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11,501 | Online Optimization with Costly and Noisy Measurements using Random
Fourier Expansions | cs.LG | This paper analyzes DONE, an online optimization algorithm that iteratively
minimizes an unknown function based on costly and noisy measurements. The
algorithm maintains a surrogate of the unknown function in the form of a random
Fourier expansion (RFE). The surrogate is updated whenever a new measurement is
available,... | computer science |
11,502 | Extending Detection with Forensic Information | cs.CR | For over a quarter century, security-relevant detection has been driven by
models learned from input features collected from real or simulated
environments. An artifact (e.g., network event, potential malware sample,
suspicious email) is deemed malicious or non-malicious based on its similarity
to the learned model at ... | computer science |
11,503 | Hierarchical Quickest Change Detection via Surrogates | cs.LG | Change detection (CD) in time series data is a critical problem as it reveal
changes in the underlying generative processes driving the time series. Despite
having received significant attention, one important unexplored aspect is how
to efficiently utilize additional correlated information to improve the
detection and... | computer science |
11,504 | Improved Sparse Low-Rank Matrix Estimation | math.OC | We address the problem of estimating a sparse low-rank matrix from its noisy
observation. We propose an objective function consisting of a data-fidelity
term and two parameterized non-convex penalty functions. Further, we show how
to set the parameters of the non-convex penalty functions, in order to ensure
that the ob... | computer science |
11,505 | Recovery of non-linear cause-effect relationships from linearly mixed
neuroimaging data | stat.ME | Causal inference concerns the identification of cause-effect relationships
between variables. However, often only linear combinations of variables
constitute meaningful causal variables. For example, recovering the signal of a
cortical source from electroencephalography requires a well-tuned combination
of signals reco... | computer science |
11,506 | Dictionary Learning for Massive Matrix Factorization | stat.ML | Sparse matrix factorization is a popular tool to obtain interpretable data
decompositions, which are also effective to perform data completion or
denoising. Its applicability to large datasets has been addressed with online
and randomized methods, that reduce the complexity in one of the matrix
dimension, but not in bo... | computer science |
11,507 | Distributed Learning with Infinitely Many Hypotheses | math.OC | We consider a distributed learning setup where a network of agents
sequentially access realizations of a set of random variables with unknown
distributions. The network objective is to find a parametrized distribution
that best describes their joint observations in the sense of the
Kullback-Leibler divergence. Apart fr... | computer science |
11,508 | Rate-Distortion Bounds on Bayes Risk in Supervised Learning | cs.IT | We present an information-theoretic framework for bounding the number of
labeled samples needed to train a classifier in a parametric Bayesian setting.
We derive bounds on the average $L_p$ distance between the learned classifier
and the true maximum a posteriori classifier, which are well-established
surrogates for th... | computer science |
11,509 | Structured Nonconvex and Nonsmooth Optimization: Algorithms and
Iteration Complexity Analysis | math.OC | Nonconvex and nonsmooth optimization problems are frequently encountered in
much of statistics, business, science and engineering, but they are not yet
widely recognized as a technology in the sense of scalability. A reason for
this relatively low degree of popularity is the lack of a well developed system
of theory an... | computer science |
11,510 | Clustering Time Series and the Surprising Robustness of HMMs | cs.IT | Suppose that we are given a time series where consecutive samples are
believed to come from a probabilistic source, that the source changes from time
to time and that the total number of sources is fixed. Our objective is to
estimate the distributions of the sources. A standard approach to this problem
is to model the ... | computer science |
11,511 | Nonconvex Sparse Learning via Stochastic Optimization with Progressive
Variance Reduction | cs.LG | We propose a stochastic variance reduced optimization algorithm for solving
sparse learning problems with cardinality constraints. Sufficient conditions
are provided, under which the proposed algorithm enjoys strong linear
convergence guarantees and optimal estimation accuracy in high dimensions. We
further extend the ... | computer science |
11,512 | Active Uncertainty Calibration in Bayesian ODE Solvers | cs.NA | There is resurging interest, in statistics and machine learning, in solvers
for ordinary differential equations (ODEs) that return probability measures
instead of point estimates. Recently, Conrad et al. introduced a sampling-based
class of methods that are 'well-calibrated' in a specific sense. But the
computational c... | computer science |
11,513 | Barzilai-Borwein Step Size for Stochastic Gradient Descent | math.OC | One of the major issues in stochastic gradient descent (SGD) methods is how
to choose an appropriate step size while running the algorithm. Since the
traditional line search technique does not apply for stochastic optimization
algorithms, the common practice in SGD is either to use a diminishing step
size, or to tune a... | computer science |
11,514 | Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online
Learning with True and Noisy Gradient | cs.LG | This work focuses on dynamic regret of online convex optimization that
compares the performance of online learning to a clairvoyant who knows the
sequence of loss functions in advance and hence selects the minimizer of the
