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12,201 | A deep generative model for single-cell RNA sequencing with application
to detecting differentially expressed genes | cs.LG | We propose a probabilistic model for interpreting gene expression levels that
are observed through single-cell RNA sequencing. In the model, each cell has a
low-dimensional latent representation. Additional latent variables account for
technical effects that may erroneously set some observations of gene expression
leve... | computer science |
12,202 | A simple data discretizer | cs.LG | Data discretization is an important step in the process of machine learning,
since it is easier for classifiers to deal with discrete attributes rather than
continuous attributes. Over the years, several methods of performing
discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have
been proposed,... | computer science |
12,203 | When Point Process Meets RNNs: Predicting Fine-Grained User Interests
with Mutual Behavioral Infectivity | cs.LG | Predicting fine-grained interests of users with temporal behavior is
important to personalization and information filtering applications. However,
existing interest prediction methods are incapable of capturing the subtle
degreed user interests towards particular items, and the internal time-varying
drifting attention ... | computer science |
12,204 | Accelerated Block Coordinate Proximal Gradients with Applications in
High Dimensional Statistics | math.OC | Nonconvex optimization problems arise in different research fields and arouse
lots of attention in signal processing, statistics and machine learning. In
this work, we explore the accelerated proximal gradient method and some of its
variants which have been shown to converge under nonconvex context recently. We
show th... | computer science |
12,205 | The Scaling Limit of High-Dimensional Online Independent Component
Analysis | cs.LG | We analyze the dynamics of an online algorithm for independent component
analysis in the high-dimensional scaling limit. As the ambient dimension tends
to infinity, and with proper time scaling, we show that the time-varying joint
empirical measure of the target feature vector and the estimates provided by
the algorith... | computer science |
12,206 | NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural
Networks | cs.LG | "How much energy is consumed for an inference made by a convolutional neural
network (CNN)?" With the increased popularity of CNNs deployed on the
wide-spectrum of platforms (from mobile devices to workstations), the answer to
this question has drawn significant attention. From lengthening battery life of
mobile device... | computer science |
12,207 | On the Hardness of Inventory Management with Censored Demand Data | cs.LG | We consider a repeated newsvendor problem where the inventory manager has no
prior information about the demand, and can access only censored/sales data. In
analogy to multi-armed bandit problems, the manager needs to simultaneously
"explore" and "exploit" with her inventory decisions, in order to minimize the
cumulati... | computer science |
12,208 | Spectral Algorithms for Computing Fair Support Vector Machines | cs.LG | Classifiers and rating scores are prone to implicitly codifying biases, which
may be present in the training data, against protected classes (i.e., age,
gender, or race). So it is important to understand how to design classifiers
and scores that prevent discrimination in predictions. This paper develops
computationally... | computer science |
12,209 | A Correspondence Between Random Neural Networks and Statistical Field
Theory | stat.ML | A number of recent papers have provided evidence that practical design
questions about neural networks may be tackled theoretically by studying the
behavior of random networks. However, until now the tools available for
analyzing random neural networks have been relatively ad-hoc. In this work, we
show that the distrib... | computer science |
12,210 | Learning Social Image Embedding with Deep Multimodal Attention Networks | cs.MM | Learning social media data embedding by deep models has attracted extensive
research interest as well as boomed a lot of applications, such as link
prediction, classification, and cross-modal search. However, for social images
which contain both link information and multimodal contents (e.g., text
description, and visu... | computer science |
12,211 | A complete characterization of optimal dictionaries for least squares
representation | math.OC | Dictionaries are collections of vectors used for representations of elements
in Euclidean spaces. While recent research on optimal dictionaries is focussed
on providing sparse (i.e., $\ell_0$-optimal,) representations, here we consider
the problem of finding optimal dictionaries such that representations of
samples of ... | computer science |
12,212 | Phase Transitions in the Pooled Data Problem | stat.ML | In this paper, we study the pooled data problem of identifying the labels
associated with a large collection of items, based on a sequence of pooled
tests revealing the counts of each label within the pool. In the noiseless
setting, we identify an exact asymptotic threshold on the required number of
tests with optimal ... | computer science |
12,213 | A Bayesian Nonparametric Method for Clustering Imputation, and
Forecasting in Multivariate Time Series | stat.ME | This article proposes a Bayesian nonparametric method for forecasting,
imputation, and clustering in sparsely observed, multivariate time series. The
method is appropriate for jointly modeling hundreds of time series with widely
varying, non-stationary dynamics. Given a collection of $N$ time series, the
Bayesian model... | computer science |
