Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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5,600 | Hilbert Space Embeddings of Predictive State Representations | cs.LG | Predictive State Representations (PSRs) are an expressive class of models for
controlled stochastic processes. PSRs represent state as a set of predictions
of future observable events. Because PSRs are defined entirely in terms of
observable data, statistically consistent estimates of PSR parameters can be
learned effi... | computer science |
5,601 | Sample Complexity of Multi-task Reinforcement Learning | cs.LG | Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this p... | computer science |
5,602 | Convex Relaxations of Bregman Divergence Clustering | cs.LG | Although many convex relaxations of clustering have been proposed in the past
decade, current formulations remain restricted to spherical Gaussian or
discriminative models and are susceptible to imbalanced clusters. To address
these shortcomings, we propose a new class of convex relaxations that can be
flexibly applied... | computer science |
5,603 | Building Bridges: Viewing Active Learning from the Multi-Armed Bandit
Lens | cs.LG | In this paper we propose a multi-armed bandit inspired, pool based active
learning algorithm for the problem of binary classification. By carefully
constructing an analogy between active learning and multi-armed bandits, we
utilize ideas such as lower confidence bounds, and self-concordant
regularization from the multi... | computer science |
5,604 | Batch-iFDD for Representation Expansion in Large MDPs | cs.LG | Matching pursuit (MP) methods are a promising class of feature construction
algorithms for value function approximation. Yet existing MP methods require
creating a pool of potential features, mandating expert knowledge or
enumeration of a large feature pool, both of which hinder scalability. This
paper introduces batch... | computer science |
5,605 | Multiple Instance Learning by Discriminative Training of Markov Networks | cs.LG | We introduce a graphical framework for multiple instance learning (MIL) based
on Markov networks. This framework can be used to model the traditional MIL
definition as well as more general MIL definitions. Different levels of
ambiguity -- the portion of positive instances in a bag -- can be explored in
weakly supervise... | computer science |
5,606 | Unsupervised Learning of Noisy-Or Bayesian Networks | cs.LG | This paper considers the problem of learning the parameters in Bayesian
networks of discrete variables with known structure and hidden variables.
Previous approaches in these settings typically use expectation maximization;
when the network has high treewidth, the required expectations might be
approximated using Monte... | computer science |
5,607 | Gaussian Processes for Big Data | cs.LG | We introduce stochastic variational inference for Gaussian process models.
This enables the application of Gaussian process (GP) models to data sets
containing millions of data points. We show how GPs can be vari- ationally
decomposed to depend on a set of globally relevant inducing variables which
factorize the model ... | computer science |
5,608 | Inverse Covariance Estimation for High-Dimensional Data in Linear Time
and Space: Spectral Methods for Riccati and Sparse Models | cs.LG | We propose maximum likelihood estimation for learning Gaussian graphical
models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast
to the commonly used Laplace (ell_1) prior for encouraging sparseness. We show
that our optimization problem leads to a Riccati matrix equation, which has a
closed form ... | computer science |
5,609 | Constrained Bayesian Inference for Low Rank Multitask Learning | cs.LG | We present a novel approach for constrained Bayesian inference. Unlike
current methods, our approach does not require convexity of the constraint set.
