanding about the limitations or the actual benefits of this exploration
scheme. Does it... | computer science |
6,757 | Neural Embeddings of Graphs in Hyperbolic Space | stat.ML | Neural embeddings have been used with great success in Natural Language
Processing (NLP). They provide compact representations that encapsulate word
similarity and attain state-of-the-art performance in a range of linguistic
tasks. The success of neural embeddings has prompted significant amounts of
research into appli... | computer science |
6,758 | Federated Multi-Task Learning | cs.LG | Federated learning poses new statistical and systems challenges in training
machine learning models over distributed networks of devices. In this work, we
show that multi-task learning is naturally suited to handle the statistical
challenges of this setting, and propose a novel systems-aware optimization
method, MOCHA,... | computer science |
6,759 | Iterative Machine Teaching | stat.ML | In this paper, we consider the problem of machine teaching, the inverse
problem of machine learning. Different from traditional machine teaching which
views the learners as batch algorithms, we study a new paradigm where the
learner uses an iterative algorithm and a teacher can feed examples
sequentially and intelligen... | computer science |
6,760 | Joint auto-encoders: a flexible multi-task learning framework | stat.ML | The incorporation of prior knowledge into learning is essential in achieving
good performance based on small noisy samples. Such knowledge is often
incorporated through the availability of related data arising from domains and
tasks similar to the one of current interest. Ideally one would like to allow
both the data f... | computer science |
6,761 | The Cramer Distance as a Solution to Biased Wasserstein Gradients | cs.LG | The Wasserstein probability metric has received much attention from the
machine learning community. Unlike the Kullback-Leibler divergence, which
strictly measures change in probability, the Wasserstein metric reflects the
underlying geometry between outcomes. The value of being sensitive to this
geometry has been demo... | computer science |
6,762 | Recurrent Estimation of Distributions | cs.LG | This paper presents the recurrent estimation of distributions (RED) for
modeling real-valued data in a semiparametric fashion. RED models make two
novel uses of recurrent neural networks (RNNs) for density estimation of
general real-valued data. First, RNNs are used to transform input covariates
into a latent space to ... | computer science |
6,763 | Forward-Backward Selection with Early Dropping | cs.LG | Forward-backward selection is one of the most basic and commonly-used feature
selection algorithms available. It is also general and conceptually applicable
to many different types of data. In this paper, we propose a heuristic that
significantly improves its running time, while preserving predictive accuracy.
The idea... | computer science |
6,764 | Objective-Reinforced Generative Adversarial Networks (ORGAN) for
Sequence Generation Models | stat.ML | In unsupervised data generation tasks, besides the generation of a sample
based on previous observations, one would often like to give hints to the model
in order to bias the generation towards desirable metrics. We propose a method
that combines Generative Adversarial Networks (GANs) and reinforcement learning
(RL) in... | computer science |
6,765 | High Dimensional Structured Superposition Models | cs.LG | High dimensional superposition models characterize observations using
parameters which can be written as a sum of multiple component parameters, each
with its own structure, e.g., sum of low rank and sparse matrices, sum of
sparse and rotated sparse vectors, etc. In this paper, we consider general
superposition models ... | computer science |
6,766 | The ALAMO approach to machine learning | cs.LG | ALAMO is a computational methodology for leaning algebraic functions from
data. Given a data set, the approach begins by building a low-complexity,
linear model composed of explicit non-linear transformations of the independent
variables. Linear combinations of these non-linear transformations allow a
linear model to b... | computer science |
6,767 | Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time
Budget | stat.ML | Deep neural network (DNN) based approaches hold significant potential for
reinforcement learning (RL) and have already shown remarkable gains over
state-of-art methods in a number of applications. The effectiveness of DNN
methods can be attributed to leveraging the abundance of supervised data to
learn value functions,... | computer science |
6,768 | Spectral Norm Regularization for Improving the Generalizability of Deep
Learning | stat.ML | We investigate the generalizability of deep learning based on the sensitivity
to input perturbation. We hypothesize that the high sensitivity to the
perturbation of data degrades the performance on it. To reduce the sensitivity
to perturbation, we propose a simple and effective regularization method,
referred to as spe... | computer science |
6,769 | FALKON: An Optimal Large Scale Kernel Method | stat.ML | Kernel methods provide a principled way to perform non linear, nonparametric
learning. They rely on solid functional analytic foundations and enjoy optimal
statistical properties. However, at least in their basic form, they have
limited applicability in large scale scenarios because of stringent
computational requireme... | computer science |
6,770 | Greedy Algorithms for Cone Constrained Optimization with Convergence
Guarantees | cs.LG | Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe
(FW) algorithms regained popularity in recent years due to their simplicity,
effectiveness and theoretical guarantees. MP and FW address optimization over
the linear span and the convex hull of a set of atoms, respectively. In this
paper, we cons... | computer science |
6,771 | Toward Robustness against Label Noise in Training Deep Discriminative
Neural Networks | cs.LG | Collecting large training datasets, annotated with high-quality labels, is
costly and time-consuming. This paper proposes a novel framework for training
deep convolutional neural networks from noisy labeled datasets that can be
obtained cheaply. The problem is formulated using an undirected graphical model
that represe... | computer science |
6,772 | Subjective fairness: Fairness is in the eye of the beholder | cs.LG | We analyze different notions of fairness in decision making when the
underlying model is not known with certainty. We argue that recent notions of
fairness in machine learning need to be modified to incorporate uncertainties
about model parameters. We introduce the notion of {\em subjective fairness} as
a suitable cand... | computer science |
6,773 | Scalable Generalized Linear Bandits: Online Computation and Hashing | stat.ML | Generalized Linear Bandits (GLBs), a natural extension of the stochastic
linear bandits, has been popular and successful in recent years. However,
existing GLBs scale poorly with the number of rounds and the number of arms,
limiting their utility in practice. This paper proposes new, scalable solutions
to the GLB probl... | computer science |
6,774 | Discriminative k-shot learning using probabilistic models | stat.ML | This paper introduces a probabilistic framework for k-shot image
classification. The goal is to generalise from an initial large-scale
classification task to a separate task comprising new classes and small numbers
of examples. The new approach not only leverages the feature-based
representation learned by a neural net... | computer science |
6,775 | On Unifying Deep Generative Models | cs.LG | Deep generative models have achieved impressive success in recent years.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
powerful frameworks for deep generative model learning, have largely been
considered as two distinct paradigms and received extensive independent study
respectively. Th... | computer science |
6,776 | Learning causal Bayes networks using interventional path queries in
polynomial time and sample complexity | cs.LG | Causal discovery from empirical data is a fundamental problem in many
scientific domains. Observational data allows for identifiability only up to
Markov equivalence class. In this paper we first propose a polynomial time
algorithm for learning the exact correctly-oriented structure of the transitive
reduction of any c... | computer science |
6,777 | Information, Privacy and Stability in Adaptive Data Analysis | cs.LG | Traditional statistical theory assumes that the analysis to be performed on a
given data set is selected independently of the data themselves. This
assumption breaks downs when data are re-used across analyses and the analysis
to be performed at a given stage depends on the results of earlier stages. Such
dependency ca... | computer science |
6,778 | Nonconvex penalties with analytical solutions for one-bit compressive
sensing | cs.LG | One-bit measurements widely exist in the real world, and they can be used to
recover sparse signals. This task is known as the problem of learning
halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in
signal processing. In this paper, we propose novel algorithms based on both
convex and nonconvex s... | computer science |
6,779 | InfiniteBoost: building infinite ensembles with gradient descent | stat.ML | In machine learning ensemble methods have demonstrated high accuracy for the
variety of problems in different areas. The most known algorithms intensively
used in practice are random forests and gradient boosting. In this paper we
present InfiniteBoost - a novel algorithm, which combines the best properties
of these tw... | computer science |
6,780 | Evolving imputation strategies for missing data in classification
problems with TPOT | cs.LG | Missing data has a ubiquitous presence in real-life applications of machine
learning techniques. Imputation methods are algorithms conceived for restoring
missing values in the data, based on other entries in the database. The choice
of the imputation method has an influence on the performance of the machine
learning t... | computer science |
6,781 | Inconsistent Node Flattening for Improving Top-down Hierarchical
Classification | cs.LG | Large-scale classification of data where classes are structurally organized
in a hierarchy is an important area of research. Top-down approaches that
exploit the hierarchy during the learning and prediction phase are efficient
for large scale hierarchical classification. However, accuracy of top-down
approaches is poor... | computer science |
6,782 | UCB Exploration via Q-Ensembles | cs.LG | We show how an ensemble of $Q^*$-functions can be leveraged for more
effective exploration in deep reinforcement learning. We build on well
established algorithms from the bandit setting, and adapt them to the
$Q$-learning setting. We propose an exploration strategy based on
upper-confidence bounds (UCB). Our experimen... | computer science |
6,783 | Open Loop Hyperparameter Optimization and Determinantal Point Processes | stat.ML | Driven by the need for parallelizable hyperparameter optimization methods,
this paper studies \emph{open loop} search methods: sequences that are
predetermined and can be generated before a single configuration is evaluated.