12,214 | Characterization of Gradient Dominance and Regularity Conditions for
Neural Networks | stat.ML | The past decade has witnessed a successful application of deep learning to
solving many challenging problems in machine learning and artificial
intelligence. However, the loss functions of deep neural networks (especially
nonlinear networks) are still far from being well understood from a theoretical
aspect. In this pa... | computer science |
12,215 | Asynchronous Decentralized Parallel Stochastic Gradient Descent | math.OC | Most commonly used distributed machine learning systems are either
synchronous or centralized asynchronous. Synchronous algorithms like
AllReduce-SGD perform poorly in a heterogeneous environment, while asynchronous
algorithms using a parameter server suffer from 1) communication bottleneck at
parameter servers when wo... | computer science |
12,216 | Visual Integration of Data and Model Space in Ensemble Learning | cs.HC | Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classificatio... | computer science |
12,217 | Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural
Networks | stat.ML | Docking is an important tool in computational drug discovery that aims to
predict the binding pose of a ligand to a target protein through a combination
of pose scoring and optimization. A scoring function that is differentiable
with respect to atom positions can be used for both scoring and gradient-based
optimization... | computer science |
12,218 | First-order Methods Almost Always Avoid Saddle Points | stat.ML | We establish that first-order methods avoid saddle points for almost all
initializations. Our results apply to a wide variety of first-order methods,
including gradient descent, block coordinate descent, mirror descent and
variants thereof. The connecting thread is that such algorithms can be studied
from a dynamical s... | computer science |
12,219 | Tracking the gradients using the Hessian: A new look at variance
reducing stochastic methods | math.OC | Our goal is to improve variance reducing stochastic methods through better
control variates. We first propose a modification of SVRG which uses the
Hessian to track gradients over time, rather than to recondition, increasing
the correlation of the control variates and leading to faster theoretical
convergence close to ... | computer science |
12,220 | On the Consistency of Graph-based Bayesian Learning and the Scalability
of Sampling Algorithms | stat.ML | A popular approach to semi-supervised learning proceeds by endowing the input
data with a graph structure in order to extract geometric information and
incorporate it into a Bayesian framework. We introduce new theory that gives
appropriate scalings of graph parameters that provably lead to a well-defined
limiting post... | computer science |
12,221 | Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for
$k$-means Clustering | math.OC | In this paper, we propose an implicit gradient descent algorithm for the
classic $k$-means problem. The implicit gradient step or backward Euler is
solved via stochastic fixed-point iteration, in which we randomly sample a
mini-batch gradient in every iteration. It is the average of the fixed-point
trajectory that is c... | computer science |
12,222 | A Novel Bayesian Cluster Enumeration Criterion for Unsupervised Learning | math.ST | We derive a new Bayesian Information Criterion (BIC) from first principles by
formulating the problem of estimating the number of clusters in an observed
data set as maximization of the posterior probability of the candidate models.
Given that some mild assumptions are satisfied, we provide a general BIC
expression for... | computer science |
12,223 | Smart "Predict, then Optimize" | math.OC | Many real-world analytics problems involve two significant challenges:
prediction and optimization. Due to the typically complex nature of each
challenge, the standard paradigm is to predict, then optimize. By and large,
machine learning tools are intended to minimize prediction error and do not
account for how the pre... | computer science |
12,224 | Sequential Matrix Completion | cs.IR | We propose a novel algorithm for sequential matrix completion in a
recommender system setting, where the $(i,j)$th entry of the matrix corresponds
to a user $i$'s rating of product $j$. The objective of the algorithm is to
provide a sequential policy for user-product pair recommendation which will
yield the highest pos... | computer science |
12,225 | Interactive Visual Data Exploration with Subjective Feedback: An
Information-Theoretic Approach | stat.ML | Visual exploration of high-dimensional real-valued datasets is a fundamental
task in exploratory data analysis (EDA). Existing methods use predefined
criteria to choose the representation of data. There is a lack of methods that
(i) elicit from the user what she has learned from the data and (ii) show
patterns that she... | computer science |
12,226 | Interpretable Machine Learning for Privacy-Preserving Pervasive Systems | stat.ML | The presence of pervasive systems in our everyday lives and the interaction
of users with connected devices such as smartphones or home appliances generate
increasing amounts of traces that reflect users' behavior. A plethora of
machine learning techniques enable service providers to process these traces to
extract lat... | computer science |
12,227 | Stability Analysis of Optimal Adaptive Control using Value Iteration
with Approximation Errors | math.OC | Adaptive optimal control using value iteration initiated from a stabilizing
control policy is theoretically analyzed in terms of stability of the system
during the learning stage without ignoring the effects of approximation errors.
