Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
7,700 | Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
Networks for Thompson Sampling | stat.ML | Recent advances in deep reinforcement learning have made significant strides
in performance on applications such as Go and Atari games. However, developing
practical methods to balance exploration and exploitation in complex domains
remains largely unsolved. Thompson Sampling and its extension to reinforcement
learning... | computer science |
7,701 | A representer theorem for deep neural networks | stat.ML | We propose to optimize the activation functions of a deep neural network by
adding a corresponding functional regularization to the cost function. We
justify the use of a second-order total-variation criterion. This allows us to
derive a general representer theorem for deep neural networks that makes a
direct connectio... | computer science |
7,702 | Interpreting Complex Regression Models | cs.LG | Interpretation of a machine learning induced models is critical for feature
engineering, debugging, and, arguably, compliance. Yet, best of breed machine
learning models tend to be very complex. This paper presents a method for model
interpretation which has the main benefit that the simple interpretations it
provides ... | computer science |
7,703 | Scalable kernel-based variable selection with sparsistency | stat.ML | Variable selection is central to high-dimensional data analysis, and various
algorithms have been developed. Ideally, a variable selection algorithm shall
be flexible, scalable, and with theoretical guarantee, yet most existing
algorithms cannot attain these properties at the same time. In this article, a
three-step va... | computer science |
7,704 | Disentangling the independently controllable factors of variation by
interacting with the world | stat.ML | It has been postulated that a good representation is one that disentangles
the underlying explanatory factors of variation. However, it remains an open
question what kind of training framework could potentially achieve that.
Whereas most previous work focuses on the static setting (e.g., with images),
we postulate that... | computer science |
7,705 | Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian
Vector Autoregressive Processes | stat.ML | High-dimensional time series data exist in numerous areas such as finance,
genomics, healthcare, and neuroscience. An unavoidable aspect of all such
datasets is missing data, and dealing with this issue has been an important
focus in statistics, control, and machine learning. In this work, we consider a
high-dimensiona... | computer science |
7,706 | Data-dependent PAC-Bayes priors via differential privacy | cs.LG | The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999)
can incorporate knowledge about the learning algorithm and data distribution
through the use of distribution-dependent priors, yielding tighter
generalization bounds on data-dependent posteriors. Using this flexibility,
however, is difficult, e... | computer science |
7,707 | Optimizing over a Restricted Policy Class in Markov Decision Processes | cs.LG | We address the problem of finding an optimal policy in a Markov decision
process under a restricted policy class defined by the convex hull of a set of
base policies. This problem is of great interest in applications in which a
number of reasonably good (or safe) policies are already known and we are only
interested in... | computer science |
7,708 | Link Prediction Based on Graph Neural Networks | cs.LG | Traditional methods for link prediction can be categorized into three main
types: graph structure feature-based, latent feature-based, and explicit
feature-based. Graph structure feature methods leverage some handcrafted node
proximity scores, e.g., common neighbors, to estimate the likelihood of links.
Latent feature ... | computer science |
7,709 | Robust GANs against Dishonest Adversaries | cs.LG | Robustness of deep learning models is a property that has recently gained
increasing attention. We formally define a notion of robustness for generative
adversarial models, and show that, perhaps surprisingly, the GAN in its
original form is not robust. Indeed, the discriminator in GANs may be viewed as
merely offering... | computer science |
7,710 | Online learning with kernel losses | stat.ML | We present a generalization of the adversarial linear bandits framework,
where the underlying losses are kernel functions (with an associated
reproducing kernel Hilbert space) rather than linear functions. We study a
version of the exponential weights algorithm and bound its regret in this
setting. Under conditions on ... | computer science |
7,711 | Train Feedfoward Neural Network with Layer-wise Adaptive Rate via
Approximating Back-matching Propagation | stat.ML | Stochastic gradient descent (SGD) has achieved great success in training deep
neural network, where the gradient is computed through back-propagation.
However, the back-propagated values of different layers vary dramatically. This
inconsistence of gradient magnitude across different layers renders
optimization of deep ... | computer science |
7,712 | Matching Convolutional Neural Networks without Priors about Data | cs.LG | We propose an extension of Convolutional Neural Networks (CNNs) to
graph-structured data, including strided convolutions and data augmentation on
graphs.
Our method matches the accuracy of state-of-the-art CNNs when applied on
images, without any prior about their 2D regular structure.
