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6,200 | Very Simple Classifier: a Concept Binary Classifier toInvestigate
Features Based on Subsampling and Localility | cs.LG | We propose Very Simple Classifier (VSC) a novel method designed to
incorporate the concepts of subsampling and locality in the definition of
features to be used as the input of a perceptron. The rationale is that
locality theoretically guarantees a bound on the generalization error. Each
feature in VSC is a max-margin ... | computer science |
6,201 | Recursive nearest agglomeration (ReNA): fast clustering for
approximation of structured signals | stat.ML | In this work, we revisit fast dimension reduction approaches, as with random
projections and random sampling. Our goal is to summarize the data to decrease
computational costs and memory footprint of subsequent analysis. Such dimension
reduction can be very efficient when the signals of interest have a strong
structure... | computer science |
6,202 | Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis | cs.LG | This paper presents a remarkably simple, yet powerful, algorithm termed
Coherence Pursuit (CoP) to robust Principal Component Analysis (PCA). As
inliers lie in a low dimensional subspace and are mostly correlated, an inlier
is likely to have strong mutual coherence with a large number of data points.
By contrast, outli... | computer science |
6,203 | Predicting Shot Making in Basketball Learnt from Adversarial Multiagent
Trajectories | stat.ML | In this paper, we predict the likelihood of a player making a shot in
basketball from multiagent trajectories. Previous approaches to similar
problems center on hand-crafting features to capture domain specific knowledge.
Although intuitive, recent work in deep learning has shown this approach is
prone to missing impor... | computer science |
6,204 | ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings | stat.ML | Many important applications, including signal reconstruction, parameter
estimation, and signal processing in a compressed domain, rely on a
low-dimensional representation of the dataset that preserves {\em all} pairwise
distances between the data points and leverages the inherent geometric
structure that is typically p... | computer science |
6,205 | Probabilistic Feature Selection and Classification Vector Machine | cs.LG | Sparse Bayesian learning is one of the state-of- the-art machine learning
algorithms, which is able to make stable and reliable probabilistic
predictions. However, some of these algorithms, e.g. probabilistic
classification vector machine (PCVM) and relevant vector machine (RVM), are not
capable of eliminating irreleva... | computer science |
6,206 | Principled Option Learning in Markov Decision Processes | cs.LG | It is well known that options can make planning more efficient, among their
many benefits. Thus far, algorithms for autonomously discovering a set of
useful options were heuristic. Naturally, a principled way of finding a set of
useful options may be more promising and insightful. In this paper we suggest a
mathematica... | computer science |
6,207 | Sequential Ensemble Learning for Outlier Detection: A Bias-Variance
Perspective | cs.LG | Ensemble methods for classification and clustering have been effectively used
for decades, while ensemble learning for outlier detection has only been
studied recently. In this work, we design a new ensemble approach for outlier
detection in multi-dimensional point data, which provides improved accuracy by
reducing err... | computer science |
6,208 | Learning Personalized Optimal Control for Repeatedly Operated Systems | cs.LG | We consider the problem of online learning of optimal control for repeatedly
operated systems in the presence of parametric uncertainty. During each round
of operation, environment selects system parameters according to a fixed but
unknown probability distribution. These parameters govern the dynamics of a
plant. An ag... | computer science |
6,209 | On Randomized Distributed Coordinate Descent with Quantized Updates | stat.ML | In this paper, we study the randomized distributed coordinate descent
algorithm with quantized updates. In the literature, the iteration complexity
of the randomized distributed coordinate descent algorithm has been
characterized under the assumption that machines can exchange updates with an
infinite precision. We con... | computer science |
6,210 | Stochastic Matrix Factorization | stat.ML | This paper considers a restriction to non-negative matrix factorization in
which at least one matrix factor is stochastic. That is, the elements of the
matrix factors are non-negative and the columns of one matrix factor sum to 1.