loss function at each step. By assuming that the clairvoyant moves slowly
(i.e., the minimizers c... | computer science |
11,515 | Solve-Select-Scale: A Three Step Process For Sparse Signal Estimation | cs.IT | In the theory of compressed sensing (CS), the sparsity $\|x\|_0$ of the
unknown signal $\mathbf{x} \in \mathcal{R}^n$ is of prime importance and the
focus of reconstruction algorithms has mainly been either $\|x\|_0$ or its
convex relaxation (via $\|x\|_1$). However, it is typically unknown in practice
and has remained... | computer science |
11,516 | Geometry Aware Mappings for High Dimensional Sparse Factors | cs.LG | While matrix factorisation models are ubiquitous in large scale
recommendation and search, real time application of such models requires inner
product computations over an intractably large set of item factors. In this
manuscript we present a novel framework that uses the inverted index
representation to exploit struct... | computer science |
11,517 | Minimax Lower Bounds for Kronecker-Structured Dictionary Learning | cs.IT | Dictionary learning is the problem of estimating the collection of atomic
elements that provide a sparse representation of measured/collected signals or
data. This paper finds fundamental limits on the sample complexity of
estimating dictionaries for tensor data by proving a lower bound on the minimax
risk. This lower ... | computer science |
11,518 | Optimization Beyond Prediction: Prescriptive Price Optimization | math.OC | This paper addresses a novel data science problem, prescriptive price
optimization, which derives the optimal price strategy to maximize future
profit/revenue on the basis of massive predictive formulas produced by machine
learning. The prescriptive price optimization first builds sales forecast
formulas of multiple pr... | computer science |
11,519 | Linearized GMM Kernels and Normalized Random Fourier Features | cs.LG | The method of "random Fourier features (RFF)" has become a popular tool for
approximating the "radial basis function (RBF)" kernel. The variance of RFF is
actually large. Interestingly, the variance can be substantially reduced by a
simple normalization step as we theoretically demonstrate. We name the improved
scheme ... | computer science |
11,520 | Supervised Learning with Quantum-Inspired Tensor Networks | stat.ML | Tensor networks are efficient representations of high-dimensional tensors
which have been very successful for physics and mathematics applications. We
demonstrate how algorithms for optimizing such networks can be adapted to
supervised learning tasks by using matrix product states (tensor trains) to
parameterize models... | computer science |
11,521 | A Multi-Batch L-BFGS Method for Machine Learning | math.OC | The question of how to parallelize the stochastic gradient descent (SGD)
method has received much attention in the literature. In this paper, we focus
instead on batch methods that use a sizeable fraction of the training set at
each iteration to facilitate parallelism, and that employ second-order
information. In order... | computer science |
11,522 | Fast $ε$-free Inference of Simulation Models with Bayesian
Conditional Density Estimation | stat.ML | Many statistical models can be simulated forwards but have intractable
likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer
properties of these models from data. Traditionally these methods approximate
the posterior over parameters by conditioning on data being inside an
$\epsilon$-ball around ... | computer science |
11,523 | Deep Variational Bayes Filters: Unsupervised Learning of State Space
Models from Raw Data | stat.ML | We introduce Deep Variational Bayes Filters (DVBF), a new method for
unsupervised learning and identification of latent Markovian state space
models. Leveraging recent advances in Stochastic Gradient Variational Bayes,
DVBF can overcome intractable inference distributions via variational
inference. Thus, it can handle ... | computer science |
11,524 | Unreasonable Effectiveness of Learning Neural Networks: From Accessible
States and Robust Ensembles to Basic Algorithmic Schemes | stat.ML | In artificial neural networks, learning from data is a computationally
demanding task in which a large number of connection weights are iteratively
tuned through stochastic-gradient-based heuristic processes over a
cost-function. It is not well understood how learning occurs in these systems,
in particular how they avo... | computer science |
11,525 | Make Workers Work Harder: Decoupled Asynchronous Proximal Stochastic
Gradient Descent | math.OC | Asynchronous parallel optimization algorithms for solving large-scale machine
learning problems have drawn significant attention from academia to industry
recently. This paper proposes a novel algorithm, decoupled asynchronous
proximal stochastic gradient descent (DAP-SGD), to minimize an objective
function that is the... | computer science |
11,526 | Smart broadcasting: Do you want to be seen? | cs.SI | Many users in online social networks are constantly trying to gain attention
from their followers by broadcasting posts to them. These broadcasters are
likely to gain greater attention if their posts can remain visible for a longer
period of time among their followers' most recent feeds. Then when to post? In
this pape... | computer science |
11,527 | Fast Stochastic Methods for Nonsmooth Nonconvex Optimization | math.OC | We analyze stochastic algorithms for optimizing nonconvex, nonsmooth
finite-sum problems, where the nonconvex part is smooth and the nonsmooth part
is convex. Surprisingly, unlike the smooth case, our knowledge of this
fundamental problem is very limited. For example, it is not known whether the
proximal stochastic gra... | computer science |
11,528 | An Information Criterion for Inferring Coupling in Distributed Dynamical
Systems | cs.LG | The behaviour of many real-world phenomena can be modelled by nonlinear
dynamical systems whereby a latent system state is observed through a filter.
