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6,100 | Predicting the evolution of stationary graph signals | stat.ML | An emerging way of tackling the dimensionality issues arising in the modeling
of a multivariate process is to assume that the inherent data structure can be
captured by a graph. Nevertheless, though state-of-the-art graph-based methods
have been successful for many learning tasks, they do not consider
time-evolving sig... | computer science |
6,101 | Incomplete Pivoted QR-based Dimensionality Reduction | cs.LG | High-dimensional big data appears in many research fields such as image
recognition, biology and collaborative filtering. Often, the exploration of
such data by classic algorithms is encountered with difficulties due to `curse
of dimensionality' phenomenon. Therefore, dimensionality reduction methods are
applied to the... | computer science |
6,102 | Nystrom Method for Approximating the GMM Kernel | stat.ML | The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a
measure of data similarity and was demonstrated effective in machine learning
tasks. In order to use the GMM kernel for large-scale datasets, the prior work
resorted to the (generalized) consistent weighted sampling (GCWS) to convert
the GMM ker... | computer science |
6,103 | Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile
Health Applications | stat.ML | The field of mobile health aims to leverage recent advances in wearable
on-body sensing technology and smart phone computing capabilities to develop
systems that can monitor health states and deliver just-in-time adaptive
interventions. However, existing work has largely focused on analyzing
collected data in the off-l... | computer science |
6,104 | Feature Extraction and Automated Classification of Heartbeats by Machine
Learning | stat.ML | We present algorithms for the detection of a class of heart arrhythmias with
the goal of eventual adoption by practicing cardiologists. In clinical
practice, detection is based on a small number of meaningful features extracted
from the heartbeat cycle. However, techniques proposed in the literature use
high dimensiona... | computer science |
6,105 | Learning Unitary Operators with Help From u(n) | stat.ML | A major challenge in the training of recurrent neural networks is the
so-called vanishing or exploding gradient problem. The use of a norm-preserving
transition operator can address this issue, but parametrization is challenging.
In this work we focus on unitary operators and describe a parametrization using
the Lie al... | computer science |
6,106 | Geometric Mean Metric Learning | stat.ML | We revisit the task of learning a Euclidean metric from data. We approach
this problem from first principles and formulate it as a surprisingly simple
optimization problem. Indeed, our formulation even admits a closed form
solution. This solution possesses several very attractive properties: (i) an
innate geometric app... | computer science |
6,107 | A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average
Reward | stat.ML | We develop an off-policy actor-critic algorithm for learning an optimal
policy from a training set composed of data from multiple individuals. This
algorithm is developed with a view towards its use in mobile health. | computer science |
6,108 | On the Identification and Mitigation of Weaknesses in the Knowledge
Gradient Policy for Multi-Armed Bandits | stat.ML | The Knowledge Gradient (KG) policy was originally proposed for online ranking
and selection problems but has recently been adapted for use in online decision
making in general and multi-armed bandit problems (MABs) in particular. We
study its use in a class of exponential family MABs and identify weaknesses,
including ... | computer science |
6,109 | Explaining Classification Models Built on High-Dimensional Sparse Data | stat.ML | Predictive modeling applications increasingly use data representing people's
behavior, opinions, and interactions. Fine-grained behavior data often has
different structure from traditional data, being very high-dimensional and
sparse. Models built from these data are quite difficult to interpret, since
they contain man... | computer science |
6,110 | Hierarchical Clustering of Asymmetric Networks | cs.LG | This paper considers networks where relationships between nodes are
represented by directed dissimilarities. The goal is to study methods that,
based on the dissimilarity structure, output hierarchical clusters, i.e., a
family of nested partitions indexed by a connectivity parameter. Our
construction of hierarchical cl... | computer science |
6,111 | Uncovering Causality from Multivariate Hawkes Integrated Cumulants | stat.ML | We design a new nonparametric method that allows one to estimate the matrix
of integrated kernels of a multivariate Hawkes process. This matrix not only
encodes the mutual influences of each nodes of the process, but also
disentangles the causality relationships between them. Our approach is the
first that leads to an ... | computer science |
6,112 | Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric
Networks | cs.LG | This paper characterizes hierarchical clustering methods that abide by two
previously introduced axioms -- thus, denominated admissible methods -- and
proposes tractable algorithms for their implementation. We leverage the fact
that, for asymmetric networks, every admissible method must be contained
between reciprocal ... | computer science |
6,113 | Distributed Supervised Learning using Neural Networks | stat.ML | Distributed learning is the problem of inferring a function in the case where
training data is distributed among multiple geographically separated sources.
