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6,000 | Provable Algorithms for Inference in Topic Models | cs.LG | Recently, there has been considerable progress on designing algorithms with
provable guarantees -- typically using linear algebraic methods -- for
parameter learning in latent variable models. But designing provable algorithms
for inference has proven to be more challenging. Here we take a first step
towards provable i... | computer science |
6,001 | Local Region Sparse Learning for Image-on-Scalar Regression | stat.ML | Identification of regions of interest (ROI) associated with certain disease
has a great impact on public health. Imposing sparsity of pixel values and
extracting active regions simultaneously greatly complicate the image analysis.
We address these challenges by introducing a novel region-selection penalty in
the framew... | computer science |
6,002 | Variational Bayesian Inference for Hidden Markov Models With
Multivariate Gaussian Output Distributions | cs.LG | Hidden Markov Models (HMM) have been used for several years in many time
series analysis or pattern recognitions tasks. HMM are often trained by means
of the Baum-Welch algorithm which can be seen as a special variant of an
expectation maximization (EM) algorithm. Second-order training techniques such
as Variational Ba... | computer science |
6,003 | PAC-Bayesian Theory Meets Bayesian Inference | stat.ML | We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the
Bayesian marginal likelihood. That is, for the negative log-likelihood loss
function, we show that the minimization of PAC-Bayesian generalization risk
bounds maximizes the Bayesian marginal likelihood. This provides an alternative
explanatio... | computer science |
6,004 | An optimal algorithm for the Thresholding Bandit Problem | stat.ML | We study a specific \textit{combinatorial pure exploration stochastic bandit
problem} where the learner aims at finding the set of arms whose means are
above a given threshold, up to a given precision, and \textit{for a fixed time
horizon}. We propose a parameter-free algorithm based on an original heuristic,
and prove... | computer science |
6,005 | Asymptotic Analysis of Objectives based on Fisher Information in Active
Learning | stat.ML | Obtaining labels can be costly and time-consuming. Active learning allows a
learning algorithm to intelligently query samples to be labeled for efficient
learning. Fisher information ratio (FIR) has been used as an objective for
selecting queries in active learning. However, little is known about the theory
behind the ... | computer science |
6,006 | Muffled Semi-Supervised Learning | cs.LG | We explore a novel approach to semi-supervised learning. This approach is
contrary to the common approach in that the unlabeled examples serve to
"muffle," rather than enhance, the guidance provided by the labeled examples.
We provide several variants of the basic algorithm and show experimentally that
they can achieve... | computer science |
6,007 | Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit
Problem | stat.ML | We consider the problem of \textit{best arm identification} with a
\textit{fixed budget $T$}, in the $K$-armed stochastic bandit setting, with
arms distribution defined on $[0,1]$. We prove that any bandit strategy, for at
least one bandit problem characterized by a complexity $H$, will misidentify
the best arm with pr... | computer science |
6,008 | TripleSpin - a generic compact paradigm for fast machine learning
computations | cs.LG | We present a generic compact computational framework relying on structured
random matrices that can be applied to speed up several machine learning
algorithms with almost no loss of accuracy. The applications include new fast
LSH-based algorithms, efficient kernel computations via random feature maps,
convex optimizati... | computer science |
6,009 | A budget-constrained inverse classification framework for smooth
classifiers | cs.LG | Inverse classification is the process of manipulating an instance such that
it is more likely to conform to a specific class. Past methods that address
such a problem have shortcomings. Greedy methods make changes that are overly
radical, often relying on data that is strictly discrete. Other methods rely on
certain da... | computer science |
6,010 | Spectral Methods for Correlated Topic Models | cs.LG | In this paper, we propose guaranteed spectral methods for learning a broad
range of topic models, which generalize the popular Latent Dirichlet Allocation
(LDA). We overcome the limitation of LDA to incorporate arbitrary topic
correlations, by assuming that the hidden topic proportions are drawn from a
flexible class o... | computer science |
6,011 | Forest Floor Visualizations of Random Forests | stat.ML | We propose a novel methodology, forest floor, to visualize and interpret
random forest (RF) models. RF is a popular and useful tool for non-linear
multi-variate classification and regression, which yields a good trade-off
between robustness (low variance) and adaptiveness (low bias). Direct
interpretation of a RF model... | computer science |
6,012 | Kernel Mean Embedding of Distributions: A Review and Beyond | stat.ML | A Hilbert space embedding of a distribution---in short, a kernel mean
embedding---has recently emerged as a powerful tool for machine learning and
inference. The basic idea behind this framework is to map distributions into a
reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel
methods can be ex... | computer science |
6,013 | Efficiently Bounding Optimal Solutions after Small Data Modification in
Large-Scale Empirical Risk Minimization | stat.ML | We study large-scale classification problems in changing environments where a
small part of the dataset is modified, and the effect of the data modification
must be quickly incorporated into the classifier. When the entire dataset is
large, even if the amount of the data modification is fairly small, the
computational ... | computer science |
6,014 | Short Communication on QUIST: A Quick Clustering Algorithm | cs.LG | In this short communication we introduce the quick clustering algorithm
(QUIST), an efficient hierarchical clustering algorithm based on sorting. QUIST
is a poly-logarithmic divisive clustering algorithm that does not assume the
number of clusters, and/or the cluster size to be known ahead of time. It is
also insensiti... | computer science |
6,015 | Adversarially Learned Inference | stat.ML | We introduce the adversarially learned inference (ALI) model, which jointly
learns a generation network and an inference network using an adversarial
process. The generation network maps samples from stochastic latent variables
to the data space while the inference network maps training examples in data
space to the sp... | computer science |
6,016 | Differentially Private Gaussian Processes | stat.ML | A major challenge for machine learning is increasing the availability of data
while respecting the privacy of individuals. Here we combine the provable
privacy guarantees of the Differential Privacy framework with the flexibility
of Gaussian processes (GPs). We propose a method using GPs to provide
Differentially Priva... | computer science |
6,017 | Sequential Principal Curves Analysis | stat.ML | This work includes all the technical details of the Sequential Principal
Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear
and invertible feature extraction technique. The identified curvilinear
features can be interpreted as a set of nonlinear sensors: the response of each
sensor is the pr... | computer science |
6,018 | Convolutional Imputation of Matrix Networks | cs.LG | A matrix network is a family of matrices, where the relationship between them
is modeled as a weighted graph. Each node represents a matrix, and the weight
on each edge represents the similarity between the two matrices. Suppose that
we observe a few entries of each matrix with noise, and the fraction of entries
we obs... | computer science |
6,019 | A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear
Manifold Distributed Data Classification | cs.LG | $k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised
learning algorithms to classify Gaussian distributed data, but it does not
achieve good results when it is applied to nonlinear manifold distributed data,
especially when a very limited amount of labeled samples are available. In this
paper, we pro... | computer science |
6,020 | On Valid Optimal Assignment Kernels and Applications to Graph
Classification | cs.LG | The success of kernel methods has initiated the design of novel positive
semidefinite functions, in particular for structured data. A leading design
paradigm for this is the convolution kernel, which decomposes structured
objects into their parts and sums over all pairs of parts. Assignment kernels,
in contrast, are ob... | computer science |
6,021 | Robust Ensemble Clustering Using Probability Trajectories | stat.ML | Although many successful ensemble clustering approaches have been developed
in recent years, there are still two limitations to most of the existing
approaches. First, they mostly overlook the issue of uncertain links, which may
mislead the overall consensus process. Second, they generally lack the ability
to incorpora... | computer science |
6,022 | Bounds for Vector-Valued Function Estimation | stat.ML | We present a framework to derive risk bounds for vector-valued learning with
a broad class of feature maps and loss functions. Multi-task learning and
one-vs-all multi-category learning are treated as examples. We discuss in
detail vector-valued functions with one hidden layer, and demonstrate that the
conditions under... | computer science |
6,023 | Semi-Supervised Learning with Generative Adversarial Networks | stat.ML | We extend Generative Adversarial Networks (GANs) to the semi-supervised
context by forcing the discriminator network to output class labels. We train a
generative model G and a discriminator D on a dataset with inputs belonging to
one of N classes. At training time, D is made to predict which of N+1 classes
the input b... | computer science |
6,024 | Regret Bounds for Non-decomposable Metrics with Missing Labels | cs.LG | We consider the problem of recommending relevant labels (items) for a given
data point (user). In particular, we are interested in the practically
important setting where the evaluation is with respect to non-decomposable
(over labels) performance metrics like the $F_1$ measure, and the training data
has missing labels... | computer science |
6,025 | Towards a Neural Statistician | stat.ML | An efficient learner is one who reuses what they already know to tackle a new
problem. For a machine learner, this means understanding the similarities
amongst datasets. In order to do this, one must take seriously the idea of
working with datasets, rather than datapoints, as the key objects to model.