This restriction includes topic models, a popular method for analyzing
unstructured data.... | computer science |
6,211 | Distributed Adaptive Learning of Graph Signals | cs.LG | The aim of this paper is to propose distributed strategies for adaptive
learning of signals defined over graphs. Assuming the graph signal to be
bandlimited, the method enables distributed reconstruction, with guaranteed
performance in terms of mean-square error, and tracking from a limited number
of sampled observatio... | computer science |
6,212 | Multiclass Classification Calibration Functions | stat.ML | In this paper we refine the process of computing calibration functions for a
number of multiclass classification surrogate losses. Calibration functions are
a powerful tool for easily converting bounds for the surrogate risk (which can
be computed through well-known methods) into bounds for the true risk, the
probabili... | computer science |
6,213 | Learning HMMs with Nonparametric Emissions via Spectral Decompositions
of Continuous Matrices | stat.ML | Recently, there has been a surge of interest in using spectral methods for
estimating latent variable models. However, it is usually assumed that the
distribution of the observations conditioned on the latent variables is either
discrete or belongs to a parametric family. In this paper, we study the
estimation of an $m... | computer science |
6,214 | On Data-Independent Properties for Density-Based Dissimilarity Measures
in Hybrid Clustering | stat.ML | Hybrid clustering combines partitional and hierarchical clustering for
computational effectiveness and versatility in cluster shape. In such
clustering, a dissimilarity measure plays a crucial role in the hierarchical
merging. The dissimilarity measure has great impact on the final clustering,
and data-independent prop... | computer science |
6,215 | Theoretical Evaluation of Feature Selection Methods based on Mutual
Information | stat.ML | Feature selection methods are usually evaluated by wrapping specific
classifiers and datasets in the evaluation process, resulting very often in
unfair comparisons between methods. In this work, we develop a theoretical
framework that allows obtaining the true feature ordering of two-dimensional
sequential forward feat... | computer science |
6,216 | Hawkes Processes with Stochastic Excitations | cs.LG | We propose an extension to Hawkes processes by treating the levels of
self-excitation as a stochastic differential equation. Our new point process
allows better approximation in application domains where events and intensities
accelerate each other with correlated levels of contagion. We generalize a
recent algorithm f... | computer science |
6,217 | Early Warning System for Seismic Events in Coal Mines Using Machine
Learning | cs.LG | This document describes an approach to the problem of predicting dangerous
seismic events in active coal mines up to 8 hours in advance. It was developed
as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic
Events in Active Coal Mines. The solutions presented consist of ensembles of
various pred... | computer science |
6,218 | (Bandit) Convex Optimization with Biased Noisy Gradient Oracles | cs.LG | Algorithms for bandit convex optimization and online learning often rely on
constructing noisy gradient estimates, which are then used in appropriately
adjusted first-order algorithms, replacing actual gradients. Depending on the
properties of the function to be optimized and the nature of "noise" in the
bandit feedbac... | computer science |
6,219 | Using Neural Network Formalism to Solve Multiple-Instance Problems | cs.LG | Many objects in the real world are difficult to describe by a single
numerical vector of a fixed length, whereas describing them by a set of vectors
is more natural. Therefore, Multiple instance learning (MIL) techniques have
been constantly gaining on importance throughout last years. MIL formalism
represents each obj... | computer science |
6,220 | Constraint-Based Clustering Selection | stat.ML | Semi-supervised clustering methods incorporate a limited amount of
supervision into the clustering process. Typically, this supervision is
provided by the user in the form of pairwise constraints. Existing methods use
such constraints in one of the following ways: they adapt their clustering
procedure, their similarity... | computer science |
6,221 | Dynamic Pricing in High-dimensions | stat.ML | We study the pricing problem faced by a firm that sells a large number of
products, described via a wide range of features, to customers that arrive over
time. Customers independently make purchasing decisions according to a general
choice model that includes products features and customers' characteristics,
encoded as... | computer science |
6,222 | Dropout with Expectation-linear Regularization | cs.LG | Dropout, a simple and effective way to train deep neural networks, has led to
a number of impressive empirical successes and spawned many recent theoretical
investigations. However, the gap between dropout's training and inference
phases, introduced due to tractability considerations, has largely remained
under-appreci... | computer science |
6,223 | Generalization Error Bounds for Optimization Algorithms via Stability | stat.ML | Many machine learning tasks can be formulated as Regularized Empirical Risk
Minimization (R-ERM), and solved by optimization algorithms such as gradient