Particularly, the focus is on designing learning strategies with low
computational requirements, in which communication is restricted only to
neighboring agents, wi... | computer science |
6,114 | Layer Normalization | stat.ML | Training state-of-the-art, deep neural networks is computationally expensive.
One way to reduce the training time is to normalize the activities of the
neurons. A recently introduced technique called batch normalization uses the
distribution of the summed input to a neuron over a mini-batch of training
cases to compute... | computer science |
6,115 | On the Use of Sparse Filtering for Covariate Shift Adaptation | cs.LG | In this paper we formally analyse the use of sparse filtering algorithms to
perform covariate shift adaptation. We provide a theoretical analysis of sparse
filtering by evaluating the conditions required to perform covariate shift
adaptation. We prove that sparse filtering can perform adaptation only if the
conditional... | computer science |
6,116 | Interactive Learning from Multiple Noisy Labels | cs.LG | Interactive learning is a process in which a machine learning algorithm is
provided with meaningful, well-chosen examples as opposed to randomly chosen
examples typical in standard supervised learning. In this paper, we propose a
new method for interactive learning from multiple noisy labels where we exploit
the disagr... | computer science |
6,117 | Scaling Up Sparse Support Vector Machines by Simultaneous Feature and
Sample Reduction | stat.ML | Sparse support vector machine (SVM) is a popular classification technique
that can simultaneously learn a small set of the most interpretable features
and identify the support vectors. It has achieved great successes in many
real-world applications. However, for large-scale problems involving a huge
number of samples a... | computer science |
6,118 | Higher-Order Factorization Machines | stat.ML | Factorization machines (FMs) are a supervised learning approach that can use
second-order feature combinations even when the data is very high-dimensional.
Unfortunately, despite increasing interest in FMs, there exists to date no
efficient training algorithm for higher-order FMs (HOFMs). In this paper, we
present the ... | computer science |
6,119 | Deepr: A Convolutional Net for Medical Records | stat.ML | Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that ... | computer science |
6,120 | Using Kernel Methods and Model Selection for Prediction of Preterm Birth | cs.LG | We describe an application of machine learning to the problem of predicting
preterm birth. We conduct a secondary analysis on a clinical trial dataset
collected by the National In- stitute of Child Health and Human Development
(NICHD) while focusing our attention on predicting different classes of preterm
birth. We com... | computer science |
6,121 | Convolutional Neural Networks Analyzed via Convolutional Sparse Coding | stat.ML | Convolutional neural networks (CNN) have led to many state-of-the-art results
spanning through various fields. However, a clear and profound theoretical
understanding of the forward pass, the core algorithm of CNN, is still lacking.
In parallel, within the wide field of sparse approximation, Convolutional
Sparse Coding... | computer science |
6,122 | A Non-Parametric Learning Approach to Identify Online Human Trafficking | cs.LG | Human trafficking is among the most challenging law enforcement problems
which demands persistent fight against from all over the globe. In this study,
we leverage readily available data from the website "Backpage"-- used for
classified advertisement-- to discern potential patterns of human trafficking
activities which... | computer science |
6,123 | Polynomial Networks and Factorization Machines: New Insights and
Efficient Training Algorithms | stat.ML | Polynomial networks and factorization machines are two recently-proposed
models that can efficiently use feature interactions in classification and
regression tasks. In this paper, we revisit both models from a unified
perspective. Based on this new view, we study the properties of both models and
propose new efficient... | computer science |
6,124 | gLOP: the global and Local Penalty for Capturing Predictive
Heterogeneity | stat.ML | When faced with a supervised learning problem, we hope to have rich enough
data to build a model that predicts future instances well. However, in
practice, problems can exhibit predictive heterogeneity: most instances might
be relatively easy to predict, while others might be predictive outliers for
which a model train... | computer science |
6,125 | Learning Tree-Structured Detection Cascades for Heterogeneous Networks
of Embedded Devices | stat.ML | In this paper, we present a new approach to learning cascaded classifiers for
use in computing environments that involve networks of heterogeneous and
resource-constrained, low-power embedded compute and sensing nodes. We present
a generalization of the classical linear detection cascade to the case of
tree-structured ... | computer science |
6,126 | On Regularization Parameter Estimation under Covariate Shift | cs.LG | This paper identifies a problem with the usual procedure for
L2-regularization parameter estimation in a domain adaptation setting. In such