Towards this goal... | computer science |
6,026 | Clustering with Same-Cluster Queries | cs.LG | We propose a framework for Semi-Supervised Active Clustering framework
(SSAC), where the learner is allowed to interact with a domain expert, asking
whether two given instances belong to the same cluster or not. We study the
query and computational complexity of clustering in this framework. We consider
a setting where... | computer science |
6,027 | Variational Information Maximization for Feature Selection | stat.ML | Feature selection is one of the most fundamental problems in machine
learning. An extensive body of work on information-theoretic feature selection
exists which is based on maximizing mutual information between subsets of
features and class labels. Practical methods are forced to rely on
approximations due to the diffi... | computer science |
6,028 | Sketching for Large-Scale Learning of Mixture Models | cs.LG | Learning parameters from voluminous data can be prohibitive in terms of
memory and computational requirements. We propose a "compressive learning"
framework where we estimate model parameters from a sketch of the training
data. This sketch is a collection of generalized moments of the underlying
probability distributio... | computer science |
6,029 | Causal Bandits: Learning Good Interventions via Causal Inference | stat.ML | We study the problem of using causal models to improve the rate at which good
interventions can be learned online in a stochastic environment. Our formalism
combines multi-arm bandits and causal inference to model a novel type of bandit
feedback that is not exploited by existing approaches. We propose a new
algorithm t... | computer science |
6,030 | Deep Directed Generative Models with Energy-Based Probability Estimation | cs.LG | Training energy-based probabilistic models is confronted with apparently
intractable sums, whose Monte Carlo estimation requires sampling from the
estimated probability distribution in the inner loop of training. This can be
approximately achieved by Markov chain Monte Carlo methods, but may still face
a formidable obs... | computer science |
6,031 | InfoGAN: Interpretable Representation Learning by Information Maximizing
Generative Adversarial Nets | cs.LG | This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent varia... | computer science |
6,032 | Comparison of Several Sparse Recovery Methods for Low Rank Matrices with
Random Samples | cs.LG | In this paper, we will investigate the efficacy of IMAT (Iterative Method of
Adaptive Thresholding) in recovering the sparse signal (parameters) for linear
models with missing data. Sparse recovery rises in compressed sensing and
machine learning problems and has various applications necessitating viable
reconstruction... | computer science |
6,033 | Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective | cs.LG | We present a new framework for online Least Squares algorithms for nonlinear
modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS
(e.g., kernel trick), we map the data to a finite dimensional Euclidean space,
using random features of the kernel's Fourier transform. The advantage is that,
the ... | computer science |
6,034 | Specialized Support Vector Machines for open-set recognition | cs.LG | Often, when dealing with real-world recognition problems, we do not need, and
often cannot have, knowledge of the entire set of possible classes that might
appear during operational testing. Moreover, sometimes some of these classes
may be ill-sampled, not sampled at all or undefined. In such cases, we need to
think of... | computer science |
6,035 | Matching Networks for One Shot Learning | cs.LG | Learning from a few examples remains a key challenge in machine learning.