On fMRI data, we obtain a sig... | computer science |
7,713 | Learning to recognize touch gestures: recurrent vs. convolutional
features and dynamic sampling | cs.LG | We propose a fully automatic method for learning gestures on big touch
devices in a potentially multi-user context. The goal is to learn general
models capable of adapting to different gestures, user styles and hardware
variations (e.g. device sizes, sampling frequencies and regularities).
Based on deep neural networ... | computer science |
7,714 | Attention-Based Guided Structured Sparsity of Deep Neural Networks | cs.LG | Network pruning is aimed at imposing sparsity in a neural network
architecture by increasing the portion of zero-valued weights for reducing its
size regarding energy-efficiency consideration and increasing evaluation speed.
In most of the conducted research efforts, the sparsity is enforced for network
pruning without... | computer science |
7,715 | The Emergence of Spectral Universality in Deep Networks | stat.ML | Recent work has shown that tight concentration of the entire spectrum of
singular values of a deep network's input-output Jacobian around one at
initialization can speed up learning by orders of magnitude. Therefore, to
guide important design choices, it is important to build a full theoretical
understanding of the spe... | computer science |
7,716 | The Mirage of Action-Dependent Baselines in Reinforcement Learning | cs.LG | Policy gradient methods are a widely used class of model-free reinforcement
learning algorithms where a state-dependent baseline is used to reduce gradient
estimator variance. Several recent papers extend the baseline to depend on both
the state and action and suggest that this significantly reduces variance and
improv... | computer science |
7,717 | ADMM-based Networked Stochastic Variational Inference | cs.LG | Owing to the recent advances in "Big Data" modeling and prediction tasks,
variational Bayesian estimation has gained popularity due to their ability to
provide exact solutions to approximate posteriors. One key technique for
approximate inference is stochastic variational inference (SVI). SVI poses
variational inferenc... | computer science |
7,718 | Semi-Supervised Learning Enabled by Multiscale Deep Neural Network
Inversion | cs.LG | Deep Neural Networks (DNNs) provide state-of-the-art solutions in several
difficult machine perceptual tasks. However, their performance relies on the
availability of a large set of labeled training data, which limits the breadth
of their applicability. Hence, there is a need for new {\em semi-supervised
learning} meth... | computer science |
7,719 | As you like it: Localization via paired comparisons | stat.ML | Suppose that we wish to estimate a vector $\mathbf{x}$ from a set of binary
paired comparisons of the form "$\mathbf{x}$ is closer to $\mathbf{p}$ than to
$\mathbf{q}$" for various choices of vectors $\mathbf{p}$ and $\mathbf{q}$. The
problem of estimating $\mathbf{x}$ from this type of observation arises in a
variety ... | computer science |
7,720 | Learning Discriminative Multilevel Structured Dictionaries for
Supervised Image Classification | stat.ML | Sparse representations using overcomplete dictionaries have proved to be a
powerful tool in many signal processing applications such as denoising,
super-resolution, inpainting, compression or classification. The sparsity of
the representation very much depends on how well the dictionary is adapted to
the data at hand. ... | computer science |
7,721 | Predictive Uncertainty Estimation via Prior Networks | stat.ML | Estimating uncertainty is important to improving the safety of AI systems.
Recently baseline tasks and metrics have been defined and several practical
methods for estimating uncertainty developed. However, these approaches attempt
to model distributional uncertainty either implicitly through model uncertainty
or as dat... | computer science |
7,722 | Memory-based Parameter Adaptation | stat.ML | Deep neural networks have excelled on a wide range of problems, from vision
to language and game playing. Neural networks very gradually incorporate
information into weights as they process data, requiring very low learning
rates. If the training distribution shifts, the network is slow to adapt, and
when it does adapt... | computer science |
7,723 | Automatic topography of high-dimensional data sets by non-parametric
Density Peak clustering | stat.ML | Data analysis in high-dimensional spaces aims at obtaining a synthetic
description of a data set, revealing its main structure and its salient
features. We here introduce an approach for charting data spaces, providing a
topography of the probability distribution from which the data are harvested.
