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11,401 | FAASTA: A fast solver for total-variation regularization of
ill-conditioned problems with application to brain imaging | cs.LG | The total variation (TV) penalty, as many other analysis-sparsity problems,
does not lead to separable factors or a proximal operatorwith a closed-form
expression, such as soft thresholding for the $\ell\_1$ penalty. As a result,
in a variational formulation of an inverse problem or statisticallearning
estimation, it l... | computer science |
11,402 | Refined Error Bounds for Several Learning Algorithms | cs.LG | This article studies the achievable guarantees on the error rates of certain
learning algorithms, with particular focus on refining logarithmic factors.
Many of the results are based on a general technique for obtaining bounds on
the error rates of sample-consistent classifiers with monotonic error regions,
in the real... | computer science |
11,403 | Adaptive Algorithms for Online Convex Optimization with Long-term
Constraints | stat.ML | We present an adaptive online gradient descent algorithm to solve online
convex optimization problems with long-term constraints , which are constraints
that need to be satisfied when accumulated over a finite number of rounds T ,
but can be violated in intermediate rounds. For some user-defined trade-off
parameter $\b... | computer science |
11,404 | Satisficing in multi-armed bandit problems | cs.LG | Satisficing is a relaxation of maximizing and allows for less risky decision
making in the face of uncertainty. We propose two sets of satisficing
objectives for the multi-armed bandit problem, where the objective is to
achieve reward-based decision-making performance above a given threshold. We
show that these new pro... | computer science |
11,405 | Inverse Reinforcement Learning via Deep Gaussian Process | cs.LG | We propose a new approach to inverse reinforcement learning (IRL) based on
the deep Gaussian process (deep GP) model, which is capable of learning
complicated reward structures with few demonstrations. Our model stacks
multiple latent GP layers to learn abstract representations of the state
feature space, which is link... | computer science |
11,406 | Sparse group factor analysis for biclustering of multiple data sources | cs.LG | Motivation: Modelling methods that find structure in data are necessary with
the current large volumes of genomic data, and there have been various efforts
to find subsets of genes exhibiting consistent patterns over subsets of
treatments. These biclustering techniques have focused on one data source,
often gene expres... | computer science |
11,407 | Strategies and Principles of Distributed Machine Learning on Big Data | stat.ML | The rise of Big Data has led to new demands for Machine Learning (ML) systems
to learn complex models with millions to billions of parameters, that promise
adequate capacity to digest massive datasets and offer powerful predictive
analytics thereupon. In order to run ML algorithms at such scales, on a
distributed clust... | computer science |
11,408 | Practical Algorithms for Learning Near-Isometric Linear Embeddings | stat.ML | We propose two practical non-convex approaches for learning near-isometric,
linear embeddings of finite sets of data points. Given a set of training points
$\mathcal{X}$, we consider the secant set $S(\mathcal{X})$ that consists of all
pairwise difference vectors of $\mathcal{X}$, normalized to lie on the unit
sphere. ... | computer science |
11,409 | Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in
Wireless Sensor Networks | stat.ML | This letter proposes a sparse diffusion steepest-descent algorithm for one
bit compressed sensing in wireless sensor networks. The approach exploits the
diffusion strategy from distributed learning in the one bit compressed sensing
framework. To estimate a common sparse vector cooperatively from only the sign
of measur... | computer science |
11,410 | Variational Inference: A Review for Statisticians | stat.CO | One of the core problems of modern statistics is to approximate
difficult-to-compute probability densities. This problem is especially
important in Bayesian statistics, which frames all inference about unknown
quantities as a calculation involving the posterior density. In this paper, we
review variational inference (V... | computer science |
11,411 | Song Recommendation with Non-Negative Matrix Factorization and Graph
Total Variation | stat.ML | This work formulates a novel song recommender system as a matrix completion
problem that benefits from collaborative filtering through Non-negative Matrix
Factorization (NMF) and content-based filtering via total variation (TV) on
graphs. The graphs encode both playlist proximity information and song
similarity, using ... | computer science |
11,412 | On Computationally Tractable Selection of Experiments in
Measurement-Constrained Regression Models | stat.ML | We derive computationally tractable methods to select a small subset of
experiment settings from a large pool of given design points. The primary focus
is on linear regression models, while the technique extends to generalized
linear models and Delta's method (estimating functions of linear regression
models) as well. ... | computer science |
11,413 | Temporal Multinomial Mixture for Instance-Oriented Evolutionary
Clustering | cs.IR | Evolutionary clustering aims at capturing the temporal evolution of clusters.