This analysis includes the system operated using any single/constant resulting
control ... | computer science |
12,228 | Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets | cs.LG | Deep learning models (aka Deep Neural Networks) have revolutionized many
fields including computer vision, natural language processing, speech
recognition, and is being increasingly used in clinical healthcare
applications. However, few works exist which have benchmarked the performance
of the deep learning models with... | computer science |
12,229 | Auto-Differentiating Linear Algebra | cs.MS | Development systems for deep learning, such as Theano, Torch, TensorFlow, or
MXNet, are easy-to-use tools for creating complex neural network models. Since
gradient computations are automatically baked in, and execution is mapped to
high performance hardware, these models can be trained end-to-end on large
amounts of d... | computer science |
12,230 | A Bayesian Method for Joint Clustering of Vectorial Data and Network
Data | stat.ML | We present a new model-based integrative method for clustering objects given
both vectorial data, which describes the feature of each object, and network
data, which indicates the similarity of connected objects. The proposed general
model is able to cluster the two types of data simultaneously within one
integrative p... | computer science |
12,231 | Curvature-aided Incremental Aggregated Gradient Method | stat.ML | We propose a new algorithm for finite sum optimization which we call the
curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the
problem of training a classifier for a d-dimensional problem, where the number
of training data is $m$ and $m \gg d \gg 1$, the CIAG method seeks to
accelerate increme... | computer science |
12,232 | Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural
Networks | eess.SP | We investigate anomaly detection in an unsupervised framework and introduce
Long Short Term Memory (LSTM) neural network based algorithms. In particular,
given variable length data sequences, we first pass these sequences through our
LSTM based structure and obtain fixed length sequences. We then find a decision
functi... | computer science |
12,233 | DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple
Large-Scale Datasets | cs.LG | Principal component analysis (PCA) has well-documented merits for data
extraction and dimensionality reduction. PCA deals with a single dataset at a
time, and it is challenged when it comes to analyzing multiple datasets. Yet in
certain setups, one wishes to extract the most significant information of one
dataset relat... | computer science |
12,234 | A Markov Chain Theory Approach to Characterizing the Minimax Optimality
of Stochastic Gradient Descent (for Least Squares) | stat.ML | This work provides a simplified proof of the statistical minimax optimality
of (iterate averaged) stochastic gradient descent (SGD), for the special case
of least squares. This result is obtained by analyzing SGD as a stochastic
process and by sharply characterizing the stationary covariance matrix of this
process. The... | computer science |
12,235 | Malware Detection by Eating a Whole EXE | stat.ML | In this work we introduce malware detection from raw byte sequences as a
fruitful research area to the larger machine learning community. Building a
neural network for such a problem presents a number of interesting challenges
that have not occurred in tasks such as image processing or NLP. In particular,
we note that ... | computer science |
12,236 | Stochastic Non-convex Optimization with Strong High Probability
Second-order Convergence | math.OC | In this paper, we study stochastic non-convex optimization with non-convex
random functions. Recent studies on non-convex optimization revolve around
establishing second-order convergence, i.e., converging to a nearly
second-order optimal stationary points. However, existing results on stochastic
non-convex optimizatio... | computer science |
12,237 | Watch Your Step: Learning Graph Embeddings Through Attention | cs.LG | Graph embedding methods represent nodes in a continuous vector space,
preserving information from the graph (e.g. by sampling random walks). There
are many hyper-parameters to these methods (such as random walk length) which
have to be manually tuned for every graph. In this paper, we replace random
walk hyper-paramete... | computer science |
12,238 | Segment Parameter Labelling in MCMC Mean-Shift Change Detection | cs.LG | This work addresses the problem of segmentation in time series data with
respect to a statistical parameter of interest in Bayesian models. It is common
to assume that the parameters are distinct within each segment. As such, many
