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11,601 | Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization | math.OC | In this paper we study stochastic quasi-Newton methods for nonconvex
stochastic optimization, where we assume that noisy information about the
gradients of the objective function is available via a stochastic first-order
oracle (SFO). We propose a general framework for such methods, for which we
prove almost sure conve... | computer science |
11,602 | Single-Channel Multi-Speaker Separation using Deep Clustering | cs.LG | Deep clustering is a recently introduced deep learning architecture that uses
discriminatively trained embeddings as the basis for clustering. It was
recently applied to spectrogram segmentation, resulting in impressive results
on speaker-independent multi-speaker separation. In this paper we extend the
baseline system... | computer science |
11,603 | Proceedings of the 2016 ICML Workshop on #Data4Good: Machine Learning in
Social Good Applications | stat.ML | This is the Proceedings of the ICML Workshop on #Data4Good: Machine Learning
in Social Good Applications, which was held on June 24, 2016 in New York. | computer science |
11,604 | Classifier Risk Estimation under Limited Labeling Resources | cs.LG | In this paper we propose strategies for estimating performance of a
classifier when labels cannot be obtained for the whole test set. The number of
test instances which can be labeled is very small compared to the whole test
data size. The goal then is to obtain a precise estimate of classifier
performance using as lit... | computer science |
11,605 | On Faster Convergence of Cyclic Block Coordinate Descent-type Methods
for Strongly Convex Minimization | math.OC | The cyclic block coordinate descent-type (CBCD-type) methods, which performs
iterative updates for a few coordinates (a block) simultaneously throughout the
procedure, have shown remarkable computational performance for solving strongly
convex minimization problems. Typical applications include many popular
statistical... | computer science |
11,606 | From Behavior to Sparse Graphical Games: Efficient Recovery of
Equilibria | cs.GT | In this paper we study the problem of exact recovery of the pure-strategy
Nash equilibria (PSNE) set of a graphical game from noisy observations of joint
actions of the players alone. We consider sparse linear influence games --- a
parametric class of graphical games with linear payoffs, and represented by
directed gra... | computer science |
11,607 | Proximal Quasi-Newton Methods for Regularized Convex Optimization with
Linear and Accelerated Sublinear Convergence Rates | cs.NA | In [19], a general, inexact, efficient proximal quasi-Newton algorithm for
composite optimization problems has been proposed and a sublinear global
convergence rate has been established. In this paper, we analyze the
convergence properties of this method, both in the exact and inexact setting,
in the case when the obje... | computer science |
11,608 | Kernel-based methods for bandit convex optimization | cs.LG | We consider the adversarial convex bandit problem and we build the first
$\mathrm{poly}(T)$-time algorithm with $\mathrm{poly}(n) \sqrt{T}$-regret for
this problem. To do so we introduce three new ideas in the derivative-free
optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli
convolutions, ... | computer science |
11,609 | How to calculate partition functions using convex programming
hierarchies: provable bounds for variational methods | cs.LG | We consider the problem of approximating partition functions for Ising
models. We make use of recent tools in combinatorial optimization: the
Sherali-Adams and Lasserre convex programming hierarchies, in combination with
variational methods to get algorithms for calculating partition functions in
these families. These ... | computer science |
11,610 | On Deterministic Conditions for Subspace Clustering under Missing Data | cs.IT | In this paper we present deterministic conditions for success of sparse
subspace clustering (SSC) under missing data, when data is assumed to come from
a Union of Subspaces (UoS) model. We consider two algorithms, which are
variants of SSC with entry-wise zero-filling that differ in terms of the
optimization problems u... | computer science |
11,611 | Approximate maximum entropy principles via Goemans-Williamson with
applications to provable variational methods | cs.LG | The well known maximum-entropy principle due to Jaynes, which states that
given mean parameters, the maximum entropy distribution matching them is in an
exponential family, has been very popular in machine learning due to its
"Occam's razor" interpretation. Unfortunately, calculating the potentials in
the maximum-entro... | computer science |
11,612 | Learning in Quantum Control: High-Dimensional Global Optimization for
Noisy Quantum Dynamics | cs.LG | Quantum control is valuable for various quantum technologies such as
high-fidelity gates for universal quantum computing, adaptive quantum-enhanced
metrology, and ultra-cold atom manipulation. Although supervised machine
learning and reinforcement learning are widely used for optimizing control
parameters in classical ... | computer science |
