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5,400 | High dimensional Sparse Gaussian Graphical Mixture Model | stat.ML | This paper considers the problem of networks reconstruction from
heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well
known that parameter estimation in this context is challenging due to large
numbers of variables coupled with the degeneracy of the likelihood. We propose
as a solution a penal... | computer science |
5,401 | Reference Distance Estimator | cs.LG | A theoretical study is presented for a simple linear classifier called
reference distance estimator (RDE), which assigns the weight of each feature j
as P(r|j)-P(r), where r is a reference feature relevant to the target class y.
The analysis shows that if r performs better than random guess in predicting y
and is condi... | computer science |
5,402 | Distributed Online Learning via Cooperative Contextual Bandits | cs.LG | In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or reques... | computer science |
5,403 | Learning Deep Representation Without Parameter Inference for Nonlinear
Dimensionality Reduction | cs.