We are interested in interacting subsystems of this form, which we model by a
set of coupled maps as a synchronous update graph dynamical systems.
Specifically, we study ... | computer science |
11,529 | A Sub-Quadratic Exact Medoid Algorithm | stat.ML | We present a new algorithm, trimed, for obtaining the medoid of a set, that
is the element of the set which minimises the mean distance to all other
elements. The algorithm is shown to have, under certain assumptions, expected
run time O(N^(3/2)) in R^d where N is the set size, making it the first
sub-quadratic exact m... | computer science |
11,530 | Collaborative Filtering with Side Information: a Gaussian Process
Perspective | stat.ML | We tackle the problem of collaborative filtering (CF) with side information,
through the lens of Gaussian Process (GP) regression. Driven by the idea of
using the kernel to explicitly model user-item similarities, we formulate the
GP in a way that allows the incorporation of low-rank matrix factorisation,
arriving at o... | computer science |
11,531 | Bayesian Model Selection of Stochastic Block Models | stat.ML | A central problem in analyzing networks is partitioning them into modules or
communities. One of the best tools for this is the stochastic block model,
which clusters vertices into blocks with statistically homogeneous pattern of
links. Despite its flexibility and popularity, there has been a lack of
principled statist... | computer science |
11,532 | Deep Learning without Poor Local Minima | stat.ML | In this paper, we prove a conjecture published in 1989 and also partially
address an open problem announced at the Conference on Learning Theory (COLT)
2015. With no unrealistic assumption, we first prove the following statements
for the squared loss function of deep linear neural networks with any depth and
any widths... | computer science |
11,533 | Global Optimality of Local Search for Low Rank Matrix Recovery | stat.ML | We show that there are no spurious local minima in the non-convex factorized
parametrization of low-rank matrix recovery from incoherent linear
measurements. With noisy measurements we show all local minima are very close
to a global optimum. Together with a curvature bound at saddle points, this
yields a polynomial ti... | computer science |
11,534 | Matrix Completion has No Spurious Local Minimum | cs.LG | Matrix completion is a basic machine learning problem that has wide
applications, especially in collaborative filtering and recommender systems.
Simple non-convex optimization algorithms are popular and effective in
practice. Despite recent progress in proving various non-convex algorithms
converge from a good initial ... | computer science |
11,535 | Riemannian stochastic variance reduced gradient on Grassmann manifold | cs.LG | Stochastic variance reduction algorithms have recently become popular for
minimizing the average of a large, but finite, number of loss functions. In
this paper, we propose a novel Riemannian extension of the Euclidean stochastic
variance reduced gradient algorithm (R-SVRG) to a compact manifold search
space. To this e... | computer science |
11,536 | Refined Lower Bounds for Adversarial Bandits | math.ST | We provide new lower bounds on the regret that must be suffered by
adversarial bandit algorithms. The new results show that recent upper bounds
that either (a) hold with high-probability or (b) depend on the total lossof
the best arm or (c) depend on the quadratic variation of the losses, are close
to tight. Besides th... | computer science |
11,537 | Computing Web-scale Topic Models using an Asynchronous Parameter Server | cs.DC | Topic models such as Latent Dirichlet Allocation (LDA) have been widely used
in information retrieval for tasks ranging from smoothing and feedback methods
to tools for exploratory search and discovery. However, classical methods for
inferring topic models do not scale up to the massive size of today's publicly
availab... | computer science |
11,538 | Inductive supervised quantum learning | cs.LG | In supervised learning, an inductive learning algorithm extracts general
rules from observed training instances, then the rules are applied to test
instances. We show that this splitting of training and application arises
naturally, in the classical setting, from a simple independence requirement
with a physical interp... | computer science |
11,539 | Recursive Sampling for the Nyström Method | cs.LG | We give the first algorithm for kernel Nystr\"om approximation that runs in
*linear time in the number of training points* and is provably accurate for all
kernel matrices, without dependence on regularity or incoherence conditions.