We reduce the constrained variational inference to a parametric optimization
over the feasible set of densities and propose a general recipe for such
problems. We apply ... | computer science |
5,610 | Learning Max-Margin Tree Predictors | cs.LG | Structured prediction is a powerful framework for coping with joint
prediction of interacting outputs. A central difficulty in using this framework
is that often the correct label dependence structure is unknown. At the same
time, we would like to avoid an overly complex structure that will lead to
intractable predicti... | computer science |
5,611 | The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature
Models | cs.LG | We propose a probabilistic model to infer supervised latent variables in the
Hamming space from observed data. Our model allows simultaneous inference of
the number of binary latent variables, and their values. The latent variables
preserve neighbourhood structure of the data in a sense that objects in the
same semanti... | computer science |
5,612 | Determinantal Clustering Processes - A Nonparametric Bayesian Approach
to Kernel Based Semi-Supervised Clustering | cs.LG | Semi-supervised clustering is the task of clustering data points into
clusters where only a fraction of the points are labelled. The true number of
clusters in the data is often unknown and most models require this parameter as
an input. Dirichlet process mixture models are appealing as they can infer the
number of clu... | computer science |
5,613 | Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs | cs.LG | We seek to learn an effective policy for a Markov Decision Process (MDP) with
continuous states via Q-Learning. Given a set of basis functions over state
action pairs we search for a corresponding set of linear weights that minimizes
the mean Bellman residual. Our algorithm uses a Kalman filter model to estimate
those ... | computer science |
5,614 | Finite-Time Analysis of Kernelised Contextual Bandits | cs.LG | We tackle the problem of online reward maximisation over a large finite set
of actions described by their contexts. We focus on the case when the number of
actions is too big to sample all of them even once. However we assume that we
have access to the similarities between actions' contexts and that the expected
reward... | computer science |
5,615 | Active Learning with Expert Advice | cs.LG | Conventional learning with expert advice methods assumes a learner is always
receiving the outcome (e.g., class labels) of every incoming training instance
at the end of each trial. In real applications, acquiring the outcome from
oracle can be costly or time consuming. In this paper, we address a new problem
of active... | computer science |
5,616 | Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate
of Convergence | stat.ML | In this paper, we present the Bennett-type generalization bounds of the
learning process for i.i.d. samples, and then show that the generalization
bounds have a faster rate of convergence than the traditional results. In
particular, we first develop two types of Bennett-type deviation inequality for
the i.i.d. learning... | computer science |
5,617 | Bayesian Inference in Sparse Gaussian Graphical Models | stat.ML | One of the fundamental tasks of science is to find explainable relationships
between observed phenomena. One approach to this task that has received
attention in recent years is based on probabilistic graphical modelling with
sparsity constraints on model structures. In this paper, we describe two new
approaches to Bay... | computer science |
5,618 | An upper bound on prototype set size for condensed nearest neighbor | cs.LG | The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing
the number of prototypical points stored by a nearest neighbor classifier,
while keeping the classification rule given by the reduced prototypical set
consistent with the full set. I present an upper bound on the number of
prototypical points ac... | computer science |
5,619 | Monotonous (Semi-)Nonnegative Matrix Factorization | cs.LG | Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into
product of two non-negative matrices, namely a signal matrix and a mixing
matrix. NMF suffers from the scale and ordering ambiguities. Often, the source
signals can be monotonous in nature. For example, in source separation problem,
the source... | computer science |
5,620 | Kernel Spectral Clustering and applications | cs.LG | In this chapter we review the main literature related to kernel spectral
clustering (KSC), an approach to clustering cast within a kernel-based
optimization setting. KSC represents a least-squares support vector machine
based formulation of spectral clustering described by a weighted kernel PCA
objective. Just as in th... | computer science |
5,621 | On Regret-Optimal Learning in Decentralized Multi-player Multi-armed
Bandits | stat.ML | We consider the problem of learning in single-player and multiplayer
multiarmed bandit models. Bandit problems are classes of online learning
problems that capture exploration versus exploitation tradeoffs. In a
multiarmed bandit model, players can pick among many arms, and each play of an
arm generates an i.i.d. rewar... | computer science |
5,622 | Re-scale boosting for regression and classification | cs.LG | Boosting is a learning scheme that combines weak prediction rules to produce
a strong composite estimator, with the underlying intuition that one can obtain
accurate prediction rules by combining "rough" ones. Although boosting is