Despite recent advances in important domains such as vision and language, the
standard supervised deep learning paradigm does not offer a satisfactory
solution for learning new concepts rapidly from little data. In this work, we
employ ideas from... | computer science |
6,036 | The Crossover Process: Learnability and Data Protection from Inference
Attacks | cs.LG | It is usual to consider data protection and learnability as conflicting
objectives. This is not always the case: we show how to jointly control
inference --- seen as the attack --- and learnability by a noise-free process
that mixes training examples, the Crossover Process (cp). One key point is that
the cp~is typicall... | computer science |
6,037 | Modal-set estimation with an application to clustering | stat.ML | We present a first procedure that can estimate -- with statistical
consistency guarantees -- any local-maxima of a density, under benign
distributional conditions. The procedure estimates all such local maxima, or
$\textit{modal-sets}$, of any bounded shape or dimension, including usual
point-modes. In practice, modal-... | computer science |
6,038 | Local Canonical Correlation Analysis for Nonlinear Common Variables
Discovery | cs.LG | In this paper, we address the problem of hidden common variables discovery
from multimodal data sets of nonlinear high-dimensional observations. We
present a metric based on local applications of canonical correlation analysis
(CCA) and incorporate it in a kernel-based manifold learning technique.We show
that this metr... | computer science |
6,039 | Time for a change: a tutorial for comparing multiple classifiers through
Bayesian analysis | stat.ML | The machine learning community adopted the use of null hypothesis
significance testing (NHST) in order to ensure the statistical validity of
results. Many scientific fields however realized the shortcomings of
frequentist reasoning and in the most radical cases even banned its use in
publications. We should do the same... | computer science |
6,040 | A scalable end-to-end Gaussian process adapter for irregularly sampled
time series classification | stat.ML | We present a general framework for classification of sparse and
irregularly-sampled time series. The properties of such time series can result
in substantial uncertainty about the values of the underlying temporal
processes, while making the data difficult to deal with using standard
classification methods that assume ... | computer science |
6,041 | Recurrent neural network training with preconditioned stochastic
gradient descent | stat.ML | This paper studies the performance of a recently proposed preconditioned
stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN)
training. PSGD adaptively estimates a preconditioner to accelerate gradient
descent, and is designed to be simple, general and easy to use, as stochastic
gradient desce... | computer science |
6,042 | Masking Strategies for Image Manifolds | stat.ML | We consider the problem of selecting an optimal mask for an image manifold,
i.e., choosing a subset of the pixels of the image that preserves the
manifold's geometric structure present in the original data. Such masking
implements a form of compressive sensing through emerging imaging sensor
platforms for which the pow... | computer science |
6,043 | Improving Variational Inference with Inverse Autoregressive Flow | cs.LG | The framework of normalizing flows provides a general strategy for flexible
variational inference of posteriors over latent variables. We propose a new
type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast
to earlier published flows, scales well to high-dimensional latent spaces. The
proposed f... | computer science |
6,044 | Logarithmic Time One-Against-Some | stat.ML | We create a new online reduction of multiclass classification to binary
classification for which training and prediction time scale logarithmically
with the number of classes. Compared to previous approaches, we obtain
substantially better statistical performance for two reasons: First, we prove a
tighter and more comp... | computer science |
6,045 | Learning Optimal Interventions | stat.ML | Our goal is to identify beneficial interventions from observational data. We
consider interventions that are narrowly focused (impacting few covariates) and
may be tailored to each individual or globally enacted over a population. For
applications where harmful intervention is drastically worse than proposing no
change... | computer science |
6,046 | Pruning Random Forests for Prediction on a Budget | stat.ML | We propose to prune a random forest (RF) for resource-constrained prediction.
We first construct a RF and then prune it to optimize expected feature cost &
accuracy. We pose pruning RFs as a novel 0-1 integer program with linear
constraints that encourages feature re-use. We establish total unimodularity of
the constra... | computer science |
6,047 | ACDC: $α$-Carving Decision Chain for Risk Stratification | stat.ML | In many healthcare settings, intuitive decision rules for risk stratification
can help effective hospital resource allocation. This paper introduces a novel
variant of decision tree algorithms that produces a chain of decisions, not a
general tree. Our algorithm, $\alpha$-Carving Decision Chain (ACDC),
sequentially car... | computer science |
6,048 | Model-Agnostic Interpretability of Machine Learning | stat.ML | Understanding why machine learning models behave the way they do empowers
both system designers and end-users in many ways: in model selection, feature
engineering, in order to trust and act upon the predictions, and in more
intuitive user interfaces. Thus, interpretability has become a vital concern in
machine learnin... | computer science |
6,049 | Learning Interpretable Musical Compositional Rules and Traces | stat.ML | Throughout music history, theorists have identified and documented
interpretable rules that capture the decisions of composers. This paper asks,
"Can a machine behave like a music theorist?" It presents MUS-ROVER, a
self-learning system for automatically discovering rules from symbolic music.