Bayesian change point detection models do not exploit the segment parameter
patterns, whi... | computer science |
12,239 | Optimal Shrinkage of Singular Values Under Random Data Contamination | cs.IT | A low rank matrix X has been contaminated by uniformly distributed noise,
missing values, outliers and corrupt entries. Reconstruction of X from the
singular values and singular vectors of the contaminated matrix Y is a key
problem in machine learning, computer vision and data science. In this paper we
show that common... | computer science |
12,240 | Gradient Sparsification for Communication-Efficient Distributed
Optimization | cs.LG | Modern large scale machine learning applications require stochastic
optimization algorithms to be implemented on distributed computational
architectures. A key bottleneck is the communication overhead for exchanging
information such as stochastic gradients among different workers. In this
paper, to reduce the communica... | computer science |
12,241 | Regularization via Mass Transportation | math.OC | The goal of regression and classification methods in supervised learning is
to minimize the empirical risk, that is, the expectation of some loss function
quantifying the prediction error under the empirical distribution. When facing
scarce training data, overfitting is typically mitigated by adding
regularization term... | computer science |
12,242 | Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion
Recognition | cs.LG | Long short-term memory (LSTM) is normally used in recurrent neural network
(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
at current time step depends on previous time step. This assumption constraints
the time dependency modeling capability. In this study, we propose a new
variation of... | computer science |
12,243 | Learning Structural Node Embeddings Via Diffusion Wavelets | cs.SI | Nodes residing in different parts of a graph can have similar structural
roles within their local network topology. The identification of such roles
provides key insight into the organization of networks and can be used for a
variety of machine learning tasks. However, learning structural representations
of nodes is a ... | computer science |
12,244 | Lower Bounds for Higher-Order Convex Optimization | math.OC | State-of-the-art methods in convex and non-convex optimization employ
higher-order derivative information, either implicitly or explicitly. We
explore the limitations of higher-order optimization and prove that even for
convex optimization, a polynomial dependence on the approximation guarantee and
higher-order smoothn... | computer science |
12,245 | Diff-DAC: Distributed Actor-Critic for Multitask Deep Reinforcement
Learning | cs.LG | We propose a multiagent distributed actor-critic algorithm for multitask
reinforcement learning (MRL), named Diff-DAC. The agents are connected, forming
a (possibly sparse) network. Each agent is assigned a task and has access to
data from this local task only. During the learning process, the agents are
able to commun... | computer science |
12,246 | Minimax Rates and Efficient Algorithms for Noisy Sorting | stat.ML | There has been a recent surge of interest in studying permutation-based
models for ranking from pairwise comparison data. Despite being structurally
richer and more robust than parametric ranking models, permutation-based models
are less well understood statistically and generally lack efficient learning
algorithms. In... | computer science |
12,247 | Speaker Diarization with LSTM | eess.AS | For many years, i-vector based audio embedding techniques were the dominant
approach for speaker verification and speaker diarization applications.
However, mirroring the rise of deep learning in various domains, neural network
based audio embeddings, also known as d-vectors, have consistently demonstrated
superior spe... | computer science |
12,248 | Attention-Based Models for Text-Dependent Speaker Verification | eess.AS | Attention-based models have recently shown great performance on a range of
tasks, such as speech recognition, machine translation, and image captioning
due to their ability to summarize relevant information that expands through the
entire length of an input sequence. In this paper, we analyze the usage of
attention mec... | computer science |
12,249 | Dimensionality reduction methods for molecular simulations | stat.ML | Molecular simulations produce very high-dimensional data-sets with millions
of data points. As analysis methods are often unable to cope with so many
dimensions, it is common to use dimensionality reduction and clustering methods
to reach a reduced representation of the data. Yet these methods often fail to