This topography inclu... | computer science |
7,724 | Exactly Robust Kernel Principal Component Analysis | cs.LG | We propose a novel method called robust kernel principal component analysis
(RKPCA) to decompose a partially corrupted matrix as a sparse matrix plus a
high or full-rank matrix whose columns are drawn from a nonlinear
low-dimensional latent variable model. RKPCA can be applied to many problems
such as noise removal and... | computer science |
7,725 | Modeling Activity Tracker Data Using Deep Boltzmann Machines | stat.ML | Commercial activity trackers are set to become an essential tool in health
research, due to increasing availability in the general population. The
corresponding vast amounts of mostly unlabeled data pose a challenge to
statistical modeling approaches. To investigate the feasibility of deep
learning approaches for unsup... | computer science |
7,726 | Constrained Classification and Ranking via Quantiles | cs.LG | In most machine learning applications, classification accuracy is not the
primary metric of interest. Binary classifiers which face class imbalance are
often evaluated by the $F_\beta$ score, area under the precision-recall curve,
Precision at K, and more. The maximization of many of these metrics can be
expressed as a... | computer science |
7,727 | Autoencoding topology | stat.ML | The problem of learning a manifold structure on a dataset is framed in terms
of a generative model, to which we use ideas behind autoencoders (namely
adversarial/Wasserstein autoencoders) to fit deep neural networks. From a
machine learning perspective, the resulting structure, an atlas of a manifold,
may be viewed as ... | computer science |
7,728 | Learning with Correntropy-induced Losses for Regression with Mixture of
Symmetric Stable Noise | cs.LG | In recent years, correntropy and its applications in machine learning have
been drawing continuous attention owing to its merits in dealing with
non-Gaussian noise and outliers. However, theoretical understanding of
correntropy, especially in the statistical learning context, is still limited.
In this study, within the... | computer science |
7,729 | Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning | stat.ML | An ensemble of neural networks is known to be more robust and accurate than
an individual network, however usually with linearly-increased cost in both
training and testing. In this work, we propose a two-stage method to learn
Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we
run SG-MCMC wi... | computer science |
7,730 | The Regularization Effects of Anisotropic Noise in Stochastic Gradient
Descent | stat.ML | Understanding the generalization of deep learning has raised lots of concerns
recently, where the learning algorithms play an important role in
generalization performance, such as stochastic gradient descent (SGD). Along
this line, we particularly study the anisotropic noise introduced by SGD, and
investigate its impor... | computer science |
7,731 | prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks | stat.ML | Phase retrieval (PR) algorithms have become an important component in many
modern computational imaging systems. For instance, in the context of
ptychography and speckle correlation imaging PR algorithms enable imaging past
the diffraction limit and through scattering media, respectively.
Unfortunately, traditional PR ... | computer science |
7,732 | Interval-based Prediction Uncertainty Bound Computation in Learning with
Missing Values | stat.ML | The problem of machine learning with missing values is common in many areas.
A simple approach is to first construct a dataset without missing values simply
by discarding instances with missing entries or by imputing a fixed value for
each missing entry, and then train a prediction model with the new dataset. A
drawbac... | computer science |
7,733 | Wasserstein Distance Measure Machines | cs.LG | This paper presents a distance-based discriminative framework for learning
with probability distributions. Instead of using kernel mean embeddings or
generalized radial basis kernels, we introduce embeddings based on
dissimilarity of distributions to some reference distributions denoted as
templates. Our framework exte... | computer science |
7,734 | Minimax rates for cost-sensitive learning on manifolds with approximate
nearest neighbours | cs.LG | We study the approximate nearest neighbour method for cost-sensitive
classification on low-dimensional manifolds embedded within a high-dimensional
feature space. We determine the minimax learning rates for distributions on a
smooth manifold, in a cost-sensitive setting. This generalises a classic result
of Audibert an... | computer science |
7,735 | The K-Nearest Neighbour UCB algorithm for multi-armed bandits with
covariates | cs.LG | In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
for multi-armed bandits with covariates. We focus on a setting where the
covariates are supported on a metric space of low intrinsic dimension, such as
a manifold embedded within a high dimensional ambient feature space. The
algorithm is concept... | computer science |
7,736 | Inferring Missing Categorical Information in Noisy and Sparse Web Markup | cs.LG | Embedded markup of Web pages has seen widespread adoption throughout the past
years driven by standards such as RDFa and Microdata and initiatives such as
schema.org, where recent studies show an adoption by 39% of all Web pages
already in 2016. While this constitutes an important information source for
tasks such as W... | computer science |
7,737 | PIP Distance: A Unitary-invariant Metric for Understanding Functionality
and Dimensionality of Vector Embeddings | stat.ML | In this paper, we present a theoretical framework for understanding vector
embedding, a fundamental building block of many deep learning models,
especially in NLP. We discover a natural unitary-invariance in vector
embeddings, which is required by the distributional hypothesis. This
unitary-invariance states the fact t... | computer science |
7,738 | On Polynomial Time PAC Reinforcement Learning with Rich Observations | cs.LG | We study the computational tractability of provably sample-efficient (PAC)
reinforcement learning in episodic environments with high-dimensional
observations. We present new sample efficient algorithms for environments with
deterministic hidden state dynamics but stochastic rich observations. These
methods represent co... | computer science |
7,739 | Clinically Meaningful Comparisons Over Time: An Approach to Measuring
Patient Similarity based on Subsequence Alignment | cs.LG | Longitudinal patient data has the potential to improve clinical risk
stratification models for disease. However, chronic diseases that progress
slowly over time are often heterogeneous in their clinical presentation.