proved to be consistent and overfitting-resistant, its numerical convergence
rate is rela... | computer science |
5,623 | Optimal Decision-Theoretic Classification Using Non-Decomposable
Performance Metrics | cs.LG | We provide a general theoretical analysis of expected out-of-sample utility,
also referred to as decision-theoretic classification, for non-decomposable
binary classification metrics such as F-measure and Jaccard coefficient. Our
key result is that the expected out-of-sample utility for many performance
metrics is prov... | computer science |
5,624 | DART: Dropouts meet Multiple Additive Regression Trees | cs.LG | Multiple Additive Regression Trees (MART), an ensemble model of boosted
regression trees, is known to deliver high prediction accuracy for diverse
tasks, and it is widely used in practice. However, it suffers an issue which we
call over-specialization, wherein trees added at later iterations tend to
impact the predicti... | computer science |
5,625 | An Asymptotically Optimal Policy for Uniform Bandits of Unknown Support | stat.ML | Consider the problem of a controller sampling sequentially from a finite
number of $N \geq 2$ populations, specified by random variables $X^i_k$, $ i =
1,\ldots , N,$ and $k = 1, 2, \ldots$; where $X^i_k$ denotes the outcome from
population $i$ the $k^{th}$ time it is sampled. It is assumed that for each
fixed $i$, $\{... | computer science |
5,626 | Estimation with Norm Regularization | stat.ML | Analysis of non-asymptotic estimation error and structured statistical
recovery based on norm regularized regression, such as Lasso, needs to consider
four aspects: the norm, the loss function, the design matrix, and the noise
model. This paper presents generalizations of such estimation error analysis on
all four aspe... | computer science |
5,627 | Spike and Slab Gaussian Process Latent Variable Models | stat.ML | The Gaussian process latent variable model (GP-LVM) is a popular approach to
non-linear probabilistic dimensionality reduction. One design choice for the
model is the number of latent variables. We present a spike and slab prior for
the GP-LVM and propose an efficient variational inference procedure that gives
a lower ... | computer science |
5,628 | Asymptotic Behavior of Minimal-Exploration Allocation Policies: Almost
Sure, Arbitrarily Slow Growing Regret | stat.ML | The purpose of this paper is to provide further understanding into the
structure of the sequential allocation ("stochastic multi-armed bandit", or
MAB) problem by establishing probability one finite horizon bounds and
convergence rates for the sample (or "pseudo") regret associated with two
simple classes of allocation... | computer science |
5,629 | Incorporating Type II Error Probabilities from Independence Tests into
Score-Based Learning of Bayesian Network Structure | cs.LG | We give a new consistent scoring function for structure learning of Bayesian
networks. In contrast to traditional approaches to score-based structure
learning, such as BDeu or MDL, the complexity penalty that we propose is
data-dependent and is given by the probability that a conditional independence
test correctly sho... | computer science |
5,630 | Permutational Rademacher Complexity: a New Complexity Measure for
Transductive Learning | stat.ML | Transductive learning considers situations when a learner observes $m$
labelled training points and $u$ unlabelled test points with the final goal of
giving correct answers for the test points. This paper introduces a new
complexity measure for transductive learning called Permutational Rademacher
Complexity (PRC) and ... | computer science |
5,631 | Training generative neural networks via Maximum Mean Discrepancy
optimization | stat.ML | We consider training a deep neural network to generate samples from an
unknown distribution given i.i.d. data. We frame learning as an optimization
minimizing a two-sample test statistic---informally speaking, a good generator
network produces samples that cause a two-sample test to fail to reject the
null hypothesis. ... | computer science |
5,632 | Consistent Algorithms for Multiclass Classification with a Reject Option | cs.LG | We consider the problem of $n$-class classification ($n\geq 2$), where the
classifier can choose to abstain from making predictions at a given cost, say,
a factor $\alpha$ of the cost of misclassification. Designing consistent
algorithms for such $n$-class classification problems with a `reject option' is
the main goal... | computer science |
5,633 | Provably Correct Algorithms for Matrix Column Subset Selection with
Selectively Sampled Data | stat.ML | We consider the problem of matrix column subset selection, which selects a
subset of columns from an input matrix such that the input can be well
approximated by the span of the selected columns. Column subset selection has
been applied to numerous real-world data applications such as population
genetics summarization,... | computer science |
5,634 | Simple regret for infinitely many armed bandits | cs.LG | We consider a stochastic bandit problem with infinitely many arms. In this
setting, the learner has no chance of trying all the arms even once and has to
dedicate its limited number of samples only to a certain number of arms. All
previous algorithms for this setting were designed for minimizing the
cumulative regret o... | computer science |
5,635 | Compressed Nonnegative Matrix Factorization is Fast and Accurate | cs.LG | Nonnegative matrix factorization (NMF) has an established reputation as a