This issue is particularly important in the context of social media data that
are naturally temporally driven. In this paper, we propose a new probabilistic
model-based evolutionary clustering technique. The Temporal Multinomial Mixture
(TMM)... | computer science |
11,414 | How to learn a graph from smooth signals | stat.ML | We propose a framework that learns the graph structure underlying a set of
smooth signals. Given $X\in\mathbb{R}^{m\times n}$ whose rows reside on the
vertices of an unknown graph, we learn the edge weights
$w\in\mathbb{R}_+^{m(m-1)/2}$ under the smoothness assumption that
$\text{tr}{X^\top LX}$ is small. We show that ... | computer science |
11,415 | On the consistency of inversion-free parameter estimation for Gaussian
random fields | math.ST | Gaussian random fields are a powerful tool for modeling environmental
processes. For high dimensional samples, classical approaches for estimating
the covariance parameters require highly challenging and massive computations,
such as the evaluation of the Cholesky factorization or solving linear systems.
Recently, Anit... | computer science |
11,416 | On-line Bayesian System Identification | cs.SY | We consider an on-line system identification setting, in which new data
become available at given time steps. In order to meet real-time estimation
requirements, we propose a tailored Bayesian system identification procedure,
in which the hyper-parameters are still updated through Marginal Likelihood
maximization, but ... | computer science |
11,417 | Incremental Semiparametric Inverse Dynamics Learning | stat.ML | This paper presents a novel approach for incremental semiparametric inverse
dynamics learning. In particular, we consider the mixture of two approaches:
Parametric modeling based on rigid body dynamics equations and nonparametric
modeling based on incremental kernel methods, with no prior information on the
mechanical ... | computer science |
11,418 | Sparse Convex Clustering | stat.ME | Convex clustering, a convex relaxation of k-means clustering and hierarchical
clustering, has drawn recent attentions since it nicely addresses the
instability issue of traditional nonconvex clustering methods. Although its
computational and statistical properties have been recently studied, the
performance of convex c... | computer science |
11,419 | Sub-Sampled Newton Methods I: Globally Convergent Algorithms | math.OC | Large scale optimization problems are ubiquitous in machine learning and data
analysis and there is a plethora of algorithms for solving such problems. Many
of these algorithms employ sub-sampling, as a way to either speed up the
computations and/or to implicitly implement a form of statistical
regularization. In this ... | computer science |
11,420 | Sub-Sampled Newton Methods II: Local Convergence Rates | math.OC | Many data-fitting applications require the solution of an optimization
problem involving a sum of large number of functions of high dimensional
parameter. Here, we consider the problem of minimizing a sum of $n$ functions
over a convex constraint set $\mathcal{X} \subseteq \mathbb{R}^{p}$ where both
$n$ and $p$ are lar... | computer science |
11,421 | Local Network Community Detection with Continuous Optimization of
Conductance and Weighted Kernel K-Means | cs.SI | Local network community detection is the task of finding a single community
of nodes concentrated around few given seed nodes in a localized way.
Conductance is a popular objective function used in many algorithms for local
community detection. This paper studies a continuous relaxation of conductance.