Patients may progress through disease stages at varying rates. This leads to
pathophysiological misalig... | computer science |
7,740 | Detecting non-causal artifacts in multivariate linear regression models | stat.ML | We consider linear models where $d$ potential causes $X_1,...,X_d$ are
correlated with one target quantity $Y$ and propose a method to infer whether
the association is causal or whether it is an artifact caused by overfitting or
hidden common causes. We employ the idea that in the former case the vector of
regression c... | computer science |
7,741 | Gradient-based Sampling: An Adaptive Importance Sampling for
Least-squares | stat.ML | In modern data analysis, random sampling is an efficient and widely-used
strategy to overcome the computational difficulties brought by large sample
size. In previous studies, researchers conducted random sampling which is
according to the input data but independent on the response variable, however
the response variab... | computer science |
7,742 | An Overview of Robust Subspace Recovery | cs.LG | This paper will serve as an introduction to the body of work on robust
subspace recovery. Robust subspace recovery involves finding an underlying
low-dimensional subspace in a dataset that is possibly corrupted with outliers.
While this problem is easy to state, it has been difficult to develop optimal
algorithms due t... | computer science |
7,743 | Building a Telescope to Look Into High-Dimensional Image Spaces | stat.ML | An image pattern can be represented by a probability distribution whose
density is concentrated on different low-dimensional subspaces in the
high-dimensional image space. Such probability densities have an astronomical
number of local modes corresponding to typical pattern appearances. Related
groups of modes can join... | computer science |
7,744 | Practical Contextual Bandits with Regression Oracles | cs.LG | A major challenge in contextual bandits is to design general-purpose
algorithms that are both practically useful and theoretically well-founded. We
present a new technique that has the empirical and computational advantages of
realizability-based approaches combined with the flexibility of agnostic
methods. Our algorit... | computer science |
7,745 | Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in
Distributed SGD | stat.ML | Distributed Stochastic Gradient Descent (SGD) when run in a synchronous
manner, suffers from delays in waiting for the slowest learners (stragglers).