descent (GD), stochastic gradient descent (SGD), and stochastic variance
reduction (SVRG). Conventional analysis on these optimization algorithms
focuses on their conv... | computer science |
6,224 | Variational Autoencoder for Deep Learning of Images, Labels and Captions | stat.ML | A novel variational autoencoder is developed to model images, as well as
associated labels or captions. The Deep Generative Deconvolutional Network
(DGDN) is used as a decoder of the latent image features, and a deep
Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used
to approximate a distri... | computer science |
6,225 | Classifier comparison using precision | cs.LG | New proposed models are often compared to state-of-the-art using statistical
significance testing. Literature is scarce for classifier comparison using
metrics other than accuracy. We present a survey of statistical methods that
can be used for classifier comparison using precision, accounting for
inter-precision corre... | computer science |
6,226 | Fast learning rates with heavy-tailed losses | stat.ML | We study fast learning rates when the losses are not necessarily bounded and
may have a distribution with heavy tails. To enable such analyses, we introduce
two new conditions: (i) the envelope function $\sup_{f \in \mathcal{F}}|\ell
\circ f|$, where $\ell$ is the loss function and $\mathcal{F}$ is the
hypothesis class... | computer science |
6,227 | Max-plus statistical leverage scores | stat.ML | The statistical leverage scores of a complex matrix $A\in\mathbb{C}^{n\times
d}$ record the degree of alignment between col$(A)$ and the coordinate axes in
$\mathbb{C}^n$. These score are used in random sampling algorithms for solving
certain numerical linear algebra problems. In this paper we present a max-plus
algebr... | computer science |
6,228 | Faster Kernels for Graphs with Continuous Attributes via Hashing | cs.LG | While state-of-the-art kernels for graphs with discrete labels scale well to
graphs with thousands of nodes, the few existing kernels for graphs with
continuous attributes, unfortunately, do not scale well. To overcome this
limitation, we present hash graph kernels, a general framework to derive
kernels for graphs with... | computer science |
6,229 | Sparsity-driven weighted ensemble classifier | stat.ML | In this letter, a novel weighted ensemble classifier is proposed that
improves classification accuracy and minimizes the number of classifiers.
Ensemble weight finding problem is modeled as a cost function with following
terms: (a) a data fidelity term aiming to decrease misclassification rate, (b)
a sparsity term aimi... | computer science |
6,230 | One-Trial Correction of Legacy AI Systems and Stochastic Separation
Theorems | stat.ML | We consider the problem of efficient "on the fly" tuning of existing, or {\it
legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are
allowed to be of arbitrary class, albeit the data they are using for computing
interim or final decision responses should posses an underlying structure of a
high-dimens... | computer science |
6,231 | The Search Problem in Mixture Models | stat.ML | We consider the task of learning the parameters of a {\em single} component
of a mixture model, for the case when we are given {\em side information} about
that component, we call this the "search problem" in mixture models. We would
like to solve this with computational and sample complexity lower than solving
the ove... | computer science |
6,232 | Ensemble Validation: Selectivity has a Price, but Variety is Free | stat.ML | If classifiers are selected from a hypothesis class to form an ensemble,
bounds on average error rate over the selected classifiers include a component
for selectivity, which grows as the fraction of hypothesis classifiers selected
for the ensemble shrinks, and a component for variety, which grows with the
size of the ... | computer science |
6,233 | Decentralized Topic Modelling with Latent Dirichlet Allocation | stat.ML | Privacy preserving networks can be modelled as decentralized networks (e.g.,
sensors, connected objects, smartphones), where communication between nodes of
the network is not controlled by an all-knowing, central node. For this type of
networks, the main issue is to gather/learn global information on the network
(e.g.,... | computer science |
6,234 | Learning Protein Dynamics with Metastable Switching Systems | stat.ML | We introduce a machine learning approach for extracting fine-grained
representations of protein evolution from molecular dynamics datasets.
Metastable switching linear dynamical systems extend standard switching models
with a physically-inspired stability constraint. This constraint enables the
learning of nuanced repr... | computer science |
6,235 | Understanding intermediate layers using linear classifier probes | stat.ML | Neural network models have a reputation for being black boxes. We propose a
new method to understand better the roles and dynamics of the intermediate
layers. This has direct consequences on the design of such models and it
enables the expert to be able to justify certain heuristics (such as the
auxiliary heads in the ... | computer science |
6,236 | Generalized Inverse Classification | cs.LG | Inverse classification is the process of perturbing an instance in a
meaningful way such that it is more likely to conform to a specific class.