11,613 | Fast Sampling for Strongly Rayleigh Measures with Application to
Determinantal Point Processes | cs.LG | In this note we consider sampling from (non-homogeneous) strongly Rayleigh
probability measures. As an important corollary, we obtain a fast mixing Markov
Chain sampler for Determinantal Point Processes. | computer science |
11,614 | Fitting a Simplicial Complex using a Variation of k-means | cs.LG | We give a simple and effective two stage algorithm for approximating a point
cloud $\mathcal{S}\subset\mathbb{R}^m$ by a simplicial complex $K$. The first
stage is an iterative fitting procedure that generalizes k-means clustering,
while the second stage involves deleting redundant simplices. A form of
dimension reduct... | computer science |
11,615 | Fifty Shades of Ratings: How to Benefit from a Negative Feedback in
Top-N Recommendations Tasks | cs.LG | Conventional collaborative filtering techniques treat a top-n recommendations
problem as a task of generating a list of the most relevant items. This
formulation, however, disregards an opposite - avoiding recommendations with
completely irrelevant items. Due to that bias, standard algorithms, as well as
commonly used ... | computer science |
11,616 | Random projections of random manifolds | stat.ML | Interesting data often concentrate on low dimensional smooth manifolds inside
a high dimensional ambient space. Random projections are a simple, powerful
tool for dimensionality reduction of such data. Previous works have studied
bounds on how many projections are needed to accurately preserve the geometry
of these man... | computer science |
11,617 | Learning from Conditional Distributions via Dual Embeddings | cs.LG | Many machine learning tasks, such as learning with invariance and policy
evaluation in reinforcement learning, can be characterized as problems of
learning from conditional distributions. In such problems, each sample $x$
itself is associated with a conditional distribution $p(z|x)$ represented by
samples $\{z_i\}_{i=1... | computer science |
11,618 | Onsager-corrected deep learning for sparse linear inverse problems | cs.IT | Deep learning has gained great popularity due to its widespread success on
many inference problems. We consider the application of deep learning to the
sparse linear inverse problem encountered in compressive sensing, where one
seeks to recover a sparse signal from a small number of noisy linear
measurements. In this p... | computer science |
11,619 | Doubly Accelerated Methods for Faster CCA and Generalized
Eigendecomposition | math.OC | We study $k$-GenEV, the problem of finding the top $k$ generalized
eigenvectors, and $k$-CCA, the problem of finding the top $k$ vectors in
canonical-correlation analysis. We propose algorithms $\mathtt{LazyEV}$ and
$\mathtt{LazyCCA}$ to solve the two problems with running times linearly
dependent on the input size and... | computer science |
11,620 | Supervised quantum gate "teaching" for quantum hardware design | cs.LG | We show how to train a quantum network of pairwise interacting qubits such
that its evolution implements a target quantum algorithm into a given network
subset. Our strategy is inspired by supervised learning and is designed to help
the physical construction of a quantum computer which operates with minimal
external cl... | computer science |
11,621 | A Non-Parametric Control Chart For High Frequency Multivariate Data | cs.LG | Support Vector Data Description (SVDD) is a machine learning technique used
for single class classification and outlier detection. SVDD based K-chart was
first introduced by Sun and Tsung for monitoring multivariate processes when
underlying distribution of process parameters or quality characteristics depart
from Norm... | computer science |
11,622 | Simultaneous Estimation of Noise Variance and Number of Peaks in
Bayesian Spectral Deconvolution | cs.LG | The heuristic identification of peaks from noisy complex spectra often leads
to misunderstanding of the physical and chemical properties of matter. In this
paper, we propose a framework based on Bayesian inference, which enables us to
separate multipeak spectra into single peaks statistically and consists of two
steps.... | computer science |
11,623 | An Adaptive Matrix Factorization Approach for Personalized Recommender
Systems | cs.LG | Given a set $U$ of users and a set of items $I$, a dataset of recommendations
can be viewed as a sparse rectangular matrix $A$ of size $|U|\times |I|$ such
that $a_{u,i}$ contains the rating the user $u$ assigns to item $i$,
$a_{u,i}=?$ if the user $u$ has not rated the item $i$. The goal of a
recommender system is to ... | computer science |
11,624 | Network-Guided Biomarker Discovery | stat.ML | Identifying measurable genetic indicators (or biomarkers) of a specific
condition of a biological system is a key element of precision medicine. Indeed
it allows to tailor diagnostic, prognostic and treatment choice to individual
characteristics of a patient. In machine learning terms, biomarker discovery
can be framed... | computer science |
11,625 | Stochastic Frank-Wolfe Methods for Nonconvex Optimization | math.OC | We study Frank-Wolfe methods for nonconvex stochastic and finite-sum