a setting, there are differences between the distributions generating the
training data (source domain) and the test data (target domain). The usual
cross-validation procedure requ... | computer science |
6,127 | Recursion-Free Online Multiple Incremental/Decremental Analysis Based on
Ridge Support Vector Learning | cs.LG | This study presents a rapid multiple incremental and decremental mechanism
based on Weight-Error Curves (WECs) for support-vector analysis. Recursion-free
computation is proposed for predicting the Lagrangian multipliers of new
samples. This study examines Ridge Support Vector Models, subsequently devising
a recursion-... | computer science |
6,128 | Efficient Multiple Incremental Computation for Kernel Ridge Regression
with Bayesian Uncertainty Modeling | cs.LG | This study presents an efficient incremental/decremental approach for big
streams based on Kernel Ridge Regression (KRR), a frequently used data analysis
in cloud centers. To avoid reanalyzing the whole dataset whenever sensors
receive new training data, typical incremental KRR used a single-instance
mechanism for upda... | computer science |
6,129 | Clinical Tagging with Joint Probabilistic Models | stat.ML | We describe a method for parameter estimation in bipartite probabilistic
graphical models for joint prediction of clinical conditions from the
electronic medical record. The method does not rely on the availability of
gold-standard labels, but rather uses noisy labels, called anchors, for
learning. We provide a likelih... | computer science |
6,130 | Identifiable Phenotyping using Constrained Non-Negative Matrix
Factorization | stat.ML | This work proposes a new algorithm for automated and simultaneous phenotyping
of multiple co-occurring medical conditions, also referred as comorbidities,
using clinical notes from the electronic health records (EHRs). A basic latent
factor estimation technique of non-negative matrix factorization (NMF) is
augmented wi... | computer science |
6,131 | Exponential Family Embeddings | stat.ML | Word embeddings are a powerful approach for capturing semantic similarity
among terms in a vocabulary. In this paper, we develop exponential family
embeddings, a class of methods that extends the idea of word embeddings to
other types of high-dimensional data. As examples, we studied neural data with
real-valued observ... | computer science |
6,132 | Hierarchically Compositional Kernels for Scalable Nonparametric Learning | cs.LG | We propose a novel class of kernels to alleviate the high computational cost
of large-scale nonparametric learning with kernel methods. The proposed kernel
is defined based on a hierarchical partitioning of the underlying data domain,
where the Nystr\"om method (a globally low-rank approximation) is married with
a loca... | computer science |
6,133 | Learning a Driving Simulator | cs.LG | Comma.ai's approach to Artificial Intelligence for self-driving cars is based
on an agent that learns to clone driver behaviors and plans maneuvers by
simulating future events in the road. This paper illustrates one of our
research approaches for driving simulation. One where we learn to simulate.
Here we investigate v... | computer science |
6,134 | Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression | cs.LG | We propose Bayesian extensions of two nonparametric regression methods which
are kernel and mutual $k$-nearest neighbor regression methods. Derived based on
Gaussian process models for regression, the extensions provide distributions
for target value estimates and the framework to select the hyperparameters. It
is show... | computer science |
6,135 | A Distance for HMMs based on Aggregated Wasserstein Metric and State
Registration | cs.LG | We propose a framework, named Aggregated Wasserstein, for computing a
dissimilarity measure or distance between two Hidden Markov Models with state
conditional distributions being Gaussian. For such HMMs, the marginal
distribution at any time spot follows a Gaussian mixture distribution, a fact
exploited to softly matc... | computer science |
6,136 | Kernel Ridge Regression via Partitioning | stat.ML | In this paper, we investigate a divide and conquer approach to Kernel Ridge
Regression (KRR). Given n samples, the division step involves separating the
points based on some underlying disjoint partition of the input space (possibly
via clustering), and then computing a KRR estimate for each partition. The
conquering s... | computer science |
6,137 | Posterior Sampling for Reinforcement Learning Without Episodes | stat.ML | This is a brief technical note to clarify some of the issues with applying
the application of the algorithm posterior sampling for reinforcement learning
(PSRL) in environments without fixed episodes. In particular, this paper aims
to:
- Review some of results which have been proven for finite horizon MDPs
(Osband et... | computer science |
6,138 | On Lower Bounds for Regret in Reinforcement Learning | stat.ML | This is a brief technical note to clarify the state of lower bounds on regret
for reinforcement learning. In particular, this paper:
- Reproduces a lower bound on regret for reinforcement learning, similar to
the result of Theorem 5 in the journal UCRL2 paper (Jaksch et al 2010).