Historical methods that address such a problem are often framed to leverage
only a single classifier, or specific set of classifiers. These works are often
accompanied by naive... | computer science |
6,237 | Automatic Sleep Stage Scoring with Single-Channel EEG Using
Convolutional Neural Networks | stat.ML | We used convolutional neural networks (CNNs) for automatic sleep stage
scoring based on single-channel electroencephalography (EEG) to learn
task-specific filters for classification without using prior domain knowledge.
We used an openly available dataset from 20 healthy young adults for evaluation
and applied 20-fold ... | computer science |
6,238 | A Methodology for Customizing Clinical Tests for Esophageal Cancer based
on Patient Preferences | cs.LG | Tests for Esophageal cancer can be expensive, uncomfortable and can have side
effects. For many patients, we can predict non-existence of disease with 100%
certainty, just using demographics, lifestyle, and medical history information.
Our objective is to devise a general methodology for customizing tests using
user pr... | computer science |
6,239 | Connecting Generative Adversarial Networks and Actor-Critic Methods | cs.LG | Both generative adversarial networks (GAN) in unsupervised learning and
actor-critic methods in reinforcement learning (RL) have gained a reputation
for being difficult to optimize. Practitioners in both fields have amassed a
large number of strategies to mitigate these instabilities and improve
training. Here we show ... | computer science |
6,240 | Revisiting Multiple Instance Neural Networks | stat.ML | Recently neural networks and multiple instance learning are both attractive
topics in Artificial Intelligence related research fields. Deep neural networks
have achieved great success in supervised learning problems, and multiple
instance learning as a typical weakly-supervised learning method is effective
for many app... | computer science |
6,241 | Dataiku's Solution to SPHERE's Activity Recognition Challenge | stat.ML | Our team won the second prize of the Safe Aging with SPHERE Challenge
organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of
the competition was to recognize activities performed by humans, using sensor
data. This paper presents our solution. It is based on a rich pre-processing
and state of th... | computer science |
6,242 | Robust Bayesian Compressed sensing | stat.ML | We consider the problem of robust compressed sensing whose objective is to
recover a high-dimensional sparse signal from compressed measurements corrupted
by outliers. A new sparse Bayesian learning method is developed for robust
compressed sensing. The basic idea of the proposed method is to identify and
remove the ou... | computer science |
6,243 | Maximum entropy models capture melodic styles | stat.ML | We introduce a Maximum Entropy model able to capture the statistics of
melodies in music. The model can be used to generate new melodies that emulate
the style of the musical corpus which was used to train it. Instead of using
the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in
automatic mus... | computer science |
6,244 | Optimistic Semi-supervised Least Squares Classification | stat.ML | The goal of semi-supervised learning is to improve supervised classifiers by
using additional unlabeled training examples. In this work we study a simple
self-learning approach to semi-supervised learning applied to the least squares
classifier. We show that a soft-label and a hard-label variant of self-learning
can be... | computer science |
6,245 | Exploring the Entire Regularization Path for the Asymmetric Cost Linear
Support Vector Machine | cs.LG | We propose an algorithm for exploring the entire regularization path of
asymmetric-cost linear support vector machines. Empirical evidence suggests the
predictive power of support vector machines depends on the regularization
parameters of the training algorithms. The algorithms exploring the entire
regularization path... | computer science |
6,246 | Generalization bound for kernel similarity learning | stat.ML | Similarity learning has received a large amount of interest and is an
important tool for many scientific and industrial applications. In this
framework, we wish to infer the distance (similarity) between points with
respect to an arbitrary distance function $d$. Here, we formulate the problem
as a regression from a fea... | computer science |
6,247 | Semi-Supervised Active Learning for Support Vector Machines: A Novel
Approach that Exploits Structure Information in Data | stat.ML | In our today's information society more and more data emerges, e.g.~in social
networks, technical applications, or business applications. Companies try to
commercialize these data using data mining or machine learning methods. For
this purpose, the data are categorized or classified, but often at high
(monetary or temp... | computer science |
6,248 | Theoretical Analysis of Domain Adaptation with Optimal Transport | stat.ML | Domain adaptation (DA) is an important and emerging field of machine learning
that tackles the problem occurring when the distributions of training (source
domain) and test (target domain) data are similar but different. Current
theoretical results show that the efficiency of DA algorithms depends on their
capacity of ... | computer science |
6,249 | The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear
Bandits | stat.ML | Stochastic linear bandits are a natural and simple generalisation of
finite-armed bandits with numerous practical applications. Current approaches
focus on generalising existing techniques for finite-armed bandits, notably the
optimism principle and Thompson sampling. While prior work has mostly been in
the worst-case ... | computer science |
6,250 | Kernel Alignment Inspired Linear Discriminant Analysis | cs.LG | Kernel alignment measures the degree of similarity between two kernels. In
this paper, inspired from kernel alignment, we propose a new Linear
Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first
define two kernels, data kernel and class indicator kernel. The problem is to
find a subspace to ... | computer science |
6,251 | Improved Strongly Adaptive Online Learning using Coin Betting | stat.ML | This paper describes a new parameter-free online learning algorithm for
changing environments. In comparing against algorithms with the same time
complexity as ours, we obtain a strongly adaptive regret bound that is a factor
of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical
results show tha... | computer science |
6,252 | An Adaptive Test of Independence with Analytic Kernel Embeddings | stat.ML | A new computationally efficient dependence measure, and an adaptive
statistical test of independence, are proposed. The dependence measure is the
difference between analytic embeddings of the joint distribution and the
product of the marginals, evaluated at a finite set of locations (features).