We show that con... | computer science |
11,422 | A Mathematical Formalization of Hierarchical Temporal Memory's Spatial
Pooler | stat.ML | Hierarchical temporal memory (HTM) is an emerging machine learning algorithm,
with the potential to provide a means to perform predictions on spatiotemporal
data. The algorithm, inspired by the neocortex, currently does not have a
comprehensive mathematical framework. This work brings together all aspects of
the spatia... | computer science |
11,423 | On the Sample Complexity of Learning Graphical Games | cs.GT | We analyze the sample complexity of learning graphical games from purely
behavioral data. We assume that we can only observe the players' joint actions
and not their payoffs. We analyze the sufficient and necessary number of
samples for the correct recovery of the set of pure-strategy Nash equilibria
(PSNE) of the true... | computer science |
11,424 | Information-theoretic limits of Bayesian network structure learning | cs.LG | In this paper, we study the information-theoretic limits of learning the
structure of Bayesian networks (BNs), on discrete as well as continuous random
variables, from a finite number of samples. We show that the minimum number of
samples required by any procedure to recover the correct structure grows as
$\Omega(m)$ a... | computer science |
11,425 | Unsupervised Learning in Neuromemristive Systems | cs.ET | Neuromemristive systems (NMSs) currently represent the most promising
platform to achieve energy efficient neuro-inspired computation. However, since
the research field is less than a decade old, there are still countless
algorithms and design paradigms to be explored within these systems. One
particular domain that re... | computer science |
11,426 | Revealing Fundamental Physics from the Daya Bay Neutrino Experiment
using Deep Neural Networks | stat.ML | Experiments in particle physics produce enormous quantities of data that must
be analyzed and interpreted by teams of physicists. This analysis is often
exploratory, where scientists are unable to enumerate the possible types of
signal prior to performing the experiment. Thus, tools for summarizing,
clustering, visuali... | computer science |
11,427 | Log-Normal Matrix Completion for Large Scale Link Prediction | cs.SI | The ubiquitous proliferation of online social networks has led to the
widescale emergence of relational graphs expressing unique patterns in link
formation and descriptive user node features. Matrix Factorization and
Completion have become popular methods for Link Prediction due to the low rank
nature of mutual node fr... | computer science |
11,428 | Information-Theoretic Lower Bounds for Recovery of Diffusion Network
Structures | cs.LG | We study the information-theoretic lower bound of the sample complexity of
the correct recovery of diffusion network structures. We introduce a
discrete-time diffusion model based on the Independent Cascade model for which
we obtain a lower bound of order $\Omega(k \log p)$, for directed graphs of $p$
nodes, and at mos... | computer science |
11,429 | System Identification through Online Sparse Gaussian Process Regression
with Input Noise | stat.ML | There has been a growing interest in using non-parametric regression methods
like Gaussian Process (GP) regression for system identification. GP regression
does traditionally have three important downsides: (1) it is computationally
intensive, (2) it cannot efficiently implement newly obtained measurements
online, and ... | computer science |
11,430 | Learning Data Triage: Linear Decoding Works for Compressive MRI | cs.IT | The standard approach to compressive sampling considers recovering an unknown
deterministic signal with certain known structure, and designing the
sub-sampling pattern and recovery algorithm based on the known structure. This
approach requires looking for a good representation that reveals the signal
structure, and sol... | computer science |
11,431 | Better safe than sorry: Risky function exploitation through safe
optimization | stat.AP | Exploration-exploitation of functions, that is learning and optimizing a
mapping between inputs and expected outputs, is ubiquitous to many real world
situations. These situations sometimes require us to avoid certain outcomes at
all cost, for example because they are poisonous, harmful, or otherwise
dangerous. We test... | computer science |
11,432 | Interactive algorithms: from pool to stream | stat.ML | We consider interactive algorithms in the pool-based setting, and in the
stream-based setting. Interactive algorithms observe suggested elements
(representing actions or queries), and interactively select some of them and
receive responses. Pool-based algorithms can select elements at any order,
while stream-based algo... | computer science |
11,433 | Compressive Spectral Clustering | cs.DS | Spectral clustering has become a popular technique due to its high
performance in many contexts. It comprises three main steps: create a
similarity graph between N objects to cluster, compute the first k eigenvectors
of its Laplacian matrix to define a feature vector for each object, and run
k-means on these features t... | computer science |
11,434 | A Note on Alternating Minimization Algorithm for the Matrix Completion
Problem | stat.ML | We consider the problem of reconstructing a low rank matrix from a subset of
its entries and analyze two variants of the so-called Alternating Minimization
algorithm, which has been proposed in the past. We establish that when the