MUS-ROVER performs feature... | computer science |
6,050 | Ground Truth Bias in External Cluster Validity Indices | stat.ML | It has been noticed that some external CVIs exhibit a preferential bias
towards a larger or smaller number of clusters which is monotonic (directly or
inversely) in the number of clusters in candidate partitions. This type of bias
is caused by the functional form of the CVI model. For example, the popular
Rand index (R... | computer science |
6,051 | Using Visual Analytics to Interpret Predictive Machine Learning Models | stat.ML | It is commonly believed that increasing the interpretability of a machine
learning model may decrease its predictive power. However, inspecting
input-output relationships of those models using visual analytics, while
treating them as black-box, can help to understand the reasoning behind
outcomes without sacrificing pr... | computer science |
6,052 | Structured Stochastic Linear Bandits | stat.ML | The stochastic linear bandit problem proceeds in rounds where at each round
the algorithm selects a vector from a decision set after which it receives a
noisy linear loss parameterized by an unknown vector. The goal in such a
problem is to minimize the (pseudo) regret which is the difference between the
total expected ... | computer science |
6,053 | Interpretable Two-level Boolean Rule Learning for Classification | stat.ML | As a contribution to interpretable machine learning research, we develop a
novel optimization framework for learning accurate and sparse two-level Boolean
rules. We consider rules in both conjunctive normal form (AND-of-ORs) and
disjunctive normal form (OR-of-ANDs). A principled objective function is
proposed to trade ... | computer science |
6,054 | Building an Interpretable Recommender via Loss-Preserving Transformation | stat.ML | We propose a method for building an interpretable recommender system for
personalizing online content and promotions. Historical data available for the
system consists of customer features, provided content (promotions), and user
responses. Unlike in a standard multi-class classification setting,
misclassification cost... | computer science |
6,055 | Clustering with a Reject Option: Interactive Clustering as Bayesian
Prior Elicitation | stat.ML | A good clustering can help a data analyst to explore and understand a data
set, but what constitutes a good clustering may depend on domain-specific and
application-specific criteria. These criteria can be difficult to formalize,
even when it is easy for an analyst to know a good clustering when they see
one. We presen... | computer science |
6,056 | Tutorial on Variational Autoencoders | stat.ML | In just three years, Variational Autoencoders (VAEs) have emerged as one of
the most popular approaches to unsupervised learning of complicated
distributions. VAEs are appealing because they are built on top of standard
function approximators (neural networks), and can be trained with stochastic
gradient descent. VAEs ... | computer science |
6,057 | Online and Differentially-Private Tensor Decomposition | stat.ML | In this paper, we resolve many of the key algorithmic questions regarding
robustness, memory efficiency, and differential privacy of tensor
decomposition. We propose simple variants of the tensor power method which
enjoy these strong properties. We present the first guarantees for online
tensor power method which has a... | computer science |
6,058 | An Empirical Comparison of Sampling Quality Metrics: A Case Study for
Bayesian Nonnegative Matrix Factorization | cs.LG | In this work, we empirically explore the question: how can we assess the
quality of samples from some target distribution? We assume that the samples
are provided by some valid Monte Carlo procedure, so we are guaranteed that the
collection of samples will asymptotically approximate the true distribution.
Most current ... | computer science |
6,059 | FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text
Categorization | stat.ML | In this paper, we present a new wrapper feature selection approach based on
Jensen-Shannon (JS) divergence, termed feature selection with maximum
JS-divergence (FSMJ), for text categorization. Unlike most existing feature
selection approaches, the proposed FSMJ approach is based on real-valued
features which provide mo... | computer science |
6,060 | Kernel-based Generative Learning in Distortion Feature Space | stat.ML | This paper presents a novel kernel-based generative classifier which is
defined in a distortion subspace using polynomial series expansion, named
Kernel-Distortion (KD) classifier. An iterative kernel selection algorithm is
developed to steadily improve classification performance by repeatedly removing
and adding kerne... | computer science |
6,061 | Finite Sample Prediction and Recovery Bounds for Ordinal Embedding | stat.ML | The goal of ordinal embedding is to represent items as points in a
low-dimensional Euclidean space given a set of constraints in the form of
distance comparisons like "item $i$ is closer to item $j$ than item $k$".