These features are chose... | computer science |
6,253 | Probabilistic Dimensionality Reduction via Structure Learning | stat.ML | We propose a novel probabilistic dimensionality reduction framework that can
naturally integrate the generative model and the locality information of data.
Based on this framework, we present a new model, which is able to learn a
smooth skeleton of embedding points in a low-dimensional space from
high-dimensional noisy... | computer science |
6,254 | Efficient Metric Learning for the Analysis of Motion Data | cs.LG | We investigate metric learning in the context of dynamic time warping (DTW),
the by far most popular dissimilarity measure used for the comparison and
analysis of motion capture data. While metric learning enables a
problem-adapted representation of data, the majority of meth- ods has been
proposed for vectorial data o... | computer science |
6,255 | Risk-Aware Algorithms for Adversarial Contextual Bandits | cs.LG | In this work we consider adversarial contextual bandits with risk
constraints. At each round, nature prepares a context, a cost for each arm, and
additionally a risk for each arm. The learner leverages the context to pull an
arm and then receives the corresponding cost and risk associated with the
pulled arm. In additi... | computer science |
6,256 | The Peaking Phenomenon in Semi-supervised Learning | stat.ML | For the supervised least squares classifier, when the number of training
objects is smaller than the dimensionality of the data, adding more data to the
training set may first increase the error rate before decreasing it. This,
possibly counterintuitive, phenomenon is known as peaking. In this work, we
observe that a s... | computer science |
6,257 | Markov Chain Truncation for Doubly-Intractable Inference | stat.ML | Computing partition functions, the normalizing constants of probability
distributions, is often hard. Variants of importance sampling give unbiased
estimates of a normalizer Z, however, unbiased estimates of the reciprocal 1/Z
are harder to obtain. Unbiased estimates of 1/Z allow Markov chain Monte Carlo
sampling of "d... | computer science |
6,258 | Learning Determinantal Point Processes in Sublinear Time | stat.ML | We propose a new class of determinantal point processes (DPPs) which can be
manipulated for inference and parameter learning in potentially sublinear time
in the number of items. This class, based on a specific low-rank factorization
of the marginal kernel, is particularly suited to a subclass of continuous DPPs
and DP... | computer science |
6,259 | Learning to Learn Neural Networks | cs.LG | Meta-learning consists in learning learning algorithms. We use a Long Short
Term Memory (LSTM) based network to learn to compute on-line updates of the
parameters of another neural network. These parameters are stored in the cell
state of the LSTM. Our framework allows to compare learned algorithms to
hand-made algorit... | computer science |
6,260 | Kernel Alignment for Unsupervised Transfer Learning | stat.ML | The ability of a human being to extrapolate previously gained knowledge to
other domains inspired a new family of methods in machine learning called
transfer learning. Transfer learning is often based on the assumption that
objects in both target and source domains share some common feature and/or data
space. In this p... | computer science |
6,261 | Regularized Optimal Transport and the Rot Mover's Distance | stat.ML | This paper presents a unified framework for smooth convex regularization of
discrete optimal transport problems. In this context, the regularized optimal
transport turns out to be equivalent to a matrix nearness problem with respect
to Bregman divergences. Our framework thus naturally generalizes a previously
proposed ... | computer science |
6,262 | Stochastic Gradient MCMC with Stale Gradients | stat.ML | Stochastic gradient MCMC (SG-MCMC) has played an important role in
large-scale Bayesian learning, with well-developed theoretical convergence
properties. In such applications of SG-MCMC, it is becoming increasingly
popular to employ distributed systems, where stochastic gradients are computed
based on some outdated par... | computer science |
6,263 | Maximally Divergent Intervals for Anomaly Detection | stat.ML | We present new methods for batch anomaly detection in multivariate time
series. Our methods are based on maximizing the Kullback-Leibler divergence
between the data distribution within and outside an interval of the time
series. An empirical analysis shows the benefits of our algorithms compared to
methods that treat e... | computer science |
6,264 | Robust training on approximated minimal-entropy set | cs.LG | In this paper, we propose a general framework to learn a robust large-margin