underlying matrix has rank $r=1$, has positive bounded entries, and the graph
$\mathcal{G... | computer science |
11,435 | Recovery guarantee of weighted low-rank approximation via alternating
minimization | cs.LG | Many applications require recovering a ground truth low-rank matrix from
noisy observations of the entries, which in practice is typically formulated as
a weighted low-rank approximation problem and solved by non-convex optimization
heuristics such as alternating minimization. In this paper, we provide provable
recover... | computer science |
11,436 | Importance Sampling for Minibatches | cs.LG | Minibatching is a very well studied and highly popular technique in
supervised learning, used by practitioners due to its ability to accelerate
training through better utilization of parallel processing power and reduction
of stochastic variance. Another popular technique is importance sampling -- a
strategy for prefer... | computer science |
11,437 | Stratified Bayesian Optimization | cs.LG | We consider derivative-free black-box global optimization of expensive noisy
functions, when most of the randomness in the objective is produced by a few
influential scalar random inputs. We present a new Bayesian global optimization
algorithm, called Stratified Bayesian Optimization (SBO), which uses this
strong depen... | computer science |
11,438 | Hyperparameter optimization with approximate gradient | stat.ML | Most models in machine learning contain at least one hyperparameter to
control for model complexity. Choosing an appropriate set of hyperparameters is
both crucial in terms of model accuracy and computationally challenging. In
this work we propose an algorithm for the optimization of continuous
hyperparameters using in... | computer science |
11,439 | Data-Efficient Reinforcement Learning in Continuous-State POMDPs | stat.ML | We present a data-efficient reinforcement learning algorithm resistant to
observation noise. Our method extends the highly data-efficient PILCO algorithm
(Deisenroth & Rasmussen, 2011) into partially observed Markov decision
processes (POMDPs) by considering the filtering process during policy
evaluation. PILCO conduct... | computer science |
11,440 | Collaborative filtering via sparse Markov random fields | stat.ML | Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov rand... | computer science |
11,441 | Graphical Model Sketch | cs.DS | Structured high-cardinality data arises in many domains, and poses a major
challenge for both modeling and inference. Graphical models are a popular
approach to modeling structured data but they are unsuitable for
high-cardinality variables. The count-min (CM) sketch is a popular approach to
estimating probabilities in... | computer science |
11,442 | Conditional Dependence via Shannon Capacity: Axioms, Estimators and
Applications | cs.IT | We conduct an axiomatic study of the problem of estimating the strength of a
known causal relationship between a pair of variables. We propose that an
estimate of causal strength should be based on the conditional distribution of
the effect given the cause (and not on the driving distribution of the cause),
and study d... | computer science |
11,443 | Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing | cs.LG | Crowdsourcing platforms provide marketplaces where task requesters can pay to
get labels on their data. Such markets have emerged recently as popular venues
for collecting annotations that are crucial in training machine learning models
in various applications. However, as jobs are tedious and payments are low,
errors ... | computer science |
11,444 | High Dimensional Inference with Random Maximum A-Posteriori
Perturbations | cs.LG | This paper presents a new approach, called perturb-max, for high-dimensional
statistical inference that is based on applying random perturbations followed
by optimization. This framework injects randomness to maximum a-posteriori
(MAP) predictors by randomly perturbing the potential function for the input. A
classic re... | computer science |
11,445 | Orthogonal Sparse PCA and Covariance Estimation via Procrustes
Reformulation | stat.ML | The problem of estimating sparse eigenvectors of a symmetric matrix attracts
a lot of attention in many applications, especially those with high dimensional
data set. While classical eigenvectors can be obtained as the solution of a
maximization problem, existing approaches formulate this problem by adding a
penalty te... | computer science |
11,446 | Evaluation of Protein Structural Models Using Random Forests | cs.LG | Protein structure prediction has been a grand challenge problem in the
structure biology over the last few decades. Protein quality assessment plays a
very important role in protein structure prediction. In the paper, we propose a
new protein quality assessment method which can predict both local and global
quality of ... | computer science |
11,447 | Deep Learning on FPGAs: Past, Present, and Future | cs.DC | The rapid growth of data size and accessibility in recent years has
instigated a shift of philosophy in algorithm design for artificial
intelligence. Instead of engineering algorithms by hand, the ability to learn
composable systems automatically from massive amounts of data has led to
ground-breaking performance in im... | computer science |
11,448 | Convex Optimization for Linear Query Processing under Approximate
Differential Privacy | cs.DB | Differential privacy enables organizations to collect accurate aggregates
over sensitive data with strong, rigorous guarantees on individuals' privacy.