- Clarifies that the proposed proof... | computer science |
6,139 | Classification with the pot-pot plot | stat.ML | We propose a procedure for supervised classification that is based on
potential functions. The potential of a class is defined as a kernel density
estimate multiplied by the class's prior probability. The method transforms the
data to a potential-potential (pot-pot) plot, where each data point is mapped
to a vector of ... | computer science |
6,140 | Stochastic Rank-1 Bandits | cs.LG | We propose stochastic rank-$1$ bandits, a class of online learning problems
where at each step a learning agent chooses a pair of row and column arms, and
receives the product of their values as a reward. The main challenge of the
problem is that the individual values of the row and column are unobserved. We
assume tha... | computer science |
6,141 | Estimation from Indirect Supervision with Linear Moments | stat.ML | In structured prediction problems where we have indirect supervision of the
output, maximum marginal likelihood faces two computational obstacles:
non-convexity of the objective and intractability of even a single gradient
computation. In this paper, we bypass both obstacles for a class of what we
call linear indirectl... | computer science |
6,142 | Temporal Learning and Sequence Modeling for a Job Recommender System | cs.LG | We present our solution to the job recommendation task for RecSys Challenge
2016. The main contribution of our work is to combine temporal learning with
sequence modeling to capture complex user-item activity patterns to improve job
recommendations. First, we propose a time-based ranking model applied to
historical obs... | computer science |
6,143 | Distributed learning with regularized least squares | cs.LG | We study distributed learning with the least squares regularization scheme in
a reproducing kernel Hilbert space (RKHS). By a divide-and-conquer approach,
the algorithm partitions a data set into disjoint data subsets, applies the
least squares regularization scheme to each data subset to produce an output
function, an... | computer science |
6,144 | Sequence Graph Transform (SGT): A Feature Extraction Function for
Sequence Data Mining (Extended Version) | stat.ML | The ubiquitous presence of sequence data across fields such as the web,
healthcare, bioinformatics, and text mining has made sequence mining a vital
research area. However, sequence mining is particularly challenging because of
difficulty in finding (dis)similarity/distance between sequences. This is
because a distance... | computer science |
6,145 | Bayesian Model Selection Methods for Mutual and Symmetric $k$-Nearest
Neighbor Classification | cs.LG | The $k$-nearest neighbor classification method ($k$-NNC) is one of the
simplest nonparametric classification methods. The mutual $k$-NN classification
method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We
propose another variant of $k$-NNC, the symmetric $k$-NN classification method
(S$k$NNC) based ... | computer science |
6,146 | The Bayesian Low-Rank Determinantal Point Process Mixture Model | stat.ML | Determinantal point processes (DPPs) are an elegant model for encoding
probabilities over subsets, such as shopping baskets, of a ground set, such as
an item catalog. They are useful for a number of machine learning tasks,
including product recommendation. DPPs are parametrized by a positive
semi-definite kernel matrix... | computer science |
6,147 | Consistency constraints for overlapping data clustering | cs.LG | We examine overlapping clustering schemes with functorial constraints, in the
spirit of Carlsson--Memoli. This avoids issues arising from the chaining
required by partition-based methods. Our principal result shows that any
clustering functor is naturally constrained to refine single-linkage clusters
and be refined by ... | computer science |
6,148 | Generalization of ERM in Stochastic Convex Optimization: The Dimension
Strikes Back | cs.LG | In stochastic convex optimization the goal is to minimize a convex function
$F(x) \doteq {\mathbf E}_{{\mathbf f}\sim D}[{\mathbf f}(x)]$ over a convex set
$\cal K \subset {\mathbb R}^d$ where $D$ is some unknown distribution and each
$f(\cdot)$ in the support of $D$ is convex over $\cal K$. The optimization is
commonl... | computer science |
6,149 | Stein Variational Gradient Descent: A General Purpose Bayesian Inference
Algorithm | stat.ML | We propose a general purpose variational inference algorithm that forms a
natural counterpart of gradient descent for optimization. Our method
iteratively transports a set of particles to match the target distribution, by
applying a form of functional gradient descent that minimizes the KL
divergence. Empirical studies... | computer science |
6,150 | A novel transfer learning method based on common space mapping and
weighted domain matching | cs.LG | In this paper, we propose a novel learning framework for the problem of
domain transfer learning. We map the data of two domains to one single common
space, and learn a classifier in this common space. Then we adapt the common
classifier to the two domains by adding two adaptive functions to it
respectively. In the com... | computer science |
6,151 | Application of multiview techniques to NHANES dataset | cs.LG | Disease prediction or classification using health datasets involve using
well-known predictors associated with the disease as features for the models.