binary classifier when corrupt measurements, called anomalies, caused by sensor
failure might be present in the training set. The goal is to minimize the
generalization error of the classifier on non-corrupted measurements while
controlling th... | computer science |
6,265 | Convex Formulation for Kernel PCA and its Use in Semi-Supervised
Learning | cs.LG | In this paper, Kernel PCA is reinterpreted as the solution to a convex
optimization problem. Actually, there is a constrained convex problem for each
principal component, so that the constraints guarantee that the principal
component is indeed a solution, and not a mere saddle point. Although these
insights do not impl... | computer science |
6,266 | An Efficient Minibatch Acceptance Test for Metropolis-Hastings | cs.LG | We present a novel Metropolis-Hastings method for large datasets that uses
small expected-size minibatches of data. Previous work on reducing the cost of
Metropolis-Hastings tests yield variable data consumed per sample, with only
constant factor reductions versus using the full dataset for each sample. Here
we present... | computer science |
6,267 | Online Classification with Complex Metrics | stat.ML | We present a framework and analysis of consistent binary classification for
complex and non-decomposable performance metrics such as the F-measure and the
Jaccard measure. The proposed framework is general, as it applies to both batch
and online learning, and to both linear and non-linear models. Our work follows
recen... | computer science |
6,268 | Learning Deep Architectures for Interaction Prediction in
Structure-based Virtual Screening | stat.ML | We introduce a deep learning architecture for structure-based virtual
screening that generates fixed-sized fingerprints of proteins and small
molecules by applying learnable atom convolution and softmax operations to each
compound separately. These fingerprints are further transformed non-linearly,
their inner-product ... | computer science |
6,269 | A Framework for Parallel and Distributed Training of Neural Networks | stat.ML | The aim of this paper is to develop a general framework for training neural
networks (NNs) in a distributed environment, where training data is partitioned
over a set of agents that communicate with each other through a sparse,
possibly time-varying, connectivity pattern. In such distributed scenario, the
training prob... | computer science |
6,270 | A Theoretical Analysis of Noisy Sparse Subspace Clustering on
Dimensionality-Reduced Data | stat.ML | Subspace clustering is the problem of partitioning unlabeled data points into
a number of clusters so that data points within one cluster lie approximately
on a low-dimensional linear subspace. In many practical scenarios, the
dimensionality of data points to be clustered are compressed due to constraints
of measuremen... | computer science |
6,271 | A Bayesian Ensemble for Unsupervised Anomaly Detection | stat.ML | Methods for unsupervised anomaly detection suffer from the fact that the data
is unlabeled, making it difficult to assess the optimality of detection
algorithms. Ensemble learning has shown exceptional results in classification
and clustering problems, but has not seen as much research in the context of
outlier detecti... | computer science |
6,272 | Approximate cross-validation formula for Bayesian linear regression | stat.ML | Cross-validation (CV) is a technique for evaluating the ability of
statistical models/learning systems based on a given data set. Despite its wide
applicability, the rather heavy computational cost can prevent its use as the
system size grows. To resolve this difficulty in the case of Bayesian linear
regression, we dev... | computer science |
6,273 | A statistical framework for fair predictive algorithms | stat.ML | Predictive modeling is increasingly being employed to assist human
decision-makers. One purported advantage of replacing human judgment with
computer models in high stakes settings-- such as sentencing, hiring, policing,
college admissions, and parole decisions-- is the perceived "neutrality" of
computers. It is argued... | computer science |
6,274 | Socratic Learning: Augmenting Generative Models to Incorporate Latent
Subsets in Training Data | cs.LG | A challenge in training discriminative models like neural networks is
obtaining enough labeled training data. Recent approaches use generative models
to combine weak supervision sources, like user-defined heuristics or knowledge
bases, to label training data. Prior work has explored learning accuracies for
these source... | computer science |
6,275 | Fairness Beyond Disparate Treatment & Disparate Impact: Learning
Classification without Disparate Mistreatment | stat.ML | Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the e... | computer science |