Previous work has found that under differential privacy, computing multiple
correlated aggregates as a batch, using an appropriate \emph{strategy}, may
yield higher acc... | computer science |
11,449 | Joint Dimensionality Reduction for Two Feature Vectors | stat.ML | Many machine learning problems, especially multi-modal learning problems,
have two sets of distinct features (e.g., image and text features in news story
classification, or neuroimaging data and neurocognitive data in cognitive
science research). This paper addresses the joint dimensionality reduction of
two feature ve... | computer science |
11,450 | Frequency Analysis of Temporal Graph Signals | cs.LG | This letter extends the concept of graph-frequency to graph signals that
evolve with time. Our goal is to generalize and, in fact, unify the familiar
concepts from time- and graph-frequency analysis. To this end, we study a joint
temporal and graph Fourier transform (JFT) and demonstrate its attractive
properties. We b... | computer science |
11,451 | Autoregressive Moving Average Graph Filtering | cs.LG | One of the cornerstones of the field of signal processing on graphs are graph
filters, direct analogues of classical filters, but intended for signals
defined on graphs. This work brings forth new insights on the distributed graph
filtering problem. We design a family of autoregressive moving average (ARMA)
recursions,... | computer science |
11,452 | Secure Approximation Guarantee for Cryptographically Private Empirical
Risk Minimization | stat.ML | Privacy concern has been increasingly important in many machine learning (ML)
problems. We study empirical risk minimization (ERM) problems under secure
multi-party computation (MPC) frameworks. Main technical tools for MPC have
been developed based on cryptography. One of limitations in current
cryptographically priva... | computer science |
11,453 | Optimal Best Arm Identification with Fixed Confidence | math.ST | We give a complete characterization of the complexity of best-arm
identification in one-parameter bandit problems. We prove a new, tight lower
bound on the sample complexity. We propose the `Track-and-Stop' strategy, which
we prove to be asymptotically optimal. It consists in a new sampling rule
(which tracks the optim... | computer science |
11,454 | Quantum Perceptron Models | cs.LG | We demonstrate how quantum computation can provide non-trivial improvements
in the computational and statistical complexity of the perceptron model. We
develop two quantum algorithms for perceptron learning. The first algorithm
exploits quantum information processing to determine a separating hyperplane
using a number ... | computer science |
11,455 | DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution
Regression | stat.ML | Performing exact posterior inference in complex generative models is often
difficult or impossible due to an expensive to evaluate or intractable
likelihood function. Approximate Bayesian computation (ABC) is an inference
framework that constructs an approximation to the true likelihood based on the
similarity between ... | computer science |
11,456 | Gradient Descent Converges to Minimizers | stat.ML | We show that gradient descent converges to a local minimizer, almost surely
with random initialization. This is proved by applying the Stable Manifold
Theorem from dynamical systems theory. | computer science |
11,457 | Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector
Data Description | cs.LG | Support Vector Data Description (SVDD) is a machine-learning technique used
for single class classification and outlier detection. SVDD formulation with
kernel function provides a flexible boundary around data. The value of kernel
function parameters affects the nature of the data boundary. For example, it is
observed ... | computer science |
11,458 | Choice by Elimination via Deep Neural Networks | stat.ML | We introduce Neural Choice by Elimination, a new framework that integrates
deep neural networks into probabilistic sequential choice models for learning
to rank. Given a set of items to chose from, the elimination strategy starts
with the whole item set and iteratively eliminates the least worthy item in the
remaining ... | computer science |
11,459 | Large Scale Kernel Learning using Block Coordinate Descent | cs.LG | We demonstrate that distributed block coordinate descent can quickly solve
kernel regression and classification problems with millions of data points.
Armed with this capability, we conduct a thorough comparison between the full
kernel, the Nystr\"om method, and random features on three large classification
tasks from ... | computer science |
11,460 | Harder, Better, Faster, Stronger Convergence Rates for Least-Squares
Regression | math.OC | We consider the optimization of a quadratic objective function whose
gradients are only accessible through a stochastic oracle that returns the
gradient at any given point plus a zero-mean finite variance random error. We
present the first algorithm that achieves jointly the optimal prediction error
rates for least-squ... | computer science |
11,461 | First-order Methods for Geodesically Convex Optimization | math.OC | Geodesic convexity generalizes the notion of (vector space) convexity to
nonlinear metric spaces. But unlike convex optimization, geodesically convex
(g-convex) optimization is much less developed. In this paper we contribute to
the understanding of g-convex optimization by developing iteration complexity
analysis for ... | computer science |
11,462 | GAP Safe Screening Rules for Sparse-Group-Lasso | stat.ML | In high dimensional settings, sparse structures are crucial for efficiency,
either in term of memory, computation or performance. In some contexts, it is
natural to handle more refined structures than pure sparsity, such as for
instance group sparsity. Sparse-Group Lasso has recently been introduced in the
context of l... | computer science |
11,463 | Semi-Markov Switching Vector Autoregressive Model-based Anomaly
Detection in Aviation Systems | cs.LG | In this work we consider the problem of anomaly detection in heterogeneous,
multivariate, variable-length time series datasets. Our focus is on the
aviation safety domain, where data objects are flights and time series are
sensor readings and pilot switches. In this context the goal is to detect
anomalous flight segmen... | computer science |
11,464 | 2-Bit Random Projections, NonLinear Estimators, and Approximate Near
Neighbor Search | stat.ML | The method of random projections has become a standard tool for machine
learning, data mining, and search with massive data at Web scale. The effective
use of random projections requires efficient coding schemes for quantizing
(real-valued) projected data into integers. In this paper, we focus on a simple
2-bit coding ... | computer science |
11,465 | Convexification of Learning from Constraints | cs.LG | Regularized empirical risk minimization with constrained labels (in contrast
to fixed labels) is a remarkably general abstraction of learning. For common
loss and regularization functions, this optimization problem assumes the form
of a mixed integer program (MIP) whose objective function is non-convex. In
this form, t... | computer science |
11,466 | Principal Component Projection Without Principal Component Analysis | cs.DS | We show how to efficiently project a vector onto the top principal components
of a matrix, without explicitly computing these components. Specifically, we
introduce an iterative algorithm that provably computes the projection using
few calls to any black-box routine for ridge regression.