optimization problems. Frank-Wolfe methods (in the convex case) have gained
tremendous recent interest in machine learning and optimization communities due
to their projection-free property and their ability to exploit structured
constraints. However,... | computer science |
11,626 | Kernel functions based on triplet comparisons | stat.ML | Given only information in the form of similarity triplets "Object A is more
similar to object B than to object C" about a data set, we propose two ways of
defining a kernel function on the data set. While previous approaches construct
a low-dimensional Euclidean embedding of the data set that reflects the given
similar... | computer science |
11,627 | Fast and Simple Optimization for Poisson Likelihood Models | cs.LG | Poisson likelihood models have been prevalently used in imaging, social
networks, and time series analysis. We propose fast, simple,
theoretically-grounded, and versatile, optimization algorithms for Poisson
likelihood modeling. The Poisson log-likelihood is concave but not
Lipschitz-continuous. Since almost all gradie... | computer science |
11,628 | A General Characterization of the Statistical Query Complexity | cs.LG | Statistical query (SQ) algorithms are algorithms that have access to an {\em
SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$.
Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an
estimate of ${\bf E}_{ x\sim D}[\phi(x)]$ within some tolerance $\tau_\phi$
that roughly c... | computer science |
11,629 | Robust High-Dimensional Linear Regression | cs.LG | The effectiveness of supervised learning techniques has made them ubiquitous
in research and practice. In high-dimensional settings, supervised learning
commonly relies on dimensionality reduction to improve performance and identify
the most important factors in predicting outcomes. However, the economic
importance of ... | computer science |
11,630 | Semi-Supervised Prediction of Gene Regulatory Networks Using Machine
Learning Algorithms | cs.LG | Use of computational methods to predict gene regulatory networks (GRNs) from
gene expression data is a challenging task. Many studies have been conducted
using unsupervised methods to fulfill the task; however, such methods usually
yield low prediction accuracies due to the lack of training data. In this
article, we pr... | computer science |
11,631 | Warm Starting Bayesian Optimization | stat.ML | We develop a framework for warm-starting Bayesian optimization, that reduces
the solution time required to solve an optimization problem that is one in a
sequence of related problems. This is useful when optimizing the output of a
stochastic simulator that fails to provide derivative information, for which
Bayesian opt... | computer science |
11,632 | Chi-squared Amplification: Identifying Hidden Hubs | cs.LG | We consider the following general hidden hubs model: an $n \times n$ random
matrix $A$ with a subset $S$ of $k$ special rows (hubs): entries in rows
outside $S$ are generated from the probability distribution $p_0 \sim
N(0,\sigma_0^2)$; for each row in $S$, some $k$ of its entries are generated
from $p_1 \sim N(0,\sigm... | computer science |
11,633 | Content-based image retrieval tutorial | stat.ML | This paper functions as a tutorial for individuals interested to enter the
field of information retrieval but wouldn't know where to begin from. It
describes two fundamental yet efficient image retrieval techniques, the first
being k - nearest neighbors (knn) and the second support vector machines(svm).
The goal is to ... | computer science |
11,634 | An approach to dealing with missing values in heterogeneous data using
k-nearest neighbors | cs.LG | Techniques such as clusterization, neural networks and decision making
usually rely on algorithms that are not well suited to deal with missing
values. However, real world data frequently contains such cases. The simplest
solution is to either substitute them by a best guess value or completely
disregard the missing va... | computer science |
11,635 | A Geometrical Approach to Topic Model Estimation | stat.ME | In the probabilistic topic models, the quantity of interest---a low-rank
matrix consisting of topic vectors---is hidden in the text corpus matrix,
masked by noise, and the Singular Value Decomposition (SVD) is a potentially
useful tool for learning such a low-rank matrix. However, the connection
between this low-rank m... | computer science |
11,636 | Fast Calculation of the Knowledge Gradient for Optimization of
Deterministic Engineering Simulations | cs.CE | A novel efficient method for computing the Knowledge-Gradient policy for
Continuous Parameters (KGCP) for deterministic optimization is derived. The
differences with Expected Improvement (EI), a popular choice for Bayesian
optimization of deterministic engineering simulations, are explored. Both
policies and the Upper ... | computer science |
11,637 | Conformalized density- and distance-based anomaly detection in
time-series data | stat.AP | Anomalies (unusual patterns) in time-series data give essential, and often
actionable information in critical situations. Examples can be found in such
fields as healthcare, intrusion detection, finance, security and flight safety.