This study considers multiple data components of an individual's health, using
the relationship between variables to generate features that may improve the
performance o... | computer science |
6,152 | Scalable Learning of Non-Decomposable Objectives | stat.ML | Modern retrieval systems are often driven by an underlying machine learning
model. The goal of such systems is to identify and possibly rank the few most
relevant items for a given query or context. Thus, such systems are typically
evaluated using a ranking-based performance metric such as the area under the
precision-... | computer science |
6,153 | Outlier Detection on Mixed-Type Data: An Energy-based Approach | stat.ML | Outlier detection amounts to finding data points that differ significantly
from the norm. Classic outlier detection methods are largely designed for
single data type such as continuous or discrete. However, real world data is
increasingly heterogeneous, where a data point can have both discrete and
continuous attribute... | computer science |
6,154 | A Bayesian Nonparametric Approach for Estimating Individualized
Treatment-Response Curves | cs.LG | We study the problem of estimating the continuous response over time to
interventions using observational time series---a retrospective dataset where
the policy by which the data are generated is unknown to the learner. We are
motivated by applications where response varies by individuals and therefore,
estimating resp... | computer science |
6,155 | Active Learning for Approximation of Expensive Functions with Normal
Distributed Output Uncertainty | cs.LG | When approximating a black-box function, sampling with active learning
focussing on regions with non-linear responses tends to improve accuracy. We
present the FLOLA-Voronoi method introduced previously for deterministic
responses, and theoretically derive the impact of output uncertainty. The
algorithm automatically p... | computer science |
6,156 | Parameter Learning for Log-supermodular Distributions | stat.ML | We consider log-supermodular models on binary variables, which are
probabilistic models with negative log-densities which are submodular. These
models provide probabilistic interpretations of common combinatorial
optimization tasks such as image segmentation. In this paper, we focus
primarily on parameter estimation in... | computer science |
6,157 | A Tight Convex Upper Bound on the Likelihood of a Finite Mixture | cs.LG | The likelihood function of a finite mixture model is a non-convex function
with multiple local maxima and commonly used iterative algorithms such as EM
will converge to different solutions depending on initial conditions. In this
paper we ask: is it possible to assess how far we are from the global maximum
of the likel... | computer science |
6,158 | Iterative Views Agreement: An Iterative Low-Rank based Structured
Optimization Method to Multi-View Spectral Clustering | cs.LG | Multi-view spectral clustering, which aims at yielding an agreement or
consensus data objects grouping across multi-views with their graph laplacian
matrices, is a fundamental clustering problem. Among the existing methods,
Low-Rank Representation (LRR) based method is quite superior in terms of its
effectiveness, intu... | computer science |
6,159 | Unsupervised Feature Selection Based on the Morisita Estimator of
Intrinsic Dimension | stat.ML | This paper deals with a new filter algorithm for selecting the smallest
subset of features carrying all the information content of a data set (i.e. for
removing redundant features). It is an advanced version of the fractal
dimension reduction technique, and it relies on the recently introduced
Morisita estimator of Int... | computer science |
6,160 | A Strongly Quasiconvex PAC-Bayesian Bound | cs.LG | We propose a new PAC-Bayesian bound and a way of constructing a hypothesis
space, so that the bound is convex in the posterior distribution and also
convex in a trade-off parameter between empirical performance of the posterior
distribution and its complexity. The complexity is measured by the
Kullback-Leibler divergen... | computer science |
6,161 | Inverting Variational Autoencoders for Improved Generative Accuracy | cs.LG | Recent advances in semi-supervised learning with deep generative models have
shown promise in generalizing from small labeled datasets
($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$). In the case
where the codomain has known structure, a large unfeatured dataset
($\mathbf{y}$) is potentially available.... | computer science |
6,162 | A Non-convex One-Pass Framework for Generalized Factorization Machine
and Rank-One Matrix Sensing | stat.ML | We develop an efficient alternating framework for learning a generalized
version of Factorization Machine (gFM) on steaming data with provable
guarantees. When the instances are sampled from $d$ dimensional random Gaussian
vectors and the target second order coefficient matrix in gFM is of rank $k$,
our algorithm conve... | computer science |
6,163 | Comparison among dimensionality reduction techniques based on Random
Projection for cancer classification | cs.LG | Random Projection (RP) technique has been widely applied in many scenarios
because it can reduce high-dimensional features into low-dimensional space
within short time and meet the need of real-time analysis of massive data.