6,276 | Adaptive matching pursuit for sparse signal recovery | cs.LG | Spike and Slab priors have been of much recent interest in signal processing
as a means of inducing sparsity in Bayesian inference. Applications domains
that benefit from the use of these priors include sparse recovery, regression
and classification. It is well-known that solving for the sparse coefficient
vector to ma... | computer science |
6,277 | Causal Network Learning from Multiple Interventions of Unknown
Manipulated Targets | stat.ML | In this paper, we discuss structure learning of causal networks from multiple
data sets obtained by external intervention experiments where we do not know
what variables are manipulated. For example, the conditions in these
experiments are changed by changing temperature or using drugs, but we do not
know what target v... | computer science |
6,278 | Regret Bounds for Lifelong Learning | stat.ML | We consider the problem of transfer learning in an online setting. Different
tasks are presented sequentially and processed by a within-task algorithm. We
propose a lifelong learning strategy which refines the underlying data
representation used by the within-task algorithm, thereby transferring
information from one ta... | computer science |
6,279 | Learning Bound for Parameter Transfer Learning | stat.ML | We consider a transfer-learning problem by using the parameter transfer
approach, where a suitable parameter of feature mapping is learned through one
task and applied to another objective task. Then, we introduce the notion of
the local stability and parameter transfer learnability of parametric feature
mapping,and th... | computer science |
6,280 | Compressive K-means | cs.LG | The Lloyd-Max algorithm is a classical approach to perform K-means
clustering. Unfortunately, its cost becomes prohibitive as the training dataset
grows large. We propose a compressive version of K-means (CKM), that estimates
cluster centers from a sketch, i.e. from a drastically compressed
representation of the traini... | computer science |
6,281 | A Category Space Approach to Supervised Dimensionality Reduction | stat.ML | Supervised dimensionality reduction has emerged as an important theme in the
last decade. Despite the plethora of models and formulations, there is a lack
of a simple model which aims to project the set of patterns into a space
defined by the classes (or categories). To this end, we set up a model in which
each class i... | computer science |
6,282 | On Bochner's and Polya's Characterizations of Positive-Definite Kernels
and the Respective Random Feature Maps | stat.ML | Positive-definite kernel functions are fundamental elements of kernel methods
and Gaussian processes. A well-known construction of such functions comes from
Bochner's characterization, which connects a positive-definite function with a
probability distribution. Another construction, which appears to have attracted
less... | computer science |
6,283 | Professor Forcing: A New Algorithm for Training Recurrent Networks | stat.ML | The Teacher Forcing algorithm trains recurrent networks by supplying observed
sequence values as inputs during training and using the network's own
one-step-ahead predictions to do multi-step sampling. We introduce the
Professor Forcing algorithm, which uses adversarial domain adaptation to
encourage the dynamics of th... | computer science |
6,284 | Orthogonal Random Features | cs.LG | We present an intriguing discovery related to Random Fourier Features: in
Gaussian kernel approximation, replacing the random Gaussian matrix by a
properly scaled random orthogonal matrix significantly decreases kernel
approximation error. We call this technique Orthogonal Random Features (ORF),
and provide theoretical... | computer science |
6,285 | Missing Data Imputation for Supervised Learning | stat.ML | This paper compares methods for imputing missing categorical data for
supervised learning tasks. The ability of researchers to accurately fit a model
and yield unbiased estimates may be compromised by missing data, which are
prevalent in survey-based social science research. We experiment on two machine
learning benchm... | computer science |
6,286 | SOL: A Library for Scalable Online Learning Algorithms | cs.LG | SOL is an open-source library for scalable online learning algorithms, and is
particularly suitable for learning with high-dimensional data. The library
provides a family of regular and sparse online learning algorithms for
large-scale binary and multi-class classification tasks with high efficiency,
scalability, porta... | computer science |
6,287 | Adaptive regularization for Lasso models in the context of
non-stationary data streams | stat.ML | Large scale, streaming datasets are ubiquitous in modern machine learning.