In this paper we propose new conformalized density- and distance-based anomaly
detection... | computer science |
11,638 | Linear Convergence of Gradient and Proximal-Gradient Methods Under the
Polyak-Łojasiewicz Condition | cs.LG | In 1963, Polyak proposed a simple condition that is sufficient to show a
global linear convergence rate for gradient descent. This condition is a
special case of the \L{}ojasiewicz inequality proposed in the same year, and it
does not require strong convexity (or even convexity). In this work, we show
that this much-ol... | computer science |
11,639 | Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets | stat.ML | The scale of functional magnetic resonance image data is rapidly increasing
as large multi-subject datasets are becoming widely available and
high-resolution scanners are adopted. The inherent low-dimensionality of the
information in this data has led neuroscientists to consider factor analysis
methods to extract and a... | computer science |
11,640 | Shape Constrained Tensor Decompositions using Sparse Representations in
Over-Complete Libraries | stat.ML | We consider $N$-way data arrays and low-rank tensor factorizations where the
time mode is coded as a sparse linear combination of temporal elements from an
over-complete library. Our method, Shape Constrained Tensor Decomposition
(SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces
$r$-rank appr... | computer science |
11,641 | A Bayesian Network approach to County-Level Corn Yield Prediction using
historical data and expert knowledge | cs.LG | Crop yield forecasting is the methodology of predicting crop yields prior to
harvest. The availability of accurate yield prediction frameworks have enormous
implications from multiple standpoints, including impact on the crop commodity
futures markets, formulation of agricultural policy, as well as crop insurance
ratin... | computer science |
11,642 | Conditional Sparse Linear Regression | cs.LG | Machine learning and statistics typically focus on building models that
capture the vast majority of the data, possibly ignoring a small subset of data
as "noise" or "outliers." By contrast, here we consider the problem of jointly
identifying a significant (but perhaps small) segment of a population in which
there is a... | computer science |
11,643 | Towards Instance Optimal Bounds for Best Arm Identification | cs.LG | In the classical best arm identification (Best-$1$-Arm) problem, we are given
$n$ stochastic bandit arms, each associated with a reward distribution with an
unknown mean. We would like to identify the arm with the largest mean with
probability at least $1-\delta$, using as few samples as possible.
Understanding the sam... | computer science |
11,644 | Survey of resampling techniques for improving classification performance
in unbalanced datasets | stat.AP | A number of classification problems need to deal with data imbalance between
classes. Often it is desired to have a high recall on the minority class while
maintaining a high precision on the majority class. In this paper, we review a
number of resampling techniques proposed in literature to handle unbalanced
datasets ... | computer science |
11,645 | Uniform Generalization, Concentration, and Adaptive Learning | cs.LG | One fundamental goal in any learning algorithm is to mitigate its risk for
overfitting. Mathematically, this requires that the learning algorithm enjoys a
small generalization risk, which is defined either in expectation or in
probability. Both types of generalization are commonly used in the literature.
For instance, ... | computer science |
11,646 | Computational and Statistical Tradeoffs in Learning to Rank | cs.LG | For massive and heterogeneous modern datasets, it is of fundamental interest
to provide guarantees on the accuracy of estimation when computational
resources are limited. In the application of learning to rank, we provide a
hierarchy of rank-breaking mechanisms ordered by the complexity in thus
generated sketch of the ... | computer science |
11,647 | Multi-Dueling Bandits and Their Application to Online Ranker Evaluation | cs.IR | New ranking algorithms are continually being developed and refined,
necessitating the development of efficient methods for evaluating these
rankers. Online ranker evaluation focuses on the challenge of efficiently
determining, from implicit user feedback, which ranker out of a finite set of
rankers is the best. Online ... | computer science |
11,648 | LFADS - Latent Factor Analysis via Dynamical Systems | cs.LG | Neuroscience is experiencing a data revolution in which many hundreds or
thousands of neurons are recorded simultaneously. Currently, there is little
consensus on how such data should be analyzed. Here we introduce LFADS (Latent
Factor Analysis via Dynamical Systems), a method to infer latent dynamics from
simultaneous... | computer science |
11,649 | AIDE: Fast and Communication Efficient Distributed Optimization | math.OC | In this paper, we present two new communication-efficient methods for
distributed minimization of an average of functions. The first algorithm is an
inexact variant of the DANE algorithm that allows any local algorithm to return
an approximate solution to a local subproblem. We show that such a strategy
does not affect... | computer science |
11,650 | Minimizing Quadratic Functions in Constant Time | cs.LG | A sampling-based optimization method for quadratic functions is proposed. Our
method approximately solves the following $n$-dimensional quadratic
minimization problem in constant time, which is independent of $n$:
$z^*=\min_{\mathbf{v} \in \mathbb{R}^n}\langle\mathbf{v}, A \mathbf{v}\rangle +
n\langle\mathbf{v}, \mathr... | computer science |
11,651 | Entity Embedding-based Anomaly Detection for Heterogeneous Categorical
Events | cs.LG | Anomaly detection plays an important role in modern data-driven security
applications, such as detecting suspicious access to a socket from a process.