capture the... | computer science |
12,250 | Attacking the Madry Defense Model with $L_1$-based Adversarial Examples | stat.ML | The Madry Lab recently hosted a competition designed to test the robustness
of their adversarially trained MNIST model. Attacks were constrained to perturb
each pixel of the input image by a scaled maximal $L_\infty$ distortion
$\epsilon$ = 0.3. This discourages the use of attacks which are not optimized
on the $L_\inf... | computer science |
12,251 | Linearly convergent stochastic heavy ball method for minimizing
generalization error | math.OC | In this work we establish the first linear convergence result for the
stochastic heavy ball method. The method performs SGD steps with a fixed
stepsize, amended by a heavy ball momentum term. In the analysis, we focus on
minimizing the expected loss and not on finite-sum minimization, which is
typically a much harder p... | computer science |
12,252 | Communication-Avoiding Optimization Methods for Massive-Scale Graphical
Model Structure Learning | stat.ML | Undirected graphical models compactly represent the structure of large,
high-dimensional data sets, which are especially important in interpreting
complex scientific data. Some data sets may run to multiple terabytes, and
current methods are intractable in both memory size and running time. We
introduce HP-CONCORD, a h... | computer science |
12,253 | Understanding GANs: the LQG Setting | stat.ML | Generative Adversarial Networks (GANs) have become a popular method to learn
a probability model from data. Many GAN architectures with different
optimization metrics have been introduced recently. Instead of proposing yet
another architecture, this paper aims to provide an understanding of some of
the basic issues sur... | computer science |
12,254 | Stochastic gradient descent performs variational inference, converges to
limit cycles for deep networks | cs.LG | Stochastic gradient descent (SGD) is widely believed to perform implicit
regularization when used to train deep neural networks, but the precise manner
in which this occurs has thus far been elusive. We prove that SGD minimizes an
average potential over the posterior distribution of weights along with an
entropic regul... | computer science |
12,255 | Onsets and Frames: Dual-Objective Piano Transcription | cs.SD | We consider the problem of transcribing polyphonic piano music with an
emphasis on generalizing to unseen instruments. We use deep neural networks and
propose a novel approach that predicts onsets and frames using both CNNs and
LSTMs. This model predicts pitch onset events and then uses those predictions
to condition f... | computer science |
12,256 | How Algorithmic Confounding in Recommendation Systems Increases
Homogeneity and Decreases Utility | cs.CY | Recommendation systems occupy an expanding role in everyday decision making,
from choice of movies and household goods to consequential medical and legal
decisions. The data used to train and test these systems is algorithmically
confounded in that it is the result of a feedback loop between human choices
and an existi... | computer science |
12,257 | Time-lagged autoencoders: Deep learning of slow collective variables for
molecular kinetics | stat.ML | Inspired by the success of deep learning techniques in the physical and
chemical sciences, we apply a modification of an autoencoder type deep neural
network to the task of dimension reduction of molecular dynamics data. We can
show that our time-lagged autoencoder reliably finds low-dimensional embeddings
for high-dim... | computer science |
12,258 | Approximation Algorithms for $\ell_0$-Low Rank Approximation | cs.DS | We study the $\ell_0$-Low Rank Approximation Problem, where the goal is,
given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which
$\|A'-A\|_0$ is minimized. Here, for a matrix $B$, $\|B\|_0$ denotes the number
of its non-zero entries. This NP-hard variant of low rank approximation is
natural for pro... | computer science |
12,259 | The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition | cs.DS | We study rank-1 {L1-norm-based TUCKER2} (L1-TUCKER2) decomposition of 3-way
tensors, treated as a collection of $N$ $D \times M$ matrices that are to be
jointly decomposed. Our contributions are as follows. i) We prove that the
problem is equivalent to combinatorial optimization over $N$ antipodal-binary
variables. ii)... | computer science |
12,260 | Learning Neural Representations of Human Cognition across Many fMRI
Studies | stat.ML | Cognitive neuroscience is enjoying rapid increase in extensive public
brain-imaging datasets. It opens the door to large-scale statistical models.