Ordinal constraints like this often come from human judgments. To account for
errors and variation in jud... | computer science |
6,062 | Visualizing Dynamics: from t-SNE to SEMI-MDPs | stat.ML | Deep Reinforcement Learning (DRL) is a trending field of research, showing
great promise in many challenging problems such as playing Atari, solving Go
and controlling robots. While DRL agents perform well in practice we are still
missing the tools to analayze their performance and visualize the temporal
abstractions t... | computer science |
6,063 | Explainable Restricted Boltzmann Machines for Collaborative Filtering | stat.ML | Most accurate recommender systems are black-box models, hiding the reasoning
behind their recommendations. Yet explanations have been shown to increase the
user's trust in the system in addition to providing other benefits such as
scrutability, meaning the ability to verify the validity of recommendations.
This gap bet... | computer science |
6,064 | Interpretable Machine Learning Models for the Digital Clock Drawing Test | stat.ML | The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular
neuropsychological screening tool for cognitive conditions. The Digital Clock
Drawing Test (dCDT) uses novel software to analyze data from a digitizing
ballpoint pen that reports its position with considerable spatial and temporal
precision, making possi... | computer science |
6,065 | Algorithmic Composition of Melodies with Deep Recurrent Neural Networks | stat.ML | A big challenge in algorithmic composition is to devise a model that is both
easily trainable and able to reproduce the long-range temporal dependencies
typical of music. Here we investigate how artificial neural networks can be
trained on a large corpus of melodies and turned into automated music composers
able to gen... | computer science |
6,066 | Multiclass feature learning for hyperspectral image classification:
sparse and hierarchical solutions | stat.ML | In this paper, we tackle the question of discovering an effective set of
spatial filters to solve hyperspectral classification problems. Instead of
fixing a priori the filters and their parameters using expert knowledge, we let
the model find them within random draws in the (possibly infinite) space of
possible filters... | computer science |
6,067 | Non-convex regularization in remote sensing | stat.ML | In this paper, we study the effect of different regularizers and their
implications in high dimensional image classification and sparse linear
unmixing. Although kernelization or sparse methods are globally accepted
solutions for processing data in high dimensions, we present here a study on
the impact of the form of r... | computer science |
6,068 | Parallel SGD: When does averaging help? | stat.ML | Consider a number of workers running SGD independently on the same pool of
data and averaging the models every once in a while -- a common but not well
understood practice. We study model averaging as a variance-reducing mechanism
and describe two ways in which the frequency of averaging affects convergence.
For convex... | computer science |
6,069 | Regression Trees and Random forest based feature selection for malaria
risk exposure prediction | stat.ML | This paper deals with prediction of anopheles number, the main vector of
malaria risk, using environmental and climate variables. The variables
selection is based on an automatic machine learning method using regression
trees, and random forests combined with stratified two levels cross validation.
The minimum threshol... | computer science |
6,070 | Is the Bellman residual a bad proxy? | cs.LG | This paper aims at theoretically and empirically comparing two standard
optimization criteria for Reinforcement Learning: i) maximization of the mean
value and ii) minimization of the Bellman residual. For that purpose, we place
ourselves in the framework of policy search algorithms, that are usually
designed to maximi... | computer science |
6,071 | Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models | cs.LG | In this paper, we investigate the recovery of a sparse weight vector
(parameters vector) from a set of noisy linear combinations. However, only
partial information about the matrix representing the linear combinations is
available. Assuming a low-rank structure for the matrix, one natural solution
would be to first app... | computer science |
6,072 | Estimating the class prior and posterior from noisy positives and
unlabeled data | stat.ML | We develop a classification algorithm for estimating posterior distributions
from positive-unlabeled data, that is robust to noise in the positive labels
and effective for high-dimensional data. In recent years, several algorithms
have been proposed to learn from positive-unlabeled data; however, many of
these contribu... | computer science |
6,073 | Clustering-Based Relational Unsupervised Representation Learning with an
Explicit Distributed Representation | stat.ML | The goal of unsupervised representation learning is to extract a new
representation of data, such that solving many different tasks becomes easier.
Existing methods typically focus on vectorized data and offer little support
for relational data, which additionally describe relationships among instances.