In many cases, such events can be described as a collection of categorical
values that are considered as entities of different types, which we call
heterogeneous categor... | computer science |
11,652 | Clustering and Community Detection with Imbalanced Clusters | stat.ML | Spectral clustering methods which are frequently used in clustering and
community detection applications are sensitive to the specific graph
constructions particularly when imbalanced clusters are present. We show that
ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to
imbalanced cluster sizes sin... | computer science |
11,653 | Data Dependent Convergence for Distributed Stochastic Optimization | math.OC | In this dissertation we propose alternative analysis of distributed
stochastic gradient descent (SGD) algorithms that rely on spectral properties
of the data covariance. As a consequence we can relate questions pertaining to
speedups and convergence rates for distributed SGD to the data distribution
instead of the regu... | computer science |
11,654 | A Mathematical Framework for Feature Selection from Real-World Data with
Non-Linear Observations | stat.ML | In this paper, we study the challenge of feature selection based on a
relatively small collection of sample pairs $\{(x_i, y_i)\}_{1 \leq i \leq m}$.
The observations $y_i \in \mathbb{R}$ are thereby supposed to follow a noisy
single-index model, depending on a certain set of signal variables. A major
difficulty is tha... | computer science |
11,655 | Least Ambiguous Set-Valued Classifiers with Bounded Error Levels | stat.ME | In most classification tasks there are observations that are ambiguous and
therefore difficult to correctly label. Set-valued classification allows the
classifiers to output a set of plausible labels rather than a single label,
thereby giving a more appropriate and informative treatment to the labeling of
ambiguous ins... | computer science |
11,656 | A deep learning model for estimating story points | cs.SE | Although there has been substantial research in software analytics for effort
estimation in traditional software projects, little work has been done for
estimation in agile projects, especially estimating user stories or issues.
Story points are the most common unit of measure used for estimating the effort
involved in... | computer science |
11,657 | Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep
Learning Model | cs.LG | Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolution... | computer science |
11,658 | Decoding visual stimuli in human brain by using Anatomical Pattern
Analysis on fMRI images | stat.ML | A universal unanswered question in neuroscience and machine learning is
whether computers can decode the patterns of the human brain. Multi-Voxels
Pattern Analysis (MVPA) is a critical tool for addressing this question.
However, there are two challenges in the previous MVPA methods, which include
decreasing sparsity an... | computer science |
11,659 | Local Maxima in the Likelihood of Gaussian Mixture Models: Structural
Results and Algorithmic Consequences | stat.ML | We provide two fundamental results on the population (infinite-sample)
likelihood function of Gaussian mixture models with $M \geq 3$ components. Our
first main result shows that the population likelihood function has bad local
maxima even in the special case of equally-weighted mixtures of well-separated
and spherical... | computer science |
11,660 | Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation | cs.IT | Estimators of information theoretic measures such as entropy and mutual
information are a basic workhorse for many downstream applications in modern
data science. State of the art approaches have been either geometric (nearest
neighbor (NN) based) or kernel based (with a globally chosen bandwidth). In
this paper, we co... | computer science |
11,661 | Non-Backtracking Spectrum of Degree-Corrected Stochastic Block Models | math.PR | Motivated by community detection, we characterise the spectrum of the
non-backtracking matrix $B$ in the Degree-Corrected Stochastic Block Model.