Asynchronous methods can alleviate stragglers, but cause gradient staleness
that can adversely affect convergence. In this work we present the first
theoretical character... | computer science |
7,746 | Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase
Procrustes Flow | stat.ML | We revisit the inductive matrix completion problem that aims to recover a
rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior
information. The goal is to make use of the known $n$ features to reduce sample
and computational complexities. We present and analyze a new gradient-based
non-convex... | computer science |
7,747 | Greedy stochastic algorithms for entropy-regularized optimal transport
problems | stat.ML | Optimal transport (OT) distances are finding evermore applications in machine
learning and computer vision, but their wide spread use in larger-scale
problems is impeded by their high computational cost. In this work we develop a
family of fast and practical stochastic algorithms for solving the optimal
transport probl... | computer science |
7,748 | Deep Network Regularization via Bayesian Inference of Synaptic
Connectivity | cs.LG | Deep neural networks (DNNs) often require good regularizers to generalize
well. Currently, state-of-the-art DNN regularization techniques consist in
randomly dropping units and/or connections on each iteration of the training
algorithm. Dropout and DropConnect are characteristic examples of such
regularizers, that are ... | computer science |
7,749 | Detecting Correlations with Little Memory and Communication | cs.LG | We study the problem of identifying correlations in multivariate data, under
information constraints: Either on the amount of memory that can be used by the
algorithm, or the amount of communication when the data is distributed across
several machines. We prove a tight trade-off between the memory/communication
complex... | computer science |
7,750 | Hierarchical Modeling and Shrinkage for User Session Length Prediction
in Media Streaming | stat.ML | An important metric of users' satisfaction and engagement within on-line
streaming services is the user session length, i.e. the amount of time they
spend on a service continuously without interruption. Being able to predict
this value directly benefits the recommendation and ad pacing contexts in music
and video strea... | computer science |
7,751 | Stochastic Activation Pruning for Robust Adversarial Defense | cs.LG | Neural networks are known to be vulnerable to adversarial examples. Carefully
chosen perturbations to real images, while imperceptible to humans, induce
misclassification and threaten the reliability of deep learning systems in the
wild. To guard against adversarial examples, we take inspiration from game
theory and ca... | computer science |
7,752 | Memorization Precedes Generation: Learning Unsupervised GANs with Memory
Networks | cs.LG | We propose an approach to address two issues that commonly occur during
training of unsupervised GANs. First, since GANs use only a continuous latent
distribution to embed multiple classes or clusters of data, they often do not
correctly handle the structural discontinuity between disparate classes in a
latent space. S... | computer science |
7,753 | Adversarial Extreme Multi-label Classification | stat.ML | The goal in extreme multi-label classification is to learn a classifier which
can assign a small subset of relevant labels to an instance from an extremely
large set of target labels. Datasets in extreme classification exhibit a long
tail of labels which have small number of positive training instances. In this
work, w... | computer science |
7,754 | A Comparative Study of Pairwise Learning Methods based on Kernel Ridge
Regression | stat.ML | Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a stat... | computer science |
7,755 | Optimizing Slate Recommendations via Slate-CVAE | stat.ML | The slate recommendation problem aims to find the "optimal" ordering of a
subset of documents to be presented on a surface that we call "slate". The
definition of "optimal" changes depending on the underlying applications but a
typical goal is to maximize user engagement with the slate. Solving this
problem at scale is... | computer science |
7,756 | How to Start Training: The Effect of Initialization and Architecture | stat.ML | We investigate the effects of initialization and architecture on the start of
training in deep ReLU nets. We identify two common failure modes for early
training in which the mean and variance of activations are poorly behaved. For
each failure mode, we give a rigorous proof of when it occurs at initialization
and how ... | computer science |
7,757 | Differentiable Submodular Maximization | stat.ML | We consider learning of submodular functions from data. These functions are
important in machine learning and have a wide range of applications, e.g. data
summarization, feature selection and active learning. Despite their
combinatorial nature, submodular functions can be maximized approximately with
strong theoretical... | computer science |
7,758 | Norm matters: efficient and accurate normalization schemes in deep
networks | stat.ML | Over the past few years batch-normalization has been commonly used in deep
networks, allowing faster training and high performance for a wide variety of
applications. However, the reasons behind its merits remained unanswered, with