In this work we... | computer science |
6,074 | Multi-View Kernel Consensus For Data Analysis and Signal Processing | cs.LG | The input data features set for many data driven tasks is high-dimensional
while the intrinsic dimension of the data is low. Data analysis methods aim to
uncover the underlying low dimensional structure imposed by the low dimensional
hidden parameters by utilizing distance metrics that consider the set of
attributes as... | computer science |
6,075 | A Semi-Definite Programming approach to low dimensional embedding for
unsupervised clustering | stat.ML | This paper proposes a variant of the method of Gu\'edon and Verhynin for
estimating the cluster matrix in the Mixture of Gaussians framework via
Semi-Definite Programming. A clustering oriented embedding is deduced from this
estimate. The procedure is suitable for very high dimensional data because it
is based on pairw... | computer science |
6,076 | Tighter bounds lead to improved classifiers | cs.LG | The standard approach to supervised classification involves the minimization
of a log-loss as an upper bound to the classification error. While this is a
tight bound early on in the optimization, it overemphasizes the influence of
incorrectly classified examples far from the decision boundary. Updating the
upper bound ... | computer science |
6,077 | Convolutional Neural Networks on Graphs with Fast Localized Spectral
Filtering | cs.LG | In this work, we are interested in generalizing convolutional neural networks
(CNNs) from low-dimensional regular grids, where image, video and speech are
represented, to high-dimensional irregular domains, such as social networks,
brain connectomes or words' embedding, represented by graphs. We present a
formulation o... | computer science |
6,078 | Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits | stat.ML | We study a generalization of the multi-armed bandit problem with multiple
plays where there is a cost associated with pulling each arm and the agent has
a budget at each time that dictates how much she can expect to spend. We derive
an asymptotic regret lower bound for any uniformly efficient algorithm in our
setting. ... | computer science |
6,079 | Vote-boosting ensembles | cs.LG | Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that i... | computer science |
6,080 | A Model Explanation System: Latest Updates and Extensions | stat.ML | We propose a general model explanation system (MES) for "explaining" the
output of black box classifiers. This paper describes extensions to Turner
(2015), which is referred to frequently in the text. We use the motivating
example of a classifier trained to detect fraud in a credit card transaction
history. The key asp... | computer science |
6,081 | Ballpark Learning: Estimating Labels from Rough Group Comparisons | stat.ML | We are interested in estimating individual labels given only coarse,
aggregated signal over the data points. In our setting, we receive sets
("bags") of unlabeled instances with constraints on label proportions. We relax
the unrealistic assumption of known label proportions, made in previous work;
instead, we assume on... | computer science |
6,082 | Unsupervised Learning with Imbalanced Data via Structure Consolidation
Latent Variable Model | cs.LG | Unsupervised learning on imbalanced data is challenging because, when given
imbalanced data, current model is often dominated by the major category and
ignores the categories with small amount of data. We develop a latent variable
model that can cope with imbalanced data by dividing the latent space into a
shared space... | computer science |
6,083 | Combining Gradient Boosting Machines with Collective Inference to
Predict Continuous Values | cs.LG | Gradient boosting of regression trees is a competitive procedure for learning
predictive models of continuous data that fits the data with an additive
non-parametric model. The classic version of gradient boosting assumes that the
data is independent and identically distributed. However, relational data with
interdepen... | computer science |
6,084 | Efficient and Consistent Robust Time Series Analysis | cs.LG | We study the problem of robust time series analysis under the standard
auto-regressive (AR) time series model in the presence of arbitrary outliers.
We devise an efficient hard thresholding based algorithm which can obtain a
consistent estimate of the optimal AR model despite a large fraction of the
time series points ... | computer science |
6,085 | A scaled Bregman theorem with applications | cs.LG | Bregman divergences play a central role in the design and analysis of a range
of machine learning algorithms. This paper explores the use of Bregman
divergences to establish reductions between such algorithms and their analyses.