Specifically, we consider a random graph on $n$ vertices partitioned into two
equal-sized clusters. The vertices have i.i.d. weights $\{ \phi_u \}_{u=1}^n$
with second mome... | computer science |
11,662 | Why is Differential Evolution Better than Grid Search for Tuning Defect
Predictors? | cs.SE | Context: One of the black arts of data mining is learning the magic
parameters which control the learners. In software analytics, at least for
defect prediction, several methods, like grid search and differential evolution
(DE), have been proposed to learn these parameters, which has been proved to be
able to improve t... | computer science |
11,663 | By-passing the Kohn-Sham equations with machine learning | cs.LG | Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of
density functional theory to solve electronic structure problems in a wide
variety of scientific fields, ranging from materials science to biochemistry to
astrophysics. Machine learning holds the promise of learning the kinetic energy
functional ... | computer science |
11,664 | Distributed Online Optimization in Dynamic Environments Using Mirror
Descent | math.OC | This work addresses decentralized online optimization in non-stationary
environments. A network of agents aim to track the minimizer of a global
time-varying convex function. The minimizer evolves according to a known
dynamics corrupted by an unknown, unstructured noise. At each time, the global
function can be cast as... | computer science |
11,665 | Robust Spectral Detection of Global Structures in the Data by Learning a
Regularization | stat.ML | Spectral methods are popular in detecting global structures in the given data
that can be represented as a matrix. However when the data matrix is sparse or
noisy, classic spectral methods usually fail to work, due to localization of
eigenvectors (or singular vectors) induced by the sparsity or noise. In this
work, we ... | computer science |
11,666 | Stealing Machine Learning Models via Prediction APIs | cs.CR | Machine learning (ML) models may be deemed confidential due to their
sensitive training data, commercial value, or use in security applications.
Increasingly often, confidential ML models are being deployed with publicly
accessible query interfaces. ML-as-a-service ("predictive analytics") systems
are an example: Some ... | computer science |
11,667 | On the Relationship between Online Gaussian Process Regression and
Kernel Least Mean Squares Algorithms | stat.ML | We study the relationship between online Gaussian process (GP) regression and
kernel least mean squares (KLMS) algorithms. While the latter have no capacity
of storing the entire posterior distribution during online learning, we
discover that their operation corresponds to the assumption of a fixed
posterior covariance... | computer science |
11,668 | Less than a Single Pass: Stochastically Controlled Stochastic Gradient
Method | math.OC | We develop and analyze a procedure for gradient-based optimization that we
refer to as stochastically controlled stochastic gradient (SCSG). As a member
of the SVRG family of algorithms, SCSG makes use of gradient estimates at two
scales, with the number of updates at the faster scale being governed by a
geometric rand... | computer science |
11,669 | Comment on "Why does deep and cheap learning work so well?"
[arXiv:1608.08225] | cs.LG | In a recent paper, "Why does deep and cheap learning work so well?", Lin and
Tegmark claim to show that the mapping between deep belief networks and the
variational renormalization group derived in [arXiv:1410.3831] is invalid, and
present a "counterexample" that claims to show that this mapping does not hold.
In this ... | computer science |
11,670 | Analysis of Kelner and Levin graph sparsification algorithm for a
streaming setting | stat.ML | We derive a new proof to show that the incremental resparsification algorithm
proposed by Kelner and Levin (2013) produces a spectral sparsifier in high
probability. We rigorously take into account the dependencies across subsequent
resparsifications using martingale inequalities, fixing a flaw in the original
analysis... | computer science |
11,671 | Information Theoretic Structure Learning with Confidence | cs.IT | Information theoretic measures (e.g. the Kullback Liebler divergence and
Shannon mutual information) have been used for exploring possibly nonlinear
multivariate dependencies in high dimension. If these dependencies are assumed
to follow a Markov factor graph model, this exploration process is called
structure discover... | computer science |
11,672 | Noisy Inductive Matrix Completion Under Sparse Factor Models | stat.ML | Inductive Matrix Completion (IMC) is an important class of matrix completion
problems that allows direct inclusion of available features to enhance
estimation capabilities. These models have found applications in personalized
recommendation systems, multilabel learning, dictionary learning, etc. This
paper examines a g... | computer science |
11,673 | Self-Sustaining Iterated Learning | math.OC | An important result from psycholinguistics (Griffiths & Kalish, 2005) states
that no language can be learned iteratively by rational agents in a
self-sustaining manner. We show how to modify the learning process slightly in
order to achieve self-sustainability. Our work is in two parts. First, we
characterize iterated ... | computer science |
11,674 | Gradient Descent Learns Linear Dynamical Systems | cs.LG | We prove that gradient descent efficiently converges to the global optimizer
of the maximum likelihood objective of an unknown linear time-invariant
dynamical system from a sequence of noisy observations generated by the system.