Examples include grid search, uniform random search, low discrepancy sequences,
and other sampl... | computer science |
6,784 | Embedding Feature Selection for Large-scale Hierarchical Classification | cs.LG | Large-scale Hierarchical Classification (HC) involves datasets consisting of
thousands of classes and millions of training instances with high-dimensional
features posing several big data challenges. Feature selection that aims to
select the subset of discriminant features is an effective strategy to deal
with large-sc... | computer science |
6,785 | Classifying Documents within Multiple Hierarchical Datasets using
Multi-Task Learning | cs.LG | Multi-task learning (MTL) is a supervised learning paradigm in which the
prediction models for several related tasks are learned jointly to achieve
better generalization performance. When there are only a few training examples
per task, MTL considerably outperforms the traditional Single task learning
(STL) in terms of... | computer science |
6,786 | Robust Online Multi-Task Learning with Correlative and Personalized
Structures | cs.LG | Multi-Task Learning (MTL) can enhance a classifier's generalization
performance by learning multiple related tasks simultaneously. Conventional MTL
works under the offline or batch setting, and suffers from expensive training
cost and poor scalability. To address such inefficiency issues, online learning
techniques hav... | computer science |
6,787 | Deep Learning: Generalization Requires Deep Compositional Feature Space
Design | cs.LG | Generalization error defines the discriminability and the representation
power of a deep model. In this work, we claim that feature space design using
deep compositional function plays a significant role in generalization along
with explicit and implicit regularizations. Our claims are being established
with several im... | computer science |
6,788 | Efficient Reinforcement Learning via Initial Pure Exploration | cs.LG | In several realistic situations, an interactive learning agent can practice
and refine its strategy before going on to be evaluated. For instance, consider
a student preparing for a series of tests. She would typically take a few
practice tests to know which areas she needs to improve upon. Based of the
scores she obta... | computer science |
6,789 | Fast Black-box Variational Inference through Stochastic Trust-Region
Optimization | cs.LG | We introduce TrustVI, a fast second-order algorithm for black-box variational
inference based on trust-region optimization and the reparameterization trick.
At each iteration, TrustVI proposes and assesses a step based on minibatches of
draws from the variational distribution. The algorithm provably converges to a
stat... | computer science |
6,790 | On learning the structure of Bayesian Networks and submodular function
maximization | cs.LG | Learning the structure of dependencies among multiple random variables is a
problem of considerable theoretical and practical interest. In practice, score
optimisation with multiple restarts provides a practical and surprisingly
successful solution, yet the conditions under which this may be a well founded
strategy are... | computer science |
6,791 | A Convex Framework for Fair Regression | cs.LG | We introduce a flexible family of fairness regularizers for (linear and
logistic) regression problems. These regularizers all enjoy convexity,
permitting fast optimization, and they span the rang from notions of group
fairness to strong individual fairness. By varying the weight on the fairness
regularizer, we can comp... | computer science |
6,792 | Distribution-Free One-Pass Learning | cs.LG | In many large-scale machine learning applications, data are accumulated with
time, and thus, an appropriate model should be able to update in an online
paradigm. Moreover, as the whole data volume is unknown when constructing the
model, it is desired to scan each data item only once with a storage
independent with the ... | computer science |
6,793 | Forward Thinking: Building and Training Neural Networks One Layer at a
Time | stat.ML | We present a general framework for training deep neural networks without
backpropagation. This substantially decreases training time and also allows for
construction of deep networks with many sorts of learners, including networks
whose layers are defined by functions that are not easily differentiated, like
decision t... | computer science |
6,794 | Self-Normalizing Neural Networks | cs.LG | Deep Learning has revolutionized vision via convolutional neural networks
(CNNs) and natural language processing via recurrent neural networks (RNNs).
However, success stories of Deep Learning with standard feed-forward neural
networks (FNNs) are rare. FNNs that perform well are typically shallow and,
therefore cannot ... | computer science |
6,795 | Scaling up the Automatic Statistician: Scalable Structure Discovery
using Gaussian Processes | stat.ML | Automating statistical modelling is a challenging problem in artificial
intelligence. The Automatic Statistician takes a first step in this direction,
by employing a kernel search algorithm with Gaussian Processes (GP) to provide
interpretable statistical models for regression problems. However this does not
scale due ... | computer science |
6,796 | Clustering with t-SNE, provably | cs.LG | t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and
visualization method proposed by van der Maaten & Hinton in 2008, has rapidly
become a standard tool in a number of natural sciences. Despite its
overwhelming success, there is a distinct lack of mathematical foundations and
the inner workings of... | computer science |
6,797 | Real-valued (Medical) Time Series Generation with Recurrent Conditional
GANs | stat.ML | Generative Adversarial Networks (GANs) have shown remarkable success as a
framework for training models to produce realistic-looking data. In this work,
we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to
produce realistic real-valued multi-dimensional time series, with an emphasis
on their appli... | computer science |
6,798 | Nuclear Discrepancy for Active Learning | cs.LG | Active learning algorithms propose which unlabeled objects should be queried
for their labels to improve a predictive model the most. We study active
learners that minimize generalization bounds and uncover relationships between
these bounds that lead to an improved approach to active learning. In
particular we show th... | computer science |
6,799 | Enhancing The Reliability of Out-of-distribution Image Detection in
Neural Networks | cs.LG | We consider the problem of detecting out-of-distribution images in neural
networks. We propose ODIN, a simple and effective method that does not require
any change to a pre-trained neural network. Our method is based on the
observation that using temperature scaling and adding small perturbations to
the input can separ... | computer science |
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