several shortcomings that hindered its use for certain tasks. In this work we
present a ... | computer science |
7,759 | Marginal Singularity, and the Benefits of Labels in Covariate-Shift | stat.ML | We present new minimax results that concisely capture the relative benefits
of source and target labeled data, under covariate-shift. Namely, we show that
the benefits of target labels are controlled by a transfer-exponent $\gamma$
that encodes how singular Q is locally w.r.t. P, and interestingly allows
situations whe... | computer science |
7,760 | Conducting Credit Assignment by Aligning Local Representations | cs.LG | The use of back-propagation and its variants to train deep networks is often
problematic for new users, with issues such as exploding gradients, vanishing
gradients, and high sensitivity to weight initialization strategies often
making networks difficult to train. In this paper, we present Local
Representation Alignmen... | computer science |
7,761 | TACO: Learning Task Decomposition via Temporal Alignment for Control | cs.LG | Many advanced Learning from Demonstration (LfD) methods consider the
decomposition of complex, real-world tasks into simpler sub-tasks. By reusing
the corresponding sub-policies within and between tasks, they provide training
data for each policy from different high-level tasks and compose them to
perform novel ones. E... | computer science |
7,762 | Convergence of Gradient Descent on Separable Data | stat.ML | The implicit bias of gradient descent is not fully understood even in simple
linear classification tasks (e.g., logistic regression). Soudry et al. (2018)
studied this bias on separable data, where there are multiple solutions that
correctly classify the data. It was found that, when optimizing monotonically
decreasing... | computer science |
7,763 | Understanding Short-Horizon Bias in Stochastic Meta-Optimization | cs.LG | Careful tuning of the learning rate, or even schedules thereof, can be
crucial to effective neural net training. There has been much recent interest
in gradient-based meta-optimization, where one tunes hyperparameters, or even
learns an optimizer, in order to minimize the expected loss when the training
procedure is un... | computer science |
7,764 | Accelerated Gradient Boosting | stat.ML | Gradient tree boosting is a prediction algorithm that sequentially produces a
model in the form of linear combinations of decision trees, by solving an
infinite-dimensional optimization problem. We combine gradient boosting and
Nesterov's accelerated descent to design a new algorithm, which we call AGB
(for Accelerated... | computer science |
7,765 | HexaConv | cs.LG | The effectiveness of Convolutional Neural Networks stems in large part from
their ability to exploit the translation invariance that is inherent in many
learning problems. Recently, it was shown that CNNs can exploit other
invariances, such as rotation invariance, by using group convolutions instead
of planar convoluti... | computer science |
7,766 | Deep Super Learner: A Deep Ensemble for Classification Problems | cs.LG | Deep learning has become very popular for tasks such as predictive modeling
and pattern recognition in handling big data. Deep learning is a powerful
machine learning method that extracts lower level features and feeds them
forward for the next layer to identify higher level features that improve
performance. However, ... | computer science |
7,767 | Learning Memory Access Patterns | cs.LG | The explosion in workload complexity and the recent slow-down in Moore's law
scaling call for new approaches towards efficient computing. Researchers are
now beginning to use recent advances in machine learning in software
optimizations, augmenting or replacing traditional heuristics and data
structures. However, the s... | computer science |
7,768 | Sequential Maximum Margin Classifiers for Partially Labeled Data | stat.ML | In many real-world applications, data is not collected as one batch, but
sequentially over time, and often it is not possible or desirable to wait until
the data is completely gathered before analyzing it. Thus, we propose a
framework to sequentially update a maximum margin classifier by taking
advantage of the Maximum... | computer science |
7,769 | Gaussian Process Latent Variable Alignment Learning | stat.ML | We present a model that can automatically learn alignments between
high-dimensional data in an unsupervised manner. Learning alignments is an
ill-constrained problem as there are many different ways of defining a good
alignment. Our proposed method casts alignment learning in a framework where
both alignment and data a... | computer science |
7,770 | Transfer Automatic Machine Learning | cs.LG | Building effective neural networks requires many design choices. These
include the network topology, optimization procedure, regularization, stability
methods, and choice of pre-trained parameters. This design is time consuming
and requires expert input. Automatic Machine Learning aims automate this
process using hyper... | computer science |
7,771 | Fast Dawid-Skene | stat.ML | Many real world problems can now be effectively solved using supervised
machine learning. A major roadblock is often the lack of an adequate quantity
of labeled data for training. A possible solution is to assign the task of
labeling data to a crowd, and then infer the true label using aggregation
methods. A well-known... | computer science |