Finding a unified perspective for all available data calls for scalable and
automated solutions to an old challenge: how to aggregate heterogeneous
information on brain func... | computer science |
12,261 | Statistical Speech Enhancement Based on Probabilistic Integration of
Variational Autoencoder and Non-Negative Matrix Factorization | cs.SD | This paper presents a statistical method of single-channel speech enhancement
that uses a variational autoencoder (VAE) as a prior distribution on clean
speech. A standard approach to speech enhancement is to train a deep neural
network (DNN) to take noisy speech as input and output clean speech. Although
this supervis... | computer science |
12,262 | Small Moving Window Calibration Models for Soft Sensing Processes with
Limited History | stat.ML | Five simple soft sensor methodologies with two update conditions were
compared on two experimentally-obtained datasets and one simulated dataset. The
soft sensors investigated were moving window partial least squares regression
(and a recursive variant), moving window random forest regression, the mean
moving window of... | computer science |
12,263 | Gene Ontology (GO) Prediction using Machine Learning Methods | cs.LG | We applied machine learning to predict whether a gene is involved in axon
regeneration. We extracted 31 features from different databases and trained
five machine learning models. Our optimal model, a Random Forest Classifier
with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than
the baseline scor... | computer science |
12,264 | Calibration for Stratified Classification Models | stat.ME | In classification problems, sampling bias between training data and testing
data is critical to the ranking performance of classification scores. Such bias
can be both unintentionally introduced by data collection and intentionally
introduced by the algorithm, such as under-sampling or weighting techniques
applied to i... | computer science |
12,265 | Training GANs with Optimism | cs.LG | We address the issue of limit cycling behavior in training Generative
Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for
training Wasserstein GANs. Recent theoretical results have shown that
optimistic mirror decent (OMD) can enjoy faster regret rates in the context of
zero-sum games. WGANs ... | computer science |
12,266 | Learning One-hidden-layer Neural Networks with Landscape Design | cs.LG | We consider the problem of learning a one-hidden-layer neural network: we
assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the
label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in
$\mathbb{R}^m$ with $m\le d$, $B\in \mathbb{R}^{m\times d}$ is a full-rank
weight matrix, and $\x... | computer science |
12,267 | Sleep Stage Classification Based on Multi-level Feature Learning and
Recurrent Neural Networks via Wearable Device | stat.ML | This paper proposes a practical approach for automatic sleep stage
classification based on a multi-level feature learning framework and Recurrent
Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
from a wearable device. The feature learning framework is designed to extract
low- and mid-level... | computer science |
12,268 | Approximation of Functions over Manifolds: A Moving Least-Squares
Approach | stat.ML | We present an algorithm for approximating a function defined over a
$d$-dimensional manifold utilizing only noisy function values at locations
sampled from the manifold with noise. To produce the approximation we do not
require any knowledge regarding the manifold other than its dimension $d$. The
approximation scheme ... | computer science |
12,269 | Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned
Transform | stat.ML | A major challenge in X-ray computed tomography (CT) is reducing radiation
dose while maintaining high quality of reconstructed images. To reduce the
radiation dose, one can reduce the number of projection views (sparse-view CT);
however, it becomes difficult to achieve high quality image reconstruction as
the number of... | computer science |
12,270 | Learning Linear Dynamical Systems via Spectral Filtering | cs.LG | We present an efficient and practical algorithm for the online prediction of
discrete-time linear dynamical systems with a symmetric transition matrix. We
circumvent the non-convex optimization problem using improper learning:
carefully overparameterize the class of LDSs by a polylogarithmic factor, in
exchange for con... | computer science |
12,271 | From which world is your graph? | cs.LG | Discovering statistical structure from links is a fundamental problem in the
analysis of social networks. Choosing a misspecified model, or equivalently, an
incorrect inference algorithm will result in an invalid analysis or even
falsely uncover patterns that are in fact artifacts of the model. This work
focuses on uni... | computer science |
12,272 | Partial correlation graphs and the neighborhood lattice | math.ST | We define and study partial correlation graphs (PCGs) with variables in a
general Hilbert space and their connections to generalized neighborhood
regression, without making any distributional assumptions. Using
operator-theoretic arguments, and especially the properties of projection
operators on Hilbert spaces, we sho... | computer science |
12,273 | Stochastic Submodular Maximization: The Case of Coverage Functions | cs.LG | Stochastic optimization of continuous objectives is at the heart of modern
machine learning. However, many important problems are of discrete nature and
often involve submodular objectives. We seek to unleash the power of stochastic
continuous optimization, namely stochastic gradient descent and its variants,
to such d... | computer science |
12,274 | Approximating Partition Functions in Constant Time | cs.LG | We study approximations of the partition function of dense graphical models.