useful data analysis technique in numerous applications. However, its usage in
practical situations is undergoing challenges in recent years. The fundamental
factor to this is the increasingly growing size of the datasets available and
needed in ... | computer science |
5,636 | Multi-task additive models with shared transfer functions based on
dictionary learning | stat.ML | Additive models form a widely popular class of regression models which
represent the relation between covariates and response variables as the sum of
low-dimensional transfer functions. Besides flexibility and accuracy, a key
benefit of these models is their interpretability: the transfer functions
provide visual means... | computer science |
5,637 | Risk and Regret of Hierarchical Bayesian Learners | cs.LG | Common statistical practice has shown that the full power of Bayesian methods
is not realized until hierarchical priors are used, as these allow for greater
"robustness" and the ability to "share statistical strength." Yet it is an
ongoing challenge to provide a learning-theoretically sound formalism of such
notions th... | computer science |
5,638 | oASIS: Adaptive Column Sampling for Kernel Matrix Approximation | stat.ML | Kernel matrices (e.g. Gram or similarity matrices) are essential for many
state-of-the-art approaches to classification, clustering, and dimensionality
reduction. For large datasets, the cost of forming and factoring such kernel
matrices becomes intractable. To address this challenge, we introduce a new
adaptive sampli... | computer science |
5,639 | Supervised Learning for Dynamical System Learning | stat.ML | Recently there has been substantial interest in spectral methods for learning
dynamical systems. These methods are popular since they often offer a good
tradeoff between computational and statistical efficiency. Unfortunately, they
can be difficult to use and extend in practice: e.g., they can make it
difficult to inco... | computer science |
5,640 | Weight Uncertainty in Neural Networks | stat.ML | We introduce a new, efficient, principled and backpropagation-compatible
algorithm for learning a probability distribution on the weights of a neural
network, called Bayes by Backprop. It regularises the weights by minimising a
compression cost, known as the variational free energy or the expected lower
bound on the ma... | computer science |
5,641 | Regulating Greed Over Time | stat.ML | In retail, there are predictable yet dramatic time-dependent patterns in
customer behavior, such as periodic changes in the number of visitors, or
increases in visitors just before major holidays. The current paradigm of
multi-armed bandit analysis does not take these known patterns into account.
This means that for ap... | computer science |
5,642 | The Benefit of Multitask Representation Learning | stat.ML | We discuss a general method to learn data representations from multiple
tasks. We provide a justification for this method in both settings of multitask
learning and learning-to-learn. The method is illustrated in detail in the
special case of linear feature learning. Conditions on the theoretical
advantage offered by m... | computer science |
5,643 | Tight Continuous Relaxation of the Balanced $k$-Cut Problem | stat.ML | Spectral Clustering as a relaxation of the normalized/ratio cut has become
one of the standard graph-based clustering methods. Existing methods for the
computation of multiple clusters, corresponding to a balanced $k$-cut of the
graph, are either based on greedy techniques or heuristics which have weak
connection to th... | computer science |
5,644 | Constrained 1-Spectral Clustering | stat.ML | An important form of prior information in clustering comes in form of
cannot-link and must-link constraints. We present a generalization of the
popular spectral clustering technique which integrates such constraints.
Motivated by the recently proposed $1$-spectral clustering for the
unconstrained problem, our method is... | computer science |
5,645 | Electre Tri-Machine Learning Approach to the Record Linkage Problem | stat.ML | In this short paper, the Electre Tri-Machine Learning Method, generally used
to solve ordinal classification problems, is proposed for solving the Record
Linkage problem. Preliminary experimental results show that, using the Electre
Tri method, high accuracy can be achieved and more than 99% of the matches and
nonmatch... | computer science |
5,646 | Sketching for Sequential Change-Point Detection | cs.LG | We study sequential change-point detection using sketches (linear
projections) of high-dimensional signal vectors, by presenting the sketching
procedures that are derived based on the generalized likelihood ratio
statistic. We consider both fixed and time-varying projections, and derive
theoretical approximations to tw... | computer science |
5,647 | Optimizing Non-decomposable Performance Measures: A Tale of Two Classes | stat.ML | Modern classification problems frequently present mild to severe label
imbalance as well as specific requirements on classification characteristics,
and require optimizing performance measures that are non-decomposable over the
dataset, such as F-measure. Such measures have spurred much interest and pose
specific chall... | computer science |
5,648 | Surrogate Functions for Maximizing Precision at the Top | stat.ML | The problem of maximizing precision at the top of a ranked list, often dubbed
Precision@k (prec@k), finds relevance in myriad learning applications such as
ranking, multi-label classification, and learning with severe label imbalance.