By avoiding explicit principa... | computer science |
11,467 | Sparse Linear Regression via Generalized Orthogonal Least-Squares | stat.ML | Sparse linear regression, which entails finding a sparse solution to an
underdetermined system of linear equations, can formally be expressed as an
$l_0$-constrained least-squares problem. The Orthogonal Least-Squares (OLS)
algorithm sequentially selects the features (i.e., columns of the coefficient
matrix) to greedil... | computer science |
11,468 | An Improved Gap-Dependency Analysis of the Noisy Power Method | stat.ML | We consider the noisy power method algorithm, which has wide applications in
machine learning and statistics, especially those related to principal
component analysis (PCA) under resource (communication, memory or privacy)
constraints. Existing analysis of the noisy power method shows an
unsatisfactory dependency over ... | computer science |
11,469 | Lens depth function and k-relative neighborhood graph: versatile tools
for ordinal data analysis | stat.ML | In recent years it has become popular to study machine learning problems in a
setting of ordinal distance information rather than numerical distance
measurements. By ordinal distance information we refer to binary answers to
distance comparisons such as $d(A,B)<d(C,D)$. For many problems in machine
learning and statist... | computer science |
11,470 | Max-Margin Nonparametric Latent Feature Models for Link Prediction | cs.LG | Link prediction is a fundamental task in statistical network analysis. Recent
advances have been made on learning flexible nonparametric Bayesian latent
feature models for link prediction. In this paper, we present a max-margin
learning method for such nonparametric latent feature relational models. Our
approach attemp... | computer science |
11,471 | Online Dual Coordinate Ascent Learning | math.OC | The stochastic dual coordinate-ascent (S-DCA) technique is a useful
alternative to the traditional stochastic gradient-descent algorithm for
solving large-scale optimization problems due to its scalability to large data
sets and strong theoretical guarantees. However, the available S-DCA
formulation is limited to finit... | computer science |
11,472 | A Compressed Sensing Based Decomposition of Electrodermal Activity
Signals | stat.ML | The measurement and analysis of Electrodermal Activity (EDA) offers
applications in diverse areas ranging from market research, to seizure
detection, to human stress analysis. Unfortunately, the analysis of EDA signals
is made difficult by the superposition of numerous components which can obscure
the signal informatio... | computer science |
11,473 | Fast Nonsmooth Regularized Risk Minimization with Continuation | cs.LG | In regularized risk minimization, the associated optimization problem becomes
particularly difficult when both the loss and regularizer are nonsmooth.
Existing approaches either have slow or unclear convergence properties, are
restricted to limited problem subclasses, or require careful setting of a
smoothing parameter... | computer science |
11,474 | A Structured Variational Auto-encoder for Learning Deep Hierarchies of
Sparse Features | stat.ML | In this note we present a generative model of natural images consisting of a
deep hierarchy of layers of latent random variables, each of which follows a
new type of distribution that we call rectified Gaussian. These rectified
Gaussian units allow spike-and-slab type sparsity, while retaining the
differentiability nec... | computer science |
11,475 | Does quantification without adjustments work? | stat.ML | Classification is the task of predicting the class labels of objects based on
the observation of their features. In contrast, quantification has been defined
as the task of determining the prevalences of the different sorts of class
labels in a target dataset. The simplest approach to quantification is Classify
& Count... | computer science |
11,476 | Without-Replacement Sampling for Stochastic Gradient Methods:
Convergence Results and Application to Distributed Optimization | cs.LG | Stochastic gradient methods for machine learning and optimization problems
are usually analyzed assuming data points are sampled \emph{with} replacement.