There is an urgent need of dimensionality reduction with fast increase of big
genomics data. Ho... | computer science |
6,164 | Large-scale Collaborative Imaging Genetics Studies of Risk Genetic
Factors for Alzheimer's Disease Across Multiple Institutions | cs.LG | Genome-wide association studies (GWAS) offer new opportunities to identify
genetic risk factors for Alzheimer's disease (AD). Recently, collaborative
efforts across different institutions emerged that enhance the power of many
existing techniques on individual institution data. However, a major barrier to
collaborative... | computer science |
6,165 | Leveraging over intact priors for boosting control and dexterity of
prosthetic hands by amputees | cs.LG | Non-invasive myoelectric prostheses require a long training time to obtain
satisfactory control dexterity. These training times could possibly be reduced
by leveraging over training efforts by previous subjects. So-called domain
adaptation algorithms formalize this strategy and have indeed been shown to
significantly r... | computer science |
6,166 | Learning Temporal Dependence from Time-Series Data with Latent Variables | cs.LG | We consider the setting where a collection of time series, modeled as random
processes, evolve in a causal manner, and one is interested in learning the
graph governing the relationships of these processes. A special case of wide
interest and applicability is the setting where the noise is Gaussian and
relationships ar... | computer science |
6,167 | A Boundary Tilting Persepective on the Phenomenon of Adversarial
Examples | cs.LG | Deep neural networks have been shown to suffer from a surprising weakness:
their classification outputs can be changed by small, non-random perturbations
of their inputs. This adversarial example phenomenon has been explained as
originating from deep networks being "too linear" (Goodfellow et al., 2014). We
show here t... | computer science |
6,168 | Random Forest for Label Ranking | cs.LG | Label ranking aims to learn a mapping from instances to rankings over a
finite number of predefined labels. Random forest is a powerful and one of the
most successfully general-purpose machine learning algorithms of modern times.
In the literature, there seems no research has yet been done in applying random
forest to ... | computer science |
6,169 | Bayesian selection for the l2-Potts model regularization parameter: 1D
piecewise constant signal denoising | cs.LG | Piecewise constant denoising can be solved either by deterministic
optimization approaches, based on the Potts model, or by stochastic Bayesian
procedures. The former lead to low computational time but require the selection
of a regularization parameter, whose value significantly impacts the achieved
solution, and whos... | computer science |
6,170 | Optimizing Recurrent Neural Networks Architectures under Time
Constraints | stat.ML | Recurrent neural network (RNN)'s architecture is a key factor influencing its
performance. We propose algorithms to optimize hidden sizes under running time
constraint. We convert the discrete optimization into a subset selection
problem. By novel transformations, the objective function becomes submodular
and constrain... | computer science |
6,171 | Relevant based structure learning for feature selection | cs.LG | Feature selection is an important task in many problems occurring in pattern
recognition, bioinformatics, machine learning and data mining applications. The
feature selection approach enables us to reduce the computation burden and the
falling accuracy effect of dealing with huge number of features in typical
learning ... | computer science |
6,172 | Robust Discriminative Clustering with Sparse Regularizers | stat.ML | Clustering high-dimensional data often requires some form of dimensionality
reduction, where clustered variables are separated from "noise-looking"
variables. We cast this problem as finding a low-dimensional projection of the
data which is well-clustered. This yields a one-dimensional projection in the
simplest situat... | computer science |
6,173 | Wasserstein Discriminant Analysis | stat.ML | Wasserstein Discriminant Analysis (WDA) is a new supervised method that can
improve classification of high-dimensional data by computing a suitable linear
map onto a lower dimensional subspace. Following the blueprint of classical
Linear Discriminant Analysis (LDA), WDA selects the projection matrix that
maximizes the ... | computer science |
6,174 | Visualizing and Understanding Sum-Product Networks | cs.LG | Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this pa... | computer science |
6,175 | hi-RF: Incremental Learning Random Forest for large-scale multi-class
Data Classification | cs.LG | In recent years, dynamically growing data and incrementally growing number of
classes pose new challenges to large-scale data classification research. Most
traditional methods struggle to balance the precision and computational burden
when data and its number of classes increased. However, some methods are with
weak pr... | computer science |
6,176 | Recursive Partitioning for Personalization using Observational Data | stat.ML | We study the problem of learning to choose from m discrete treatment options
(e.g., news item or medical drug) the one with best causal effect for a
particular instance (e.g., user or patient) where the training data consists of
passive observations of covariates, treatment, and the outcome of the
treatment. The standa... | computer science |
6,177 | Towards Competitive Classifiers for Unbalanced Classification Problems:
A Study on the Performance Scores | stat.ML | Although a great methodological effort has been invested in proposing
competitive solutions to the class-imbalance problem, little effort has been
made in pursuing a theoretical understanding of this matter.