However, despite its popularity, there exist significant gaps in our
understanding of... | computer science |
5,649 | Some Open Problems in Optimal AdaBoost and Decision Stumps | cs.LG | The significance of the study of the theoretical and practical properties of
AdaBoost is unquestionable, given its simplicity, wide practical use, and
effectiveness on real-world datasets. Here we present a few open problems
regarding the behavior of "Optimal AdaBoost," a term coined by Rudin,
Daubechies, and Schapire ... | computer science |
5,650 | An Overview of the Asymptotic Performance of the Family of the FastICA
Algorithms | stat.ML | This contribution summarizes the results on the asymptotic performance of
several variants of the FastICA algorithm. A number of new closed-form
expressions are presented. | computer science |
5,651 | Belief Flows of Robust Online Learning | stat.ML | This paper introduces a new probabilistic model for online learning which
dynamically incorporates information from stochastic gradients of an arbitrary
loss function. Similar to probabilistic filtering, the model maintains a
Gaussian belief over the optimal weight parameters. Unlike traditional Bayesian
updates, the m... | computer science |
5,652 | A Characterization of the Non-Uniqueness of Nonnegative Matrix
Factorizations | cs.LG | Nonnegative matrix factorization (NMF) is a popular dimension reduction
technique that produces interpretable decomposition of the data into parts.
However, this decompostion is not generally identifiable (even up to
permutation and scaling). While other studies have provide criteria under which
NMF is identifiable, we... | computer science |
5,653 | The CMA Evolution Strategy: A Tutorial | cs.LG | This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands
for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized,
method for real-parameter (continuous domain) optimization of non-linear,
non-convex functions. We try to motivate and derive the algorithm from
intuitive concepts and ... | computer science |
5,654 | Bayesian Optimization with Exponential Convergence | stat.ML | This paper presents a Bayesian optimization method with exponential
convergence without the need of auxiliary optimization and without the
delta-cover sampling. Most Bayesian optimization methods require auxiliary
optimization: an additional non-convex global optimization problem, which can
be time-consuming and hard t... | computer science |
5,655 | Building Ensembles of Adaptive Nested Dichotomies with Random-Pair
Selection | stat.ML | A system of nested dichotomies is a method of decomposing a multi-class
problem into a collection of binary problems. Such a system recursively splits
the set of classes into two subsets, and trains a binary classifier to
distinguish between each subset. Even though ensembles of nested dichotomies
with random structure... | computer science |
5,656 | Online Optimization of Smoothed Piecewise Constant Functions | cs.LG | We study online optimization of smoothed piecewise constant functions over
the domain [0, 1). This is motivated by the problem of adaptively picking
parameters of learning algorithms as in the recently introduced framework by
Gupta and Roughgarden (2016). Majority of the machine learning literature has
focused on Lipsc... | computer science |
5,657 | Challenges in Bayesian Adaptive Data Analysis | cs.LG | Traditional statistical analysis requires that the analysis process and data
are independent. By contrast, the new field of adaptive data analysis hopes to
understand and provide algorithms and accuracy guarantees for research as it is
commonly performed in practice, as an iterative process of interacting
repeatedly wi... | computer science |
5,658 | Confidence Decision Trees via Online and Active Learning for Streaming
(BIG) Data | stat.ML | Decision tree classifiers are a widely used tool in data stream mining. The
use of confidence intervals to estimate the gain associated with each split
leads to very effective methods, like the popular Hoeffding tree algorithm.
From a statistical viewpoint, the analysis of decision tree classifiers in a
streaming setti... | computer science |
5,659 | Loss Bounds and Time Complexity for Speed Priors | cs.LG | This paper establishes for the first time the predictive performance of speed
priors and their computational complexity. A speed prior is essentially a
probability distribution that puts low probability on strings that are not
efficiently computable. We propose a variant to the original speed prior
(Schmidhuber, 2002),... | computer science |
5,660 | A Convex Surrogate Operator for General Non-Modular Loss Functions | stat.ML | Empirical risk minimization frequently employs convex surrogates to
underlying discrete loss functions in order to achieve computational
tractability during optimization. However, classical convex surrogates can only
tightly bound modular loss functions, sub-modular functions or supermodular
functions separately while ... | computer science |
5,661 | A Differentiable Transition Between Additive and Multiplicative Neurons | cs.LG | Existing approaches to combine both additive and multiplicative neural units
either use a fixed assignment of operations or require discrete optimization to
determine what function a neuron should perform. However, this leads to an
extensive increase in the computational complexity of the training procedure.