The algorithm projects the kernel onto a set of $s$ landmark points sampled by
their *r... | computer science |
11,540 | NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and
Stochastic Optimization | math.OC | We study a stochastic and distributed algorithm for nonconvex problems whose
objective consists of a sum of $N$ nonconvex $L_i/N$-smooth functions, plus a
nonsmooth regularizer. The proposed NonconvEx primal-dual SpliTTing (NESTT)
algorithm splits the problem into $N$ subproblems, and utilizes an augmented
Lagrangian b... | computer science |
11,541 | On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization:
Don't Worry About Its Nonsmooth Loss Function | cs.LG | Many machine learning techniques sacrifice convenient computational
structures to gain estimation robustness and modeling flexibility. However, by
exploring the modeling structures, we find these "sacrifices" do not always
require more computational efforts. To shed light on such a "free-lunch"
phenomenon, we study the... | computer science |
11,542 | Tight Complexity Bounds for Optimizing Composite Objectives | math.OC | We provide tight upper and lower bounds on the complexity of minimizing the
average of $m$ convex functions using gradient and prox oracles of the
component functions. We show a significant gap between the complexity of
deterministic vs randomized optimization. For smooth functions, we show that
accelerated gradient de... | computer science |
11,543 | FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods | math.OC | We consider first order gradient methods for effectively optimizing a
composite objective in the form of a sum of smooth and, potentially, non-smooth
functions. We present accelerated and adaptive gradient methods, called FLAG
and FLARE, which can offer the best of both worlds. They can achieve the
optimal convergence ... | computer science |
11,544 | Low-rank tensor completion: a Riemannian manifold preconditioning
approach | cs.LG | We propose a novel Riemannian manifold preconditioning approach for the
tensor completion problem with rank constraint. A novel Riemannian metric or
inner product is proposed that exploits the least-squares structure of the cost
function and takes into account the structured symmetry that exists in Tucker
decomposition... | computer science |
11,545 | Provable Efficient Online Matrix Completion via Non-convex Stochastic
Gradient Descent | cs.LG | Matrix completion, where we wish to recover a low rank matrix by observing a
few entries from it, is a widely studied problem in both theory and practice
with wide applications. Most of the provable algorithms so far on this problem
have been restricted to the offline setting where they provide an estimate of
the unkno... | computer science |
11,546 | Suppressing Background Radiation Using Poisson Principal Component
Analysis | cs.LG | Performance of nuclear threat detection systems based on gamma-ray
spectrometry often strongly depends on the ability to identify the part of
measured signal that can be attributed to background radiation. We have
successfully applied a method based on Principal Component Analysis (PCA) to
obtain a compact null-space m... | computer science |
11,547 | Optimal Rates for Multi-pass Stochastic Gradient Methods | cs.LG | We analyze the learning properties of the stochastic gradient method when
multiple passes over the data and mini-batches are allowed. We study how
regularization properties are controlled by the step-size, the number of passes
and the mini-batch size. In particular, we consider the square loss and show
that for a unive... | computer science |
11,548 | Recycling Randomness with Structure for Sublinear time Kernel Expansions | cs.LG | We propose a scheme for recycling Gaussian random vectors into structured
matrices to approximate various kernel functions in sublinear time via random
embeddings. Our framework includes the Fastfood construction as a special case,
but also extends to Circulant, Toeplitz and Hankel matrices, and the broader
family of s... | computer science |
11,549 | Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs | cs.LG | In this paper, we propose several improvements on the block-coordinate
Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to
optimize the structured support vector machine (SSVM) objective in the context
of structured prediction, though it has wider applications. The key intuition
behind our i... | computer science |
11,550 | A Neural Autoregressive Approach to Collaborative Filtering | cs.IR | This paper proposes CF-NADE, a neural autoregressive architecture for
collaborative filtering (CF) tasks, which is inspired by the Restricted
Boltzmann Machine (RBM) based CF model and the Neural Autoregressive
Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF
tasks. Then we propose to imp... | computer science |
11,551 | Horizontally Scalable Submodular Maximization | stat.ML | A variety of large-scale machine learning problems can be cast as instances
of constrained submodular maximization. Existing approaches for distributed
submodular maximization have a critical drawback: The capacity - number of
instances that can fit in memory - must grow with the data set size. In
practice, while one c... | computer science |
11,552 | Asynchrony begets Momentum, with an Application to Deep Learning | stat.ML | Asynchronous methods are widely used in deep learning, but have limited