In order to shed some light on this topic, we perform, through a novel
framework, an exhaustive analysis of th... | computer science |
6,178 | A Tutorial on Online Supervised Learning with Applications to Node
Classification in Social Networks | cs.LG | We revisit the elegant observation of T. Cover '65 which, perhaps, is not as
well-known to the broader community as it should be. The first goal of the
tutorial is to explain---through the prism of this elementary result---how to
solve certain sequence prediction problems by modeling sets of solutions rather
than the u... | computer science |
6,179 | Neural Network Architecture Optimization through Submodularity and
Supermodularity | stat.ML | Deep learning models' architectures, including depth and width, are key
factors influencing models' performance, such as test accuracy and computation
time. This paper solves two problems: given computation time budget, choose an
architecture to maximize accuracy, and given accuracy requirement, choose an
architecture ... | computer science |
6,180 | Graph-Based Active Learning: A New Look at Expected Error Minimization | stat.ML | In graph-based active learning, algorithms based on expected error
minimization (EEM) have been popular and yield good empirical performance. The
exact computation of EEM optimally balances exploration and exploitation. In
practice, however, EEM-based algorithms employ various approximations due to
the computational ha... | computer science |
6,181 | The Robustness of Estimator Composition | cs.LG | We formalize notions of robustness for composite estimators via the notion of
a breakdown point. A composite estimator successively applies two (or more)
estimators: on data decomposed into disjoint parts, it applies the first
estimator on each part, then the second estimator on the outputs of the first
estimator. And ... | computer science |
6,182 | Direct Feedback Alignment Provides Learning in Deep Neural Networks | stat.ML | Artificial neural networks are most commonly trained with the
back-propagation algorithm, where the gradient for learning is provided by
back-propagating the error, layer by layer, from the output layer to the hidden
layers. A recently discovered method called feedback-alignment shows that the
weights used for propagat... | computer science |
6,183 | Learning Boltzmann Machine with EM-like Method | cs.LG | We propose an expectation-maximization-like(EMlike) method to train Boltzmann
machine with unconstrained connectivity. It adopts Monte Carlo approximation in
the E-step, and replaces the intractable likelihood objective with efficiently
computed objectives or directly approximates the gradient of likelihood
objective i... | computer science |
6,184 | Chaining Bounds for Empirical Risk Minimization | stat.ML | This paper extends the standard chaining technique to prove excess risk upper
bounds for empirical risk minimization with random design settings even if the
magnitude of the noise and the estimates is unbounded. The bound applies to
many loss functions besides the squared loss, and scales only with the
sub-Gaussian or ... | computer science |
6,185 | Random matrices meet machine learning: a large dimensional analysis of
LS-SVM | stat.ML | This article proposes a performance analysis of kernel least squares support
vector machines (LS-SVMs) based on a random matrix approach, in the regime
where both the dimension of data $p$ and their number $n$ grow large at the
same rate. Under a two-class Gaussian mixture model for the input data, we
prove that the LS... | computer science |
6,186 | Discrete Variational Autoencoders | stat.ML | Probabilistic models with discrete latent variables naturally capture
datasets composed of discrete classes. However, they are difficult to train
efficiently, since backpropagation through discrete variables is generally not
possible. We present a novel method to train a class of probabilistic models
with discrete late... | computer science |
6,187 | Functorial Hierarchical Clustering with Overlaps | cs.LG | This work draws its inspiration from three important sources of research on
dissimilarity-based clustering and intertwines those three threads into a
consistent principled functorial theory of clustering. Those three are the
overlapping clustering of Jardine and Sibson, the functorial approach of
Carlsson and Memoli to... | computer science |
6,188 | DiSMEC - Distributed Sparse Machines for Extreme Multi-label
Classification | stat.ML | Extreme multi-label classification refers to supervised multi-label learning
involving hundreds of thousands or even millions of labels. Datasets in extreme
classification exhibit fit to power-law distribution, i.e. a large fraction of
labels have very few positive instances in the data distribution. Most