Streaming algorithms must be scalable, amenable to incremental training and
robust to the presence of non-stationarity. In this work consider the problem
of learning $\ell_1$ regularized linear models in the context of streaming
data. In particu... | computer science |
6,288 | Correlated-PCA: Principal Components' Analysis when Data and Noise are
Correlated | cs.LG | Given a matrix of observed data, Principal Components Analysis (PCA) computes
a small number of orthogonal directions that contain most of its variability.
Provably accurate solutions for PCA have been in use for a long time. However,
to the best of our knowledge, all existing theoretical guarantees for it assume
that ... | computer science |
6,289 | Homotopy Analysis for Tensor PCA | stat.ML | Developing efficient and guaranteed nonconvex algorithms has been an
important challenge in modern machine learning. Algorithms with good empirical
performance such as stochastic gradient descent often lack theoretical
guarantees. In this paper, we analyze the class of homotopy or continuation
methods for global optimi... | computer science |
6,290 | Dynamic matrix recovery from incomplete observations under an exact
low-rank constraint | stat.ML | Low-rank matrix factorizations arise in a wide variety of applications --
including recommendation systems, topic models, and source separation, to name
just a few. In these and many other applications, it has been widely noted that
by incorporating temporal information and allowing for the possibility of
time-varying ... | computer science |
6,291 | Contextual Decision Processes with Low Bellman Rank are PAC-Learnable | cs.LG | This paper studies systematic exploration for reinforcement learning with
rich observations and function approximation. We introduce a new model called
contextual decision processes, that unifies and generalizes most prior
settings. Our first contribution is a complexity measure, the Bellman rank,
that we show enables ... | computer science |
6,292 | Active Learning from Imperfect Labelers | cs.LG | We study active learning where the labeler can not only return incorrect
labels but also abstain from labeling. We consider different noise and
abstention conditions of the labeler. We propose an algorithm which utilizes
abstention responses, and analyze its statistical consistency and query
complexity under fairly nat... | computer science |
6,293 | Complex-Valued Kernel Methods for Regression | stat.ML | Usually, complex-valued RKHS are presented as an straightforward application
of the real-valued case. In this paper we prove that this procedure yields a
limited solution for regression. We show that another kernel, here denoted as
pseudo kernel, is needed to learn any function in complex-valued fields.
Accordingly, we... | computer science |
6,294 | Optimization for Large-Scale Machine Learning with Distributed Features
and Observations | stat.ML | As the size of modern data sets exceeds the disk and memory capacities of a
single computer, machine learning practitioners have resorted to parallel and
distributed computing. Given that optimization is one of the pillars of machine
learning and predictive modeling, distributed optimization methods have
recently garne... | computer science |
6,295 | Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large
Data | cs.LG | Support Vector Data Description (SVDD) provides a useful approach to
construct a description of multivariate data for single-class classification
and outlier detection with various practical applications. Gaussian kernel used
in SVDD formulation allows flexible data description defined by observations
designated as sup... | computer science |
6,296 | The Concrete Distribution: A Continuous Relaxation of Discrete Random
Variables | cs.LG | The reparameterization trick enables optimizing large scale stochastic
computation graphs via gradient descent. The essence of the trick is to
refactor each stochastic node into a differentiable function of its parameters
and a random variable with fixed distribution. After refactoring, the gradients
of the loss propag... | computer science |
6,297 | A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski
metric and a fast k -means initialisation | cs.LG | In this paper we make two novel contributions to hierarchical clustering.
First, we introduce an anomalous pattern initialisation method for hierarchical
clustering algorithms, called A-Ward, capable of substantially reducing the
time they take to converge. This method generates an initial partition with a
sufficiently... | computer science |
6,298 | Categorical Reparameterization with Gumbel-Softmax | stat.ML | Categorical variables are a natural choice for representing discrete
structure in the world. However, stochastic neural networks rarely use
categorical latent variables due to the inability to backpropagate through
samples. In this work, we present an efficient gradient estimator that replaces
the non-differentiable sa... | computer science |
6,299 | Deep Information Propagation | stat.ML | We study the behavior of untrained neural networks whose weights and biases
are randomly distributed using mean field theory. We show the existence of
depth scales that naturally limit the maximum depth of signal propagation
through these random networks. Our main practical result is to show that random
networks may be... | computer science |
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