theoretical justification when applied to non-convex problems. We show that
running stochastic gradient descent (SGD) in an asynchronous manner can be
viewed as adding a momentum-like term to the SGD iteration. Our result does not
assume convexity ... | computer science |
11,553 | Contextual Bandits with Latent Confounders: An NMF Approach | cs.LG | Motivated by online recommendation and advertising systems, we consider a
causal model for stochastic contextual bandits with a latent low-dimensional
confounder. In our model, there are $L$ observed contexts and $K$ arms of the
bandit. The observed context influences the reward obtained through a latent
confounder var... | computer science |
11,554 | A Minimax Optimal Algorithm for Crowdsourcing | stat.ML | We consider the problem of accurately estimating the reliability of workers
based on noisy labels they provide, which is a fundamental question in
crowdsourcing. We propose a novel lower bound on the minimax estimation error
which applies to any estimation procedure. We further propose Triangular
Estimation (TE), an al... | computer science |
11,555 | Stream Clipper: Scalable Submodular Maximization on Stream | stat.ML | We propose a streaming submodular maximization algorithm "stream clipper"
that performs as well as the offline greedy algorithm on document/video
summarization in practice. It adds elements from a stream either to a solution
set $S$ or to an extra buffer $B$ based on two adaptive thresholds, and
improves $S$ by a final... | computer science |
11,556 | Scaling Submodular Maximization via Pruned Submodularity Graphs | cs.LG | We propose a new random pruning method (called "submodular sparsification
(SS)") to reduce the cost of submodular maximization. The pruning is applied
via a "submodularity graph" over the $n$ ground elements, where each directed
edge is associated with a pairwise dependency defined by the submodular
function. In each s... | computer science |
11,557 | Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex
Composite optimization | stat.ML | Here we study non-convex composite optimization: first, a finite-sum of
smooth but non-convex functions, and second, a general function that admits a
simple proximal mapping. Most research on stochastic methods for composite
optimization assumes convexity or strong convexity of each function. In this
paper, we extend t... | computer science |
11,558 | f-GAN: Training Generative Neural Samplers using Variational Divergence
Minimization | stat.ML | Generative neural samplers are probabilistic models that implement sampling
using feedforward neural networks: they take a random input vector and produce
a sample from a probability distribution defined by the network weights. These
models are expressive and allow efficient computation of samples and
derivatives, but ... | computer science |
11,559 | Multi-Organ Cancer Classification and Survival Analysis | cs.LG | Accurate and robust cell nuclei classification is the cornerstone for a wider
range of tasks in digital and Computational Pathology. However, most machine
learning systems require extensive labeling from expert pathologists for each
individual problem at hand, with no or limited abilities for knowledge transfer
between... | computer science |
11,560 | Difference of Convex Functions Programming Applied to Control with
Expert Data | math.OC | This paper reports applications of Difference of Convex functions (DC)
programming to Learning from Demonstrations (LfD) and Reinforcement Learning
(RL) with expert data. This is made possible because the norm of the Optimal
Bellman Residual (OBR), which is at the heart of many RL and LfD algorithms, is
DC. Improvement... | computer science |
11,561 | Low-rank Optimization with Convex Constraints | math.OC | The problem of low-rank approximation with convex constraints, which appears
in data analysis, system identification, model order reduction, low-order
controller design and low-complexity modelling is considered. Given a matrix,
the objective is to find a low-rank approximation that meets rank and convex
constraints, w... | computer science |
11,562 | Efficient differentially private learning improves drug sensitivity
prediction | stat.ML | Users of a personalised recommendation system face a dilemma: recommendations
can be improved by learning from data, but only if the other users are willing
to share their private information. Good personalised predictions are vitally
important in precision medicine, but genomic information on which the
predictions are... | computer science |
11,563 | A Minimax Approach to Supervised Learning | stat.ML | Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of
probability distributions $\Gamma$ on $(X,Y)$, what is the optimal decision
rule minimizing the worst-case expected loss over $\Gamma$? In this paper, we
address this question by introducing a generalization of the principle of
maximum entropy. ... | computer science |
11,564 | Measuring the reliability of MCMC inference with bidirectional Monte
Carlo | cs.LG | Markov chain Monte Carlo (MCMC) is one of the main workhorses of
probabilistic inference, but it is notoriously hard to measure the quality of
approximate posterior samples. This challenge is particularly salient in black
box inference methods, which can hide details and obscure inference failures.