In practice, however, sampling \emph{without} replacement is very common,
easier to implement in many cases, and often performs better. In this paper, we
provide comp... | computer science |
11,477 | Confidence-Constrained Maximum Entropy Framework for Learning from
Multi-Instance Data | cs.LG | Multi-instance data, in which each object (bag) contains a collection of
instances, are widespread in machine learning, computer vision, bioinformatics,
signal processing, and social sciences. We present a maximum entropy (ME)
framework for learning from multi-instance data. In this approach each bag is
represented as ... | computer science |
11,478 | Optimal dictionary for least squares representation | cs.LG | Dictionaries are collections of vectors used for representations of random
vectors in Euclidean spaces. Recent research on optimal dictionaries is focused
on constructing dictionaries that offer sparse representations, i.e.,
$\ell_0$-optimal representations. Here we consider the problem of finding
optimal dictionaries ... | computer science |
11,479 | Stochastic dual averaging methods using variance reduction techniques
for regularized empirical risk minimization problems | math.OC | We consider a composite convex minimization problem associated with
regularized empirical risk minimization, which often arises in machine
learning. We propose two new stochastic gradient methods that are based on
stochastic dual averaging method with variance reduction. Our methods generate
a sparser solution than the... | computer science |
11,480 | A Bayesian non-parametric method for clustering high-dimensional binary
data | stat.AP | In many real life problems, objects are described by large number of binary
features. For instance, documents are characterized by presence or absence of
certain keywords; cancer patients are characterized by presence or absence of
certain mutations etc. In such cases, grouping together similar
objects/profiles based o... | computer science |
11,481 | On the inconsistency of $\ell_1$-penalised sparse precision matrix
estimation | cs.LG | Various $\ell_1$-penalised estimation methods such as graphical lasso and
CLIME are widely used for sparse precision matrix estimation. Many of these
methods have been shown to be consistent under various quantitative assumptions
about the underlying true covariance matrix. Intuitively, these conditions are
related to ... | computer science |
11,482 | Small ensembles of kriging models for optimization | math.OC | The Efficient Global Optimization (EGO) algorithm uses a conditional
Gaus-sian Process (GP) to approximate an objective function known at a finite
number of observation points and sequentially adds new points which maximize
the Expected Improvement criterion according to the GP. The important factor
that controls the e... | computer science |
11,483 | megaman: Manifold Learning with Millions of points | cs.LG | Manifold Learning is a class of algorithms seeking a low-dimensional
non-linear representation of high-dimensional data. Thus manifold learning
algorithms are, at least in theory, most applicable to high-dimensional data
and sample sizes to enable accurate estimation of the manifold. Despite this,
most existing manifol... | computer science |
11,484 | Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic
Differentiation | cs.MS | Optimization on manifolds is a class of methods for optimization of an
objective function, subject to constraints which are smooth, in the sense that
the set of points which satisfy the constraints admits the structure of a
differentiable manifold. While many optimization problems are of the described
form, technicalit... | computer science |
11,485 | A Primer on the Signature Method in Machine Learning | stat.ML | In these notes, we wish to provide an introduction to the signature method,
focusing on its basic theoretical properties and recent numerical applications.
The notes are split into two parts. The first part focuses on the definition
and fundamental properties of the signature of a path, or the path signature.