Even though the objective function is non-convex, we provide polynomial running
time and sa... | computer science |
11,675 | Inherent Trade-Offs in the Fair Determination of Risk Scores | cs.LG | Recent discussion in the public sphere about algorithmic classification has
involved tension between competing notions of what it means for a probabilistic
classification to be fair to different groups. We formalize three fairness
conditions that lie at the heart of these debates, and we prove that except in
highly con... | computer science |
11,676 | Conformalized Kernel Ridge Regression | stat.ML | General predictive models do not provide a measure of confidence in
predictions without Bayesian assumptions. A way to circumvent potential
restrictions is to use conformal methods for constructing non-parametric
confidence regions, that offer guarantees regarding validity. In this paper we
provide a detailed descripti... | computer science |
11,677 | AMOS: An Automated Model Order Selection Algorithm for Spectral Graph
Clustering | cs.SI | One of the longstanding problems in spectral graph clustering (SGC) is the
so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. In this paper, we
propose AMOS... | computer science |
11,678 | Network-regularized Sparse Logistic Regression Models for Clinical Risk
Prediction and Biomarker Discovery | cs.LG | Molecular profiling data (e.g., gene expression) has been used for clinical
risk prediction and biomarker discovery. However, it is necessary to integrate
other prior knowledge like biological pathways or gene interaction networks to
improve the predictive ability and biological interpretability of biomarkers.
Here, we... | computer science |
11,679 | Bibliographic Analysis on Research Publications using Authors,
Categorical Labels and the Citation Network | cs.DL | Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-... | computer science |
11,680 | Bibliographic Analysis with the Citation Network Topic Model | cs.DL | Bibliographic analysis considers author's research areas, the citation
network and paper content among other things. In this paper, we combine these
three in a topic model that produces a bibliographic model of authors, topics
and documents using a non-parametric extension of a combination of the Poisson
mixed-topic li... | computer science |
11,681 | Exact Sampling from Determinantal Point Processes | cs.LG | Determinantal point processes (DPPs) are an important concept in random
matrix theory and combinatorics. They have also recently attracted interest in
the study of numerical methods for machine learning, as they offer an elegant
"missing link" between independent Monte Carlo sampling and deterministic
evaluation on reg... | computer science |
11,682 | Multilayer Spectral Graph Clustering via Convex Layer Aggregation | cs.LG | Multilayer graphs are commonly used for representing different relations
between entities and handling heterogeneous data processing tasks. New
challenges arise in multilayer graph clustering for assigning clusters to a
common multilayer node set and for combining information from each layer. This
paper presents a theo... | computer science |
11,683 | Screening Rules for Convex Problems | math.OC | We propose a new framework for deriving screening rules for convex
optimization problems. Our approach covers a large class of constrained and
penalized optimization formulations, and works in two steps. First, given any
approximate point, the structure of the objective function and the duality gap
is used to gather in... | computer science |
11,684 | Informative Planning and Online Learning with Sparse Gaussian Processes | cs.RO | A big challenge in environmental monitoring is the spatiotemporal variation
of the phenomena to be observed. To enable persistent sensing and estimation in
such a setting, it is beneficial to have a time-varying underlying
environmental model. Here we present a planning and learning method that
enables an autonomous ma... | computer science |
11,685 | Multi-label Methods for Prediction with Sequential Data | cs.LG | The number of methods available for classification of multi-label data has
increased rapidly over recent years, yet relatively few links have been made
with the related task of classification of sequential data. If labels indices
are considered as time indices, the problems can often be seen as equivalent.
In this pape... | computer science |
11,686 | Statistical comparison of classifiers through Bayesian hierarchical
modelling | cs.LG | Usually one compares the accuracy of two competing classifiers via null
hypothesis significance tests (nhst). Yet the nhst tests suffer from important
shortcomings, which can be overcome by switching to Bayesian hypothesis
testing. We propose a Bayesian hierarchical model which jointly analyzes the
cross-validation res... | computer science |
11,687 | The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm | stat.ML | Casting machine learning as a type of search, we demonstrate that the
proportion of problems that are favorable for a fixed algorithm is strictly
bounded, such that no single algorithm can perform well over a large fraction
of them. Our results explain why we must either continue to develop new
learning methods year af... | computer science |
11,688 | EXTRACT: Strong Examples from Weakly-Labeled Sensor Data | stat.ML | Thanks to the rise of wearable and connected devices, sensor-generated time
series comprise a large and growing fraction of the world's data.