state-of-the-... | computer science |
6,189 | On Sequential Elimination Algorithms for Best-Arm Identification in
Multi-Armed Bandits | stat.ML | We consider the best-arm identification problem in multi-armed bandits, which
focuses purely on exploration. A player is given a fixed budget to explore a
finite set of arms, and the rewards of each arm are drawn independently from a
fixed, unknown distribution. The player aims to identify the arm with the
largest expe... | computer science |
6,190 | Distributed Processing of Biosignal-Database for Emotion Recognition
with Mahout | stat.ML | This paper investigates the use of distributed processing on the problem of
emotion recognition from physiological sensors using a popular machine learning
library on distributed mode. Specifically, we run a random forests classifier
on the biosignal-data, which have been pre-processed to form exclusive groups
in an un... | computer science |
6,191 | Semi-Supervised Classification with Graph Convolutional Networks | cs.LG | We present a scalable approach for semi-supervised learning on
graph-structured data that is based on an efficient variant of convolutional
neural networks which operate directly on graphs. We motivate the choice of our
convolutional architecture via a localized first-order approximation of
spectral graph convolutions.... | computer science |
6,192 | Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal
Component Analysis | stat.ML | Principal component analysis (PCA) is often used to reduce the dimension of
data by selecting a few orthonormal vectors that explain most of the variance
structure of the data. L1 PCA uses the L1 norm to measure error, whereas the
conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the
fitting error of... | computer science |
6,193 | Energy-based Generative Adversarial Network | cs.LG | We introduce the "Energy-based Generative Adversarial Network" model (EBGAN)
which views the discriminator as an energy function that attributes low
energies to the regions near the data manifold and higher energies to other
regions. Similar to the probabilistic GANs, a generator is seen as being
trained to produce con... | computer science |
6,194 | Sharing Hash Codes for Multiple Purposes | stat.ML | Locality sensitive hashing (LSH) is a powerful tool for sublinear-time
approximate nearest neighbor search, and a variety of hashing schemes have been
proposed for different dissimilarity measures. However, hash codes
significantly depend on the dissimilarity, which prohibits users from adjusting
the dissimilarity at q... | computer science |
6,195 | CompAdaGrad: A Compressed, Complementary, Computationally-Efficient
Adaptive Gradient Method | cs.LG | The adaptive gradient online learning method known as AdaGrad has seen
widespread use in the machine learning community in stochastic and adversarial
online learning problems and more recently in deep learning methods. The
method's full-matrix incarnation offers much better theoretical guarantees and
potentially better... | computer science |
6,196 | Online Data Thinning via Multi-Subspace Tracking | stat.ML | In an era of ubiquitous large-scale streaming data, the availability of data
far exceeds the capacity of expert human analysts. In many settings, such data
is either discarded or stored unprocessed in datacenters. This paper proposes a
method of online data thinning, in which large-scale streaming datasets are
winnowed... | computer science |
6,197 | A Greedy Algorithm to Cluster Specialists | cs.LG | Several recent deep neural networks experiments leverage the
generalist-specialist paradigm for classification. However, no formal study
compared the performance of different clustering algorithms for class
assignment. In this paper we perform such a study, suggest slight modifications
to the clustering procedures, and... | computer science |
6,198 | Making Deep Neural Networks Robust to Label Noise: a Loss Correction
Approach | stat.ML | We present a theoretically grounded approach to train deep neural networks,
including recurrent networks, subject to class-dependent label noise. We
propose two procedures for loss correction that are agnostic to both
application domain and network architecture. They simply amount to at most a
matrix inversion and mult... | computer science |
6,199 | Learning conditional independence structure for high-dimensional
uncorrelated vector processes | stat.ML | We formulate and analyze a graphical model selection method for inferring the
conditional independence graph of a high-dimensional nonstationary Gaussian
random process (time series) from a finite-length observation. The observed
process samples are assumed uncorrelated over time and having a time-varying
marginal dist... | computer science |
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