We present a new scaled isodistortion theorem involving Bregman divergences
(scaled Bregman... | computer science |
6,086 | Group Sparse Regularization for Deep Neural Networks | stat.ML | In this paper, we consider the joint task of simultaneously optimizing (i)
the weights of a deep neural network, (ii) the number of neurons for each
hidden layer, and (iii) the subset of active input features (i.e., feature
selection). While these problems are generally dealt with separately, we
present a simple regula... | computer science |
6,087 | Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised
Algorithm | stat.ML | We propose Rademacher complexity bounds for multiclass classifiers trained
with a two-step semi-supervised model. In the first step, the algorithm
partitions the partially labeled data and then identifies dense clusters
containing $\kappa$ predominant classes using the labeled training examples
such that the proportion... | computer science |
6,088 | Understanding the Energy and Precision Requirements for Online Learning | stat.ML | It is well-known that the precision of data, hyperparameters, and internal
representations employed in learning systems directly impacts its energy,
throughput, and latency. The precision requirements for the training algorithm
are also important for systems that learn on-the-fly. Prior work has shown that
the data and... | computer science |
6,089 | How to Evaluate the Quality of Unsupervised Anomaly Detection
Algorithms? | stat.ML | When sufficient labeled data are available, classical criteria based on
Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be
used to compare the performance of un-supervised anomaly detection algorithms.
However , in many situations, few or no data are labeled. This calls for
alternative crite... | computer science |
6,090 | Learning Discriminative Features using Encoder-Decoder type Deep Neural
Nets | cs.LG | As machine learning is applied to an increasing variety of complex problems,
which are defined by high dimensional and complex data sets, the necessity for
task oriented feature learning grows in importance. With the advancement of
Deep Learning algorithms, various successful feature learning techniques have
evolved. I... | computer science |
6,091 | An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality
in Machine Learning | stat.ML | We propose a clustering-based iterative algorithm to solve certain
optimization problems in machine learning, where we start the algorithm by
aggregating the original data, solving the problem on aggregated data, and then
in subsequent steps gradually disaggregate the aggregated data. We apply the
algorithm to common m... | computer science |
6,092 | Algorithms for Generalized Cluster-wise Linear Regression | stat.ML | Cluster-wise linear regression (CLR), a clustering problem intertwined with
regression, is to find clusters of entities such that the overall sum of
squared errors from regressions performed over these clusters is minimized,
where each cluster may have different variances. We generalize the CLR problem
by allowing each... | computer science |
6,093 | An optimal learning method for developing personalized treatment regimes | stat.ML | A treatment regime is a function that maps individual patient information to
a recommended treatment, hence explicitly incorporating the heterogeneity in
need for treatment across individuals. Patient responses are dichotomous and
can be predicted through an unknown relationship that depends on the patient
information ... | computer science |
6,094 | Tensor Decomposition for Signal Processing and Machine Learning | stat.ML | Tensors or {\em multi-way arrays} are functions of three or more indices
$(i,j,k,\cdots)$ -- similar to matrices (two-way arrays), which are functions
of two indices $(r,c)$ for (row,column). Tensors have a rich history,
stretching over almost a century, and touching upon numerous disciplines; but
they have only recent... | computer science |
6,095 | Nesterov's Accelerated Gradient and Momentum as approximations to
Regularised Update Descent | stat.ML | We present a unifying framework for adapting the update direction in
gradient-based iterative optimization methods. As natural special cases we
re-derive classical momentum and Nesterov's accelerated gradient method,
lending a new intuitive interpretation to the latter algorithm. We show that a
new algorithm, which we ... | computer science |
6,096 | Mini-Batch Spectral Clustering | stat.ML | The cost of computing the spectrum of Laplacian matrices hinders the
application of spectral clustering to large data sets. While approximations
recover computational tractability, they can potentially affect clustering
performance. This paper proposes a practical approach to learn spectral
clustering based on adaptive... | computer science |
6,097 | Proceedings of the 2016 ICML Workshop on Human Interpretability in
Machine Learning (WHI 2016) | stat.ML | This is the Proceedings of the 2016 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016.
Invited speakers were Susan Athey, Rich Caruana, Jacob Feldman, Percy Liang,
and Hanna Wallach. | computer science |
6,098 | Learning a metric for class-conditional KNN | cs.LG | Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework
which addresses many of the pitfalls of K-Nearest Neighbour (KNN)
classification. It has yielded competitive results on several computer vision
benchmarks. Its central tenet is that during NN search, a query is not compared
to every example in a d... | computer science |
6,099 | Information Projection and Approximate Inference for Structured Sparse
Variables | stat.ML | Approximate inference via information projection has been recently introduced
as a general-purpose approach for efficient probabilistic inference given
sparse variables. This manuscript goes beyond classical sparsity by proposing
efficient algorithms for approximate inference via information projection that
are applica... | computer science |
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