Partition functions of graphical models play a fundamental role is statistical
physics, in statistics and in machine learning. Two of the main methods for
approximating the partition function are Markov Chain Monte Carlo and
Variational Method... | computer science |
12,275 | Memory-efficient Kernel PCA via Partial Matrix Sampling and Nonconvex
Optimization: a Model-free Analysis of Local Minima | math.OC | Kernel PCA is a widely used nonlinear dimension reduction technique in
machine learning, but storing the kernel matrix is notoriously challenging when
the sample size is large. Inspired by Yi et al. [2016], where the idea of
partial matrix sampling followed by nonconvex optimization is proposed for
matrix completion an... | computer science |
12,276 | AdaBatch: Efficient Gradient Aggregation Rules for Sequential and
Parallel Stochastic Gradient Methods | cs.LG | We study a new aggregation operator for gradients coming from a mini-batch
for stochastic gradient (SG) methods that allows a significant speed-up in the
case of sparse optimization problems. We call this method AdaBatch and it only
requires a few lines of code change compared to regular mini-batch SGD
algorithms. We p... | computer science |
12,277 | Fast amortized inference of neural activity from calcium imaging data
with variational autoencoders | stat.ML | Calcium imaging permits optical measurement of neural activity. Since
intracellular calcium concentration is an indirect measurement of neural
activity, computational tools are necessary to infer the true underlying
spiking activity from fluorescence measurements. Bayesian model inversion can
be used to solve this prob... | computer science |
12,278 | Estimating Cosmological Parameters from the Dark Matter Distribution | cs.LG | A grand challenge of the 21st century cosmology is to accurately estimate the
cosmological parameters of our Universe. A major approach to estimating the
cosmological parameters is to use the large-scale matter distribution of the
Universe. Galaxy surveys provide the means to map out cosmic large-scale
structure in thr... | computer science |
12,279 | An efficient quantum algorithm for generative machine learning | cs.LG | A central task in the field of quantum computing is to find applications
where quantum computer could provide exponential speedup over any classical
computer. Machine learning represents an important field with broad
applications where quantum computer may offer significant speedup. Several
quantum algorithms for discr... | computer science |
12,280 | Regret Bounds and Regimes of Optimality for User-User and Item-Item
Collaborative Filtering | stat.ML | We consider an online model for recommendation systems, with each user being
recommended an item at each time-step and providing 'like' or 'dislike'
feedback. A latent variable model specifies the user preferences: both users
and items are clustered into types. All users of a given type have identical
preferences for t... | computer science |
12,281 | Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep
Neural Networks | cs.LG | Deep neural networks are commonly developed and trained in 32-bit floating
point format. Significant gains in performance and energy efficiency could be
realized by training and inference in numerical formats optimized for deep
learning. Despite advances in limited precision inference in recent years,
training of neura... | computer science |
12,282 | Finding Heavily-Weighted Features in Data Streams | cs.LG | We introduce a new sub-linear space data structure---the Weight-Median
Sketch---that captures the most heavily weighted features in linear classifiers
trained over data streams. This enables memory-limited execution of several
statistical analyses over streams, including online feature selection,
streaming data explana... | computer science |
12,283 | Neural system identification for large populations separating "what" and
"where" | stat.ML | Neuroscientists classify neurons into different types that perform similar
computations at different locations in the visual field. Traditional methods
for neural system identification do not capitalize on this separation of 'what'
and 'where'. Learning deep convolutional feature spaces that are shared among
many neuro... | computer science |
12,284 | Stochastic Cubic Regularization for Fast Nonconvex Optimization | cs.LG | This paper proposes a stochastic variant of a classic algorithm---the
cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed
algorithm efficiently escapes saddle points and finds approximate local minima
for general smooth, nonconvex functions in only
$\mathcal{\tilde{O}}(\epsilon^{-3.5})$ stochastic ... | computer science |
12,285 | Using Phone Sensors and an Artificial Neural Network to Detect Gait
Changes During Drinking Episodes in the Natural Environment | cs.CY | Phone sensors could be useful in assessing changes in gait that occur with
alcohol consumption. This study determined (1) feasibility of collecting
gait-related data during drinking occasions in the natural environment, and (2)
how gait-related features measured by phone sensors relate to estimated blood
alcohol concen... | computer science |
12,286 | Learning Non-overlapping Convolutional Neural Networks with Multiple
Kernels | cs.LG | In this paper, we consider parameter recovery for non-overlapping
convolutional neural networks (CNNs) with multiple kernels. We show that when
the inputs follow Gaussian distribution and the sample size is sufficiently
large, the squared loss of such CNNs is $\mathit{~locally~strongly~convex}$ in
a basin of attraction... | computer science |
12,287 | SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and
Complements | stat.ML | We develop SHOPPER, a sequential probabilistic model of market baskets.