In this work, we ext... | computer science |
11,565 | How is a data-driven approach better than random choice in label space
division for multi-label classification? | cs.LG | We propose using five data-driven community detection approaches from social
networks to partition the label space for the task of multi-label
classification as an alternative to random partitioning into equal subsets as
performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector,
infomap, walktrap an... | computer science |
11,566 | Gossip Dual Averaging for Decentralized Optimization of Pairwise
Functions | stat.ML | In decentralized networks (of sensors, connected objects, etc.), there is an
important need for efficient algorithms to optimize a global cost function, for
instance to learn a global model from the local data collected by each
computing unit. In this paper, we address the problem of decentralized
minimization of pairw... | computer science |
11,567 | Efficient Smoothed Concomitant Lasso Estimation for High Dimensional
Regression | stat.ML | In high dimensional settings, sparse structures are crucial for efficiency,
both in term of memory, computation and performance. It is customary to
consider $\ell_1$ penalty to enforce sparsity in such scenarios. Sparsity
enforcing methods, the Lasso being a canonical example, are popular candidates
to address high dim... | computer science |
11,568 | An Application of Network Lasso Optimization For Ride Sharing Prediction | cs.CY | Ride sharing has important implications in terms of environmental, social and
individual goals by reducing carbon footprints, fostering social interactions
and economizing commuter costs. The ride sharing systems that are commonly
available lack adaptive and scalable techniques that can simultaneously learn
from the la... | computer science |
11,569 | Drug response prediction by inferring pathway-response associations with
Kernelized Bayesian Matrix Factorization | stat.ML | A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially va... | computer science |
11,570 | Efficient Learning with a Family of Nonconvex Regularizers by
Redistributing Nonconvexity | math.OC | The use of convex regularizers allows for easy optimization, though they
often produce biased estimation and inferior prediction performance. Recently,
nonconvex regularizers have attracted a lot of attention and outperformed
convex ones. However, the resultant optimization problem is much harder. In
this paper, for a ... | computer science |
11,571 | Calibration of Phone Likelihoods in Automatic Speech Recognition | stat.ML | In this paper we study the probabilistic properties of the posteriors in a
speech recognition system that uses a deep neural network (DNN) for acoustic
modeling. We do this by reducing Kaldi's DNN shared pdf-id posteriors to phone
likelihoods, and using test set forced alignments to evaluate these using a
calibration s... | computer science |
11,572 | Model-Free Episodic Control | stat.ML | State of the art deep reinforcement learning algorithms take many millions of
interactions to attain human-level performance. Humans, on the other hand, can
very quickly exploit highly rewarding nuances of an environment upon first
discovery. In the brain, such rapid learning is thought to depend on the
hippocampus and... | computer science |
11,573 | Bolt-on Differential Privacy for Scalable Stochastic Gradient
Descent-based Analytics | cs.LG | While significant progress has been made separately on analytics systems for
scalable stochastic gradient descent (SGD) and private SGD, none of the major
scalable analytics frameworks have incorporated differentially private SGD.
There are two inter-related issues for this disconnect between research and
practice: (1)... | computer science |
11,574 | ASAGA: Asynchronous Parallel SAGA | math.OC | We describe ASAGA, an asynchronous parallel version of the incremental
gradient algorithm SAGA that enjoys fast linear convergence rates. Through a
novel perspective, we revisit and clarify a subtle but important technical
issue present in a large fraction of the recent convergence rate proofs for
asynchronous parallel... | computer science |
11,575 | Optimization Methods for Large-Scale Machine Learning | stat.ML | This paper provides a review and commentary on the past, present, and future
of numerical optimization algorithms in the context of machine learning
applications. Through case studies on text classification and the training of
deep neural networks, we discuss how optimization problems arise in machine
learning and what... | computer science |
11,576 | A Class of Parallel Doubly Stochastic Algorithms for Large-Scale
Learning | cs.LG | We consider learning problems over training sets in which both, the number of
training examples and the dimension of the feature vectors, are large. To solve
these problems we propose the random parallel stochastic algorithm (RAPSA). We
call the algorithm random parallel because it utilizes multiple parallel
processors... | computer science |
11,577 | Exponential expressivity in deep neural networks through transient chaos | stat.ML | We combine Riemannian geometry with the mean field theory of high dimensional
chaos to study the nature of signal propagation in generic, deep neural
networks with random weights. Our results reveal an order-to-chaos expressivity
phase transition, with networks in the chaotic phase computing nonlinear
functions whose g... | computer science |
11,578 | Sampling Method for Fast Training of Support Vector Data Description | cs.LG | Support Vector Data Description (SVDD) is a popular outlier detection
technique which constructs a flexible description of the input data. SVDD
computation time is high for large training datasets which limits its use in
big-data process-monitoring applications. We propose a new iterative
sampling-based method for SVDD... | computer science |
11,579 | Early Visual Concept Learning with Unsupervised Deep Learning | stat.ML | Automated discovery of early visual concepts from raw image data is a major
open challenge in AI research. Addressing this problem, we propose an
unsupervised approach for learning disentangled representations of the
underlying factors of variation. We draw inspiration from neuroscience, and
show how this can be achiev... | computer science |
11,580 | Balancing New Against Old Information: The Role of Surprise in Learning | stat.ML | Surprise describes a range of phenomena from unexpected events to behavioral
responses. We propose a measure of surprise and use it for surprise-driven
learning. Our surprise measure takes into account data likelihood as well as
the degree of commitment to a belief via the entropy of the belief
distribution. We find th... | computer science |
11,581 | Guaranteed bounds on the Kullback-Leibler divergence of univariate
mixtures using piecewise log-sum-exp inequalities | cs.LG | Information-theoretic measures such as the entropy, cross-entropy and the
Kullback-Leibler divergence between two mixture models is a core primitive in
many signal processing tasks. Since the Kullback-Leibler divergence of mixtures
provably does not admit a closed-form formula, it is in practice either
estimated using ... | computer science |
11,582 | Continuum directions for supervised dimension reduction | stat.ME | Dimension reduction of multivariate data supervised by auxiliary information
is considered. A series of basis for dimension reduction is obtained as
minimizers of a novel criterion. The proposed method is akin to continuum
regression, and the resulting basis is called continuum directions. With a
presence of binary sup... | computer science |
11,583 | An artificial neural network to find correlation patterns in an
arbitrary number of variables | cs.LG | Methods to find correlation among variables are of interest to many
disciplines, including statistics, machine learning, (big) data mining and
neurosciences. Parameters that measure correlation between two variables are of
limited utility when used with multiple variables. In this work, I propose a
simple criterion to ... | computer science |
11,584 | Towards stationary time-vertex signal processing | cs.LG | Graph-based methods for signal processing have shown promise for the analysis
of data exhibiting irregular structure, such as those found in social,
transportation, and sensor networks. Yet, though these systems are often
dynamic, state-of-the-art methods for signal processing on graphs ignore the
dimension of time, tr... | computer science |
11,585 | Unsupervised preprocessing for Tactile Data | cs.RO | Tactile information is important for gripping, stable grasp, and in-hand
manipulation, yet the complexity of tactile data prevents widespread use of
such sensors. We make use of an unsupervised learning algorithm that transforms
the complex tactile data into a compact, latent representation without the need
to record g... | computer science |
11,586 | Personalized Prognostic Models for Oncology: A Machine Learning Approach | stat.AP | We have applied a little-known data transformation to subsets of the
Surveillance, Epidemiology, and End Results (SEER) publically available data of
the National Cancer Institute (NCI) to make it suitable input to standard
machine learning classifiers. This transformation properly treats the
right-censored data in the ... | computer science |
11,587 | Wide & Deep Learning for Recommender Systems | cs.LG | Generalized linear models with nonlinear feature transformations are widely
used for large-scale regression and classification problems with sparse inputs.
Memorization of feature interactions through a wide set of cross-product
feature transformations are effective and interpretable, while generalization
requires more... | computer science |
11,588 | Theory reconstruction: a representation learning view on predicate
invention | stat.ML | With this positional paper we present a representation learning view on
predicate invention. The intention of this proposal is to bridge the relational
and deep learning communities on the problem of predicate invention. We propose
a theory reconstruction approach, a formalism that extends autoencoder approach
to repre... | computer science |
11,589 | Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary
and Multiclass Imbalanced Data | cs.LG | Class imbalance presents a major hurdle in the application of data mining
methods. A common practice to deal with it is to create ensembles of
classifiers that learn from resampled balanced data. For example, bagged
decision trees combined with random undersampling (RUS) or the synthetic
minority oversampling technique... | computer science |
11,590 | European Union regulations on algorithmic decision-making and a "right
to explanation" | stat.ML | We summarize the potential impact that the European Union's new General Data
Protection Regulation will have on the routine use of machine learning
algorithms. Slated to take effect as law across the EU in 2018, it will
restrict automated individual decision-making (that is, algorithms that make
decisions based on user... | computer science |
11,591 | Disease Trajectory Maps | stat.ML | Medical researchers are coming to appreciate that many diseases are in fact
complex, heterogeneous syndromes composed of subpopulations that express
different variants of a related complication. Time series data extracted from
individual electronic health records (EHR) offer an exciting new way to study
subtle differen... | computer science |
11,592 | Multi-class classification: mirror descent approach | math.OC | We consider the problem of multi-class classification and a stochastic opti-
mization approach to it. We derive risk bounds for stochastic mirror descent
algorithm and provide examples of set geometries that make the use of the
algorithm efficient in terms of error in k. | computer science |
11,593 | Deep Learning with Differential Privacy | stat.ML | Machine learning techniques based on neural networks are achieving remarkable
results in a wide variety of domains. Often, the training of models requires
large, representative datasets, which may be crowdsourced and contain sensitive
information. The models should not expose private information in these
datasets. Addr... | computer science |
11,594 | Convergence Rate of Frank-Wolfe for Non-Convex Objectives | math.OC | We give a simple proof that the Frank-Wolfe algorithm obtains a stationary
point at a rate of $O(1/\sqrt{t})$ on non-convex objectives with a Lipschitz
continuous gradient. Our analysis is affine invariant and is the first, to the
best of our knowledge, giving a similar rate to what was already proven for
projected gra... | computer science |
11,595 | Decoding the Encoding of Functional Brain Networks: an fMRI
Classification Comparison of Non-negative Matrix Factorization (NMF),
Independent Component Analysis (ICA), and Sparse Coding Algorithms | cs.LG | Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet mathematical constraints such as sparse coding
and positivity both provide alternate biologically-plausible frameworks for
generating brain networks. Non-negative Matrix Factorization (NMF) would
suppress negative BO... | computer science |
11,596 | Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional
Network | cs.LG | Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer\{'}s disease (AD), is based on classification of
features extracted from brain images. The features have to accurately capture
main AD-related variations of anatomical brain structures, such as, e.g.,
ventricles size, hippocamp... | computer science |
11,597 | Approximate Joint Matrix Triangularization | cs.NA | We consider the problem of approximate joint triangularization of a set of
noisy jointly diagonalizable real matrices. Approximate joint triangularizers
are commonly used in the estimation of the joint eigenstructure of a set of
matrices, with applications in signal processing, linear algebra, and tensor
decomposition.... | computer science |
11,598 | Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D
Convolutional Network | cs.LG | Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer's disease (AD), is based on classification of features
extracted from brain images. The features have to accurately capture main
AD-related variations of anatomical brain structures, such as, e.g., ventricles
size, hippocampus ... | computer science |
11,599 | node2vec: Scalable Feature Learning for Networks | cs.SI | Prediction tasks over nodes and edges in networks require careful effort in
engineering features used by learning algorithms. Recent research in the
broader field of representation learning has led to significant progress in
automating prediction by learning the features themselves. However, present
feature learning ap... | computer science |
11,600 | Accelerated Stochastic Subgradient Methods under Local Error Bound
Condition | math.OC | In this paper, we propose two {\bf accelerated stochastic subgradient}
methods for stochastic non-strongly convex optimization problems by leveraging
a generic local error bound condition. The novelty of the proposed methods lies
at smartly leveraging the recent historical solution to tackle the variance in
the stochas... | computer science |
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