We prese... | computer science |
5,662 | Consistently Estimating Markov Chains with Noisy Aggregate Data | cs.LG | We address the problem of estimating the parameters of a time-homogeneous
Markov chain given only noisy, aggregate data. This arises when a population of
individuals behave independently according to a Markov chain, but individual
sample paths cannot be observed due to limitations of the observation process
or the need... | computer science |
5,663 | Bayesian linear regression with Student-t assumptions | cs.LG | As an automatic method of determining model complexity using the training
data alone, Bayesian linear regression provides us a principled way to select
hyperparameters. But one often needs approximation inference if distribution
assumption is beyond Gaussian distribution. In this paper, we propose a
Bayesian linear reg... | computer science |
5,664 | A short note on extension theorems and their connection to universal
consistency in machine learning | stat.ML | Statistical machine learning plays an important role in modern statistics and
computer science. One main goal of statistical machine learning is to provide
universally consistent algorithms, i.e., the estimator converges in probability
or in some stronger sense to the Bayes risk or to the Bayes decision function.
Kerne... | computer science |
5,665 | DS-MLR: Exploiting Double Separability for Scaling up Distributed
Multinomial Logistic Regression | cs.LG | Scaling multinomial logistic regression to datasets with very large number of
data points and classes has not been trivial. This is primarily because one
needs to compute the log-partition function on every data point. This makes
distributing the computation hard. In this paper, we present a distributed
stochastic grad... | computer science |
5,666 | Identifying global optimality for dictionary learning | stat.ML | Learning new representations of input observations in machine learning is
often tackled using a factorization of the data. For many such problems,
including sparse coding and matrix completion, learning these factorizations
can be difficult, in terms of efficiency and to guarantee that the solution is
a global minimum.... | computer science |
5,667 | Gaussian Copula Variational Autoencoders for Mixed Data | stat.ML | The variational autoencoder (VAE) is a generative model with continuous
latent variables where a pair of probabilistic encoder (bottom-up) and decoder
(top-down) is jointly learned by stochastic gradient variational Bayes. We
first elaborate Gaussian VAE, approximating the local covariance matrix of the
decoder as an o... | computer science |
5,668 | Chained Gaussian Processes | stat.ML | Gaussian process models are flexible, Bayesian non-parametric approaches to
regression. Properties of multivariate Gaussians mean that they can be combined
linearly in the manner of additive models and via a link function (like in
generalized linear models) to handle non-Gaussian data. However, the link
function formal... | computer science |
5,669 | Streaming Label Learning for Modeling Labels on the Fly | stat.ML | It is challenging to handle a large volume of labels in multi-label learning.
However, existing approaches explicitly or implicitly assume that all the
labels in the learning process are given, which could be easily violated in
changing environments. In this paper, we define and study streaming label
learning (SLL), i.... | computer science |
5,670 | Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive
Models | stat.ML | Predictive models are finding an increasing number of applications in many
industries. As a result, a practical means for trading-off the cost of
deploying a model versus its effectiveness is needed. Our work is motivated by
risk prediction problems in healthcare. Cost-structures in domains such as
healthcare are quite... | computer science |
5,671 | Stabilized Sparse Online Learning for Sparse Data | stat.ML | Stochastic gradient descent (SGD) is commonly used for optimization in
large-scale machine learning problems. Langford et al. (2009) introduce a
sparse online learning method to induce sparsity via truncated gradient. With
high-dimensional sparse data, however, the method suffers from slow convergence
and high variance... | computer science |
5,672 | Approximation Vector Machines for Large-scale Online Learning | cs.LG | One of the most challenging problems in kernel online learning is to bound
the model size and to promote the model sparsity. Sparse models not only
improve computation and memory usage, but also enhance the generalization
capacity, a principle that concurs with the law of parsimony. However,
inappropriate sparsity mode... | computer science |
5,673 | Double Thompson Sampling for Dueling Bandits | cs.LG | In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for
dueling bandit problems. As indicated by its name, D-TS selects both the first
and the second candidates according to Thompson Sampling. Specifically, D-TS
maintains a posterior distribution for the preference matrix, and chooses the
pair of arms... | computer science |
5,674 | Neural Random Forests | stat.ML | Given an ensemble of randomized regression trees, it is possible to
restructure them as a collection of multilayered neural networks with
particular connection weights. Following this principle, we reformulate the
random forest method of Breiman (2001) into a neural network setting, and in
turn propose two new hybrid p... | computer science |
5,675 | Fast nonlinear embeddings via structured matrices | stat.ML | We present a new paradigm for speeding up randomized computations of several
frequently used functions in machine learning. In particular, our paradigm can
be applied for improving computations of kernels based on random embeddings.