Unfortunately, extracting value from this data can be challenging, since
sensors report low-level signals (e.g., acceleration), not the high-level
events that are typically of in... | computer science |
11,689 | Deep Multi-Species Embedding | cs.LG | Understanding how species are distributed across landscapes over time is a
fundamental question in biodiversity research. Unfortunately, most species
distribution models only target a single species at a time, despite strong
ecological evidence that species are not independently distributed. We propose
Deep Multi-Speci... | computer science |
11,690 | CNN Architectures for Large-Scale Audio Classification | cs.SD | Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs)... | computer science |
11,691 | Social Computing for Mobile Big Data in Wireless Networks | cs.SI | Mobile big data contains vast statistical features in various dimensions,
including spatial, temporal, and the underlying social domain. Understanding
and exploiting the features of mobile data from a social network perspective
will be extremely beneficial to wireless networks, from planning, operation,
and maintenance... | computer science |
11,692 | On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian
Learning Approach | cs.SY | This paper begins with considering the identification of sparse linear
time-invariant networks described by multivariable ARX models. Such models
possess relatively simple structure thus used as a benchmark to promote further
research. With identifiability of the network guaranteed, this paper presents
an identificatio... | computer science |
11,693 | Optimal spectral transportation with application to music transcription | stat.ML | Many spectral unmixing methods rely on the non-negative decomposition of
spectral data onto a dictionary of spectral templates. In particular,
state-of-the-art music transcription systems decompose the spectrogram of the
input signal onto a dictionary of representative note spectra. The typical
measures of fit used to ... | computer science |
11,694 | HNP3: A Hierarchical Nonparametric Point Process for Modeling Content
Diffusion over Social Media | stat.ML | This paper introduces a novel framework for modeling temporal events with
complex longitudinal dependency that are generated by dependent sources. This
framework takes advantage of multidimensional point processes for modeling time
of events. The intensity function of the proposed process is a mixture of
intensities, a... | computer science |
11,695 | Sequential Low-Rank Change Detection | stat.ML | Detecting emergence of a low-rank signal from high-dimensional data is an
important problem arising from many applications such as camera surveillance
and swarm monitoring using sensors. We consider a procedure based on the
largest eigenvalue of the sample covariance matrix over a sliding window to
detect the change. T... | computer science |
11,696 | cleverhans v2.0.0: an adversarial machine learning library | cs.LG | \texttt{cleverhans} is a software library that provides standardized
reference implementations of \emph{adversarial example} construction techniques
and \emph{adversarial training}. The library may be used to develop more robust
machine learning models and to provide standardized benchmarks of models'
performance in th... | computer science |
11,697 | Revisiting Role Discovery in Networks: From Node to Edge Roles | stat.ML | Previous work in network analysis has focused on modeling the
mixed-memberships of node roles in the graph, but not the roles of edges. We
introduce the edge role discovery problem and present a generalizable framework
for learning and extracting edge roles from arbitrary graphs automatically.
Furthermore, while existi... | computer science |
11,698 | Stochastic Optimization with Variance Reduction for Infinite Datasets
with Finite-Sum Structure | stat.ML | Stochastic optimization algorithms with variance reduction have proven
successful for minimizing large finite sums of functions. Unfortunately, these
techniques are unable to deal with stochastic perturbations of input data,
induced for example by data augmentation. In such cases, the objective is no
longer a finite su... | computer science |
11,699 | A SMART Stochastic Algorithm for Nonconvex Optimization with
Applications to Robust Machine Learning | stat.ML | In this paper, we show how to transform any optimization problem that arises
from fitting a machine learning model into one that (1) detects and removes
contaminated data from the training set while (2) simultaneously fitting the
trimmed model on the uncontaminated data that remains. To solve the resulting
nonconvex op... | computer science |
11,700 | A Non-generative Framework and Convex Relaxations for Unsupervised
Learning | cs.LG | We give a novel formal theoretical framework for unsupervised learning with
two distinctive characteristics. First, it does not assume any generative model
and based on a worst-case performance metric. Second, it is comparative, namely
performance is measured with respect to a given hypothesis class. This allows
to avo... | computer science |
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