7,772 | A bag-to-class divergence approach to multiple-instance learning | stat.ML | In multi-instance (MI) learning, each object (bag) consists of multiple
feature vectors (instances), and is most commonly regarded as a set of points
in a multidimensional space. A different viewpoint is that the instances are
realisations of random vectors with corresponding probability distribution, and
that a bag is... | computer science |
7,773 | Deep Models of Interactions Across Sets | stat.ML | We use deep learning to model interactions across two or more sets of
objects, such as user-movie ratings or protein-drug bindings. The canonical
representation of such interactions is a matrix (or tensor) with an
exchangeability property: the encoding's meaning is not changed by permuting
rows or columns. We argue tha... | computer science |
7,774 | Fast Convergence for Stochastic and Distributed Gradient Descent in the
Interpolation Limit | stat.ML | Modern supervised learning techniques, particularly those using so called
deep nets, involve fitting high dimensional labelled data sets with functions
containing very large numbers of parameters. Much of this work is empirical,
and interesting phenomena have been observed that require theoretical
explanations, however... | computer science |
7,775 | A Bayesian and Machine Learning approach to estimating Influence Model
parameters for IM-RO | stat.ML | The rise of Online Social Networks (OSNs) has caused an insurmountable amount
of interest from advertisers and researchers seeking to monopolize on its
features. Researchers aim to develop strategies for determining how information
is propagated among users within an OSN that is captured by diffusion or
influence model... | computer science |
7,776 | Improving Optimization in Models With Continuous Symmetry Breaking | stat.ML | Many loss functions in representation learning are invariant under a
continuous symmetry transformation. As an example, consider word embeddings
(Mikolov et al., 2013), where the loss remains unchanged if we simultaneously
rotate all word and context embedding vectors. We show that representation
learning models with a... | computer science |
7,777 | Efficient Loss-Based Decoding On Graphs For Extreme Classification | cs.LG | In extreme classification problems, learning algorithms are required to map
instances to labels from an extremely large label set. We build on a recent
extreme classification framework with logarithmic time and space, and on a
general approach for error correcting output coding (ECOC), and introduce a
flexible and effi... | computer science |
7,778 | Learning Deep Generative Models of Graphs | cs.LG | Graphs are fundamental data structures which concisely capture the relational
structure in many important real-world domains, such as knowledge graphs,
physical and social interactions, language, and chemistry. Here we introduce a
powerful new approach for learning generative models over graphs, which can
capture both ... | computer science |
7,779 | High-Accuracy Low-Precision Training | cs.LG | Low-precision computation is often used to lower the time and energy cost of
machine learning, and recently hardware accelerators have been developed to
support it. Still, it has been used primarily for inference - not training.
Previous low-precision training algorithms suffered from a fundamental
tradeoff: as the num... | computer science |
7,780 | On Generation of Adversarial Examples using Convex Programming | cs.LG | It has been observed that deep learning architectures tend to make erroneous
decisions with high reliability for particularly designed adversarial
instances. In this work, we show that the perturbation analysis of these
architectures provides a method for generating adversarial instances by convex
programming which, fo... | computer science |
7,781 | Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine
Learning Blending | stat.ML | With the increasing penetration of solar power into power systems,
forecasting becomes critical in power system operations. In this paper, an
hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global
horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal
patterns, statistic... | computer science |
7,782 | Competitive Machine Learning: Best Theoretical Prediction vs
Optimization | cs.LG | Machine learning is often used in competitive scenarios: Participants learn
and fit static models, and those models compete in a shared platform. The
common assumption is that in order to win a competition one has to have the
best predictive model, i.e., the model with the smallest out-sample error. Is
that necessarily... | computer science |
7,783 | Scoring Formulation for Multi-Condition Joint PLDA | cs.LG | The joint PLDA model, is a generalization of PLDA where the nuisance variable
is no longer considered independent across samples, but potentially shared
(tied) across samples that correspond to the same nuisance condition. The
original work considered a single nuisance condition, deriving the EM and
scoring formulas fo... | computer science |
7,784 | Influence of the Event Rate on Discrimination Abilities of Bankruptcy
Prediction Models | stat.ML | In bankruptcy prediction, the proportion of events is very low, which is
often oversampled to eliminate this bias. In this paper, we study the influence
of the event rate on discrimination abilities of bankruptcy prediction models.