We have... | computer science |
11,486 | Pufferfish Privacy Mechanisms for Correlated Data | cs.LG | Many modern databases include personal and sensitive correlated data, such as
private information on users connected together in a social network, and
measurements of physical activity of single subjects across time. However,
differential privacy, the current gold standard in data privacy, does not
adequately address p... | computer science |
11,487 | On the Influence of Momentum Acceleration on Online Learning | math.OC | The article examines in some detail the convergence rate and
mean-square-error performance of momentum stochastic gradient methods in the
constant step-size and slow adaptation regime. The results establish that
momentum methods are equivalent to the standard stochastic gradient method with
a re-scaled (larger) step-si... | computer science |
11,488 | A Variational Perspective on Accelerated Methods in Optimization | math.OC | Accelerated gradient methods play a central role in optimization, achieving
optimal rates in many settings. While many generalizations and extensions of
Nesterov's original acceleration method have been proposed, it is not yet clear
what is the natural scope of the acceleration concept. In this paper, we study
accelera... | computer science |
11,489 | Matching While Learning | cs.LG | We consider the problem faced by a service platform that needs to match
supply with demand, but also to learn attributes of new arrivals in order to
match them better in the future. We introduce a benchmark model with
heterogeneous workers and jobs that arrive over time. Job types are known to
the platform, but worker ... | computer science |
11,490 | Repeated Games with Vector Losses: A Set-valued Dynamic Programming
Approach | cs.GT | We consider infinitely repeated games with vector losses discounted over
time. We characterize the set of minimal upper bounds on expected losses that a
player can simultaneously guarantee across the different dimensions.
Specifically, we show that this set is the fixed point of a set-valued dynamic
programming operato... | computer science |
11,491 | Online semi-parametric learning for inverse dynamics modeling | math.OC | This paper presents a semi-parametric algorithm for online learning of a
robot inverse dynamics model. It combines the strength of the parametric and
non-parametric modeling. The former exploits the rigid body dynamics equa-
tion, while the latter exploits a suitable kernel function. We provide an
extensive comparison ... | computer science |
11,492 | Optimal Black-Box Reductions Between Optimization Objectives | math.OC | The diverse world of machine learning applications has given rise to a
plethora of algorithms and optimization methods, finely tuned to the specific
regression or classification task at hand. We reduce the complexity of
algorithm design for machine learning by reductions: we develop reductions that
take a method develo... | computer science |
11,493 | Katyusha: The First Direct Acceleration of Stochastic Gradient Methods | math.OC | Nesterov's momentum trick is famously known for accelerating gradient
descent, and has been proven useful in building fast iterative algorithms.
However, in the stochastic setting, counterexamples exist and prevent
Nesterov's momentum from providing similar acceleration, even if the underlying
problem is convex.
We i... | computer science |
11,494 | Tensor Methods and Recommender Systems | cs.LG | A substantial progress in development of new and efficient tensor
factorization techniques has led to an extensive research of their
applicability in recommender systems field. Tensor-based recommender models
push the boundaries of traditional collaborative filtering techniques by taking
into account a multifaceted nat... | computer science |
11,495 | Fast Incremental Method for Nonconvex Optimization | math.OC | We analyze a fast incremental aggregated gradient method for optimizing
nonconvex problems of the form $\min_x \sum_i f_i(x)$. Specifically, we analyze
the SAGA algorithm within an Incremental First-order Oracle framework, and show
that it converges to a stationary point provably faster than both gradient
descent and s... | computer science |
11,496 | Localized Lasso for High-Dimensional Regression | stat.ML | We introduce the localized Lasso, which is suited for learning models that
are both interpretable and have a high predictive power in problems with high
dimensionality $d$ and small sample size $n$. More specifically, we consider a
function defined by local sparse models, one at each data point. We introduce
sample-wis... | computer science |
11,497 | On kernel methods for covariates that are rankings | stat.ML | Permutation-valued features arise in a variety of applications, either in a
direct way when preferences are elicited over a collection of items, or an
indirect way in which numerical ratings are converted to a ranking. To date,
there has been relatively limited study of regression, classification, and
testing problems ... | computer science |
11,498 | Submodular Variational Inference for Network Reconstruction | cs.LG | In real-world and online social networks, individuals receive and transmit
information in real time. Cascading information transmissions (e.g. phone
calls, text messages, social media posts) may be understood as a realization of
a diffusion process operating on the network, and its branching path can be
represented by ... | computer science |
11,499 | Regret Analysis of the Anytime Optimally Confident UCB Algorithm | cs.LG | I introduce and analyse an anytime version of the Optimally Confident UCB
(OCUCB) algorithm designed for minimising the cumulative regret in finite-armed
stochastic bandits with subgaussian noise. The new algorithm is simple,
intuitive (in hindsight) and comes with the strongest finite-time regret
guarantees for a hori... | computer science |
11,500 | Towards Geo-Distributed Machine Learning | cs.LG | Latency to end-users and regulatory requirements push large companies to
build data centers all around the world. The resulting data is "born"
geographically distributed. On the other hand, many machine learning
applications require a global view of such data in order to achieve the best
results. These types of applica... | computer science |
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