SHOPPER uses interpretable components to model the forces that drive how a
customer chooses products; in particular, we designed SHOPPER to capture how
items interact with other items. We develop an efficient posterior inference
algorithm to estima... | computer science |
12,288 | Traffic Analysis with Deep Learning | cs.CR | Deep Neural Networks (DNN) has obtained enormous attention with its
advantageous feature learning and its powerful prediction ability. In this
paper, we broadly study the applicability of deep learning to traffic analysis
and present its effectiveness on the feature extraction for state-of-the-art
machine learning algo... | computer science |
12,289 | WMRB: Learning to Rank in a Scalable Batch Training Approach | stat.ML | We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch
loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss
(WARP). WMRB uses a new rank estimator and an efficient batch training
algorithm. The approach allows more accurate item rank approximation and
explicit utilization of... | computer science |
12,290 | A Batch Learning Framework for Scalable Personalized Ranking | stat.ML | In designing personalized ranking algorithms, it is desirable to encourage a
high precision at the top of the ranked list. Existing methods either seek a
smooth convex surrogate for a non-smooth ranking metric or directly modify
updating procedures to encourage top accuracy. In this work we point out that
these methods... | computer science |
12,291 | Enhancing Network Embedding with Auxiliary Information: An Explicit
Matrix Factorization Perspective | cs.SI | Recent advances in the field of network embedding have shown the
low-dimensional network representation is playing a critical role in network
analysis. However, most of the existing principles of network embedding do not
incorporate auxiliary information such as content and labels of nodes flexibly.
In this paper, we t... | computer science |
12,292 | STWalk: Learning Trajectory Representations in Temporal Graphs | cs.SI | Analyzing the temporal behavior of nodes in time-varying graphs is useful for
many applications such as targeted advertising, community evolution and outlier
detection. In this paper, we present a novel approach, STWalk, for learning
trajectory representations of nodes in temporal graphs. The proposed framework
makes u... | computer science |
12,293 | A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with
Confounding Correction | cs.LG | While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists hav... | computer science |
12,294 | Linking Sequences of Events with Sparse or No Common Occurrence across
Data Sets | cs.LG | Data of practical interest - such as personal records, transaction logs, and
medical histories - are sequential collections of events relevant to a
particular source entity. Recent studies have attempted to link sequences that
represent a common entity across data sets to allow more comprehensive
statistical analyses a... | computer science |
12,295 | On the ERM Principle with Networked Data | cs.LG | Networked data, in which every training example involves two objects and may
share some common objects with others, is used in many machine learning tasks
such as learning to rank and link prediction. A challenge of learning from
networked examples is that target values are not known for some pairs of
objects. In this ... | computer science |
12,296 | A machine learning approach for efficient uncertainty quantification
using multiscale methods | cs.LG | Several multiscale methods account for sub-grid scale features using coarse
scale basis functions. For example, in the Multiscale Finite Volume method the
coarse scale basis functions are obtained by solving a set of local problems
over dual-grid cells. We introduce a data-driven approach for the estimation of
these co... | computer science |
12,297 | A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex
Sparsity-Regularized Rank Minimization | cs.DC | In this paper, we propose a convergent parallel best-response algorithm with
the exact line search for the nondifferentiable nonconvex sparsity-regularized
rank minimization problem. On the one hand, it exhibits a faster convergence
than subgradient algorithms and block coordinate descent algorithms. On the
other hand,... | computer science |
12,298 | Tensor Decompositions for Modeling Inverse Dynamics | cs.LG | Modeling inverse dynamics is crucial for accurate feedforward robot control.
The model computes the necessary joint torques, to perform a desired movement.
The highly non-linear inverse function of the dynamical system can be
approximated using regression techniques. We propose as regression method a
tensor decompositi... | computer science |
12,299 | Invariances and Data Augmentation for Supervised Music Transcription | stat.ML | This paper explores a variety of models for frame-based music transcription,
with an emphasis on the methods needed to reach state-of-the-art on human
recordings. The translation-invariant network discussed in this paper, which
combines a traditional filterbank with a convolutional neural network, was the
top-performin... | computer science |
12,300 | Near-optimal sample complexity for convex tensor completion | cs.LG | We analyze low rank tensor completion (TC) using noisy measurements of a
subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots
\times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved
is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm
minimizatio... | computer science |
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