First the statistical association and significance of public records and
firmographics i... | computer science |
7,785 | Speech Recognition: Keyword Spotting Through Image Recognition | stat.ML | The problem of identifying voice commands has always been a challenge due to
the presence of noise and variability in speed, pitch, etc. We will compare the
efficacies of several neural network architectures for the speech recognition
problem. In particular, we will build a model to determine whether a one second
audio... | computer science |
7,786 | A Minimax Surrogate Loss Approach to Conditional Difference Estimation | stat.ML | We present a new machine learning approach to estimate personalized treatment
effects in the classical potential outcomes framework with binary outcomes. To
overcome the problem that both treatment and control outcomes for the same unit
are required for supervised learning, we propose surrogate loss functions that
inco... | computer science |
7,787 | On dynamic ensemble selection and data preprocessing for multi-class
imbalance learning | stat.ML | Class-imbalance refers to classification problems in which many more
instances are available for certain classes than for others. Such imbalanced
datasets require special attention because traditional classifiers generally
favor the majority class which has a large number of instances. Ensemble of
classifiers have been... | computer science |
7,788 | Deep reinforcement learning for time series: playing idealized trading
games | cs.LG | Deep Q-learning is investigated as an end-to-end solution to estimate the
optimal strategies for acting on time series input. Experiments are conducted
on two idealized trading games. 1) Univariate: the only input is a wave-like
price time series, and 2) Bivariate: the input includes a random stepwise price
time series... | computer science |
7,789 | Multi-objective Contextual Bandit Problem with Similarity Information | stat.ML | In this paper we propose the multi-objective contextual bandit problem with
similarity information. This problem extends the classical contextual bandit
problem with similarity information by introducing multiple and possibly
conflicting objectives. Since the best arm in each objective can be different
given the contex... | computer science |
7,790 | Interpreting Deep Classifier by Visual Distillation of Dark Knowledge | cs.LG | Interpreting black box classifiers, such as deep networks, allows an analyst
to validate a classifier before it is deployed in a high-stakes setting. A
natural idea is to visualize the deep network's representations, so as to "see
what the network sees". In this paper, we demonstrate that standard dimension
reduction m... | computer science |
7,791 | Representation Learning over Dynamic Graphs | cs.LG | How can we effectively encode evolving information over dynamic graphs into
low-dimensional representations? In this paper, we propose DyRep, an inductive
deep representation learning framework that learns a set of functions to
efficiently produce low-dimensional node embeddings that evolves over time. The
learned embe... | computer science |
7,792 | Pseudo-task Augmentation: From Deep Multitask Learning to Intratask
Sharing---and Back | cs.LG | Deep multitask learning boosts performance by sharing learned structure
across related tasks. This paper adapts ideas from deep multitask learning to
the setting where only a single task is available. The method is formalized as
pseudo-task augmentation, in which models are trained with multiple decoders
for each task.... | computer science |
7,793 | Learning Binary Bayesian Networks in Polynomial Time and Sample
Complexity | cs.LG | We consider the problem of structure learning for binary Bayesian networks.
Our approach is to recover the true parents and children for each node first
and then combine the results to recover the skeleton. We do not assume any
specific probability distribution for the nodes. Rather, we show that if the
probability dis... | computer science |
7,794 | R3Net: Random Weights, Rectifier Linear Units and Robustness for
Artificial Neural Network | stat.ML | We consider a neural network architecture with randomized features, a
sign-splitter, followed by rectified linear units (ReLU). We prove that our
architecture exhibits robustness to the input perturbation: the output feature
of the neural network exhibits a Lipschitz continuity in terms of the input
perturbation. We fu... | computer science |
7,795 | Multi-kernel Regression For Graph Signal Processing | stat.ML | We develop a multi-kernel based regression method for graph signal processing
where the target signal is assumed to be smooth over a graph. In multi-kernel
regression, an effective kernel function is expressed as a linear combination
of many basis kernel functions. We estimate the linear weights to learn the
effective ... | computer science |
7,796 | Semiparametric Contextual Bandits | stat.ML | This paper studies semiparametric contextual bandits, a generalization of the
linear stochastic bandit problem where the reward for an action is modeled as a
linear function of known action features confounded by an non-linear
action-independent term. We design new algorithms that achieve
$\tilde{O}(d\sqrt{T})$ regret ... | computer science |
7,797 | Neural Conditional Gradients | cs.LG | The move from hand-designed to learned optimizers in machine learning has
been quite successful for gradient-based and -free optimizers. When facing a
constrained problem, however, maintaining feasibility typically requires a
projection step, which might be computationally expensive and not
differentiable. We show how ... | computer science |
7,798 | Delayed Impact of Fair Machine Learning | cs.LG | Fairness in machine learning has predominantly been studied in static
classification settings without concern for how decisions change the underlying
population over time. Conventional wisdom suggests that fairness criteria
promote the long-term well-being of those groups they aim to protect.
We study how static fair... | computer science |
7,799 | Flipout: Efficient Pseudo-Independent Weight Perturbations on
Mini-Batches | cs.LG | Stochastic neural net weights are used in a variety of contexts, including
regularization, Bayesian neural nets, exploration in reinforcement learning,
and evolution strategies. Unfortunately, due to the large number of weights,
all the examples in a mini-batch typically share the same weight perturbation,
thereby limi... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.