Above that, the presented framework covers multivariate randomized functions.
As a bypr... | computer science |
5,676 | Deep Multi-fidelity Gaussian Processes | cs.LG | We develop a novel multi-fidelity framework that goes far beyond the
classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can
handle general discontinuous cross-correlations among systems with different
levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1)
Co-kriging) and ... | computer science |
5,677 | Condorcet's Jury Theorem for Consensus Clustering and its Implications
for Diversity | stat.ML | Condorcet's Jury Theorem has been invoked for ensemble classifiers to
indicate that the combination of many classifiers can have better predictive
performance than a single classifier. Such a theoretical underpinning is
unknown for consensus clustering. This article extends Condorcet's Jury Theorem
to the mean partitio... | computer science |
5,678 | Streaming View Learning | stat.ML | An underlying assumption in conventional multi-view learning algorithms is
that all views can be simultaneously accessed. However, due to various factors
when collecting and pre-processing data from different views, the streaming
view setting, in which views arrive in a streaming manner, is becoming more
common. By ass... | computer science |
5,679 | Kernels on Sample Sets via Nonparametric Divergence Estimates | cs.LG | Most machine learning algorithms, such as classification or regression, treat
the individual data point as the object of interest. Here we consider extending
machine learning algorithms to operate on groups of data points. We suggest
treating a group of data points as an i.i.d. sample set from an underlying
feature dis... | computer science |
5,680 | A Reconstruction Error Formulation for Semi-Supervised Multi-task and
Multi-view Learning | cs.LG | A significant challenge to make learning techniques more suitable for general
purpose use is to move beyond i) complete supervision, ii) low dimensional
data, iii) a single task and single view per instance. Solving these challenges
allows working with "Big Data" problems that are typically high dimensional
with multip... | computer science |
5,681 | Cramer Rao-Type Bounds for Sparse Bayesian Learning | cs.LG | In this paper, we derive Hybrid, Bayesian and Marginalized Cram\'{e}r-Rao
lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement
vector Sparse Bayesian Learning (SBL) problem of estimating compressible
vectors and their prior distribution parameters. We assume the unknown vector
to be drawn from a c... | computer science |
5,682 | rFerns: An Implementation of the Random Ferns Method for General-Purpose
Machine Learning | cs.LG | In this paper I present an extended implementation of the Random ferns
algorithm contained in the R package rFerns. It differs from the original by
the ability of consuming categorical and numerical attributes instead of only
binary ones. Also, instead of using simple attribute subspace ensemble it
employs bagging and ... | computer science |
5,683 | Information Forests | cs.LG | We describe Information Forests, an approach to classification that
generalizes Random Forests by replacing the splitting criterion of non-leaf
nodes from a discriminative one -- based on the entropy of the label
distribution -- to a generative one -- based on maximizing the information
divergence between the class-con... | computer science |
5,684 | Towards minimax policies for online linear optimization with bandit
feedback | cs.LG | We address the online linear optimization problem with bandit feedback. Our
contribution is twofold. First, we provide an algorithm (based on exponential
weights) with a regret of order $\sqrt{d n \log N}$ for any finite action set
with $N$ actions, under the assumption that the instantaneous loss is bounded
by 1. This... | computer science |
5,685 | Mirror Descent Meets Fixed Share (and feels no regret) | cs.LG | Mirror descent with an entropic regularizer is known to achieve shifting
regret bounds that are logarithmic in the dimension. This is done using either
a carefully designed projection or by a weight sharing technique. Via a novel
unified analysis, we show that these two approaches deliver essentially
equivalent bounds ... | computer science |
5,686 | Semi-supervised Learning with Density Based Distances | cs.LG | We present a simple, yet effective, approach to Semi-Supervised Learning. Our
approach is based on estimating density-based distances (DBD) using a shortest
path calculation on a graph. These Graph-DBD estimates can then be used in any
distance-based supervised learning method, such as Nearest Neighbor methods and
SVMs... | computer science |
5,687 | Near-Optimal Target Learning With Stochastic Binary Signals | cs.LG | We study learning in a noisy bisection model: specifically, Bayesian
algorithms to learn a target value V given access only to noisy realizations of
whether V is less than or greater than a threshold theta. At step t = 0, 1, 2,
..., the learner sets threshold theta t and observes a noisy realization of
sign(V - theta t... | computer science |
5,688 | Smoothing Proximal Gradient Method for General Structured Sparse
Learning | cs.LG | We study the problem of learning high dimensional regression models
regularized by a structured-sparsity-inducing penalty that encodes prior
structural information on either input or output sides. We consider two widely
adopted types of such penalties as our motivating examples: 1) overlapping
group lasso penalty, base... | computer science |
5,689 | Ensembles of Kernel Predictors | cs.LG | This paper examines the problem of learning with a finite and possibly large
set of p base kernels. It presents a theoretical and empirical analysis of an
approach addressing this problem based on ensembles of kernel predictors. This
includes novel theoretical guarantees based on the Rademacher complexity of the
corres... | computer science |
5,690 | Active Learning for Developing Personalized Treatment | cs.LG | The personalization of treatment via bio-markers and other risk categories
has drawn increasing interest among clinical scientists. Personalized treatment
strategies can be learned using data from clinical trials, but such trials are
very costly to run. This paper explores the use of active learning techniques
to desig... | computer science |
5,691 | Boosting as a Product of Experts | cs.LG | In this paper, we derive a novel probabilistic model of boosting as a Product
of Experts. We re-derive the boosting algorithm as a greedy incremental model
selection procedure which ensures that addition of new experts to the ensemble
does not decrease the likelihood of the data. These learning rules lead to a
generic ... | computer science |
5,692 | PAC-Bayesian Policy Evaluation for Reinforcement Learning | cs.LG | Bayesian priors offer a compact yet general means of incorporating domain
knowledge into many learning tasks. The correctness of the Bayesian analysis
and inference, however, largely depends on accuracy and correctness of these
priors. PAC-Bayesian methods overcome this problem by providing bounds that
hold regardless ... | computer science |
5,693 | Generalized Fisher Score for Feature Selection | cs.LG | Fisher score is one of the most widely used supervised feature selection
methods. However, it selects each feature independently according to their
scores under the Fisher criterion, which leads to a suboptimal subset of
features. In this paper, we present a generalized Fisher score to jointly
select features. It aims ... | computer science |
5,694 | Active Semi-Supervised Learning using Submodular Functions | cs.LG | We consider active, semi-supervised learning in an offline transductive
setting. We show that a previously proposed error bound for active learning on
undirected weighted graphs can be generalized by replacing graph cut with an
arbitrary symmetric submodular function. Arbitrary non-symmetric submodular
functions can be... | computer science |
5,695 | Bregman divergence as general framework to estimate unnormalized
statistical models | cs.LG | We show that the Bregman divergence provides a rich framework to estimate
unnormalized statistical models for continuous or discrete random variables,
that is, models which do not integrate or sum to one, respectively. We prove
that recent estimation methods such as noise-contrastive estimation, ratio
matching, and sco... | computer science |
5,696 | Sequential Inference for Latent Force Models | cs.LG | Latent force models (LFMs) are hybrid models combining mechanistic principles
with non-parametric components. In this article, we shall show how LFMs can be
equivalently formulated and solved using the state variable approach. We shall
also show how the Gaussian process prior used in LFMs can be equivalently
formulated... | computer science |
5,697 | What Cannot be Learned with Bethe Approximations | cs.LG | We address the problem of learning the parameters in graphical models when
inference is intractable. A common strategy in this case is to replace the
partition function with its Bethe approximation. We show that there exists a
regime of empirical marginals where such Bethe learning will fail. By failure
we mean that th... | computer science |
5,698 | Lipschitz Parametrization of Probabilistic Graphical Models | cs.LG | We show that the log-likelihood of several probabilistic graphical models is
Lipschitz continuous with respect to the lp-norm of the parameters. We discuss
several implications of Lipschitz parametrization. We present an upper bound of
the Kullback-Leibler divergence that allows understanding methods that penalize
the ... | computer science |
5,699 | Noisy-OR Models with Latent Confounding | cs.LG | Given a set of experiments in which varying subsets of observed variables are
subject to intervention, we consider the problem of identifiability of causal
models exhibiting latent confounding. While identifiability is trivial when
each experiment intervenes on a large number of variables, the situation is
more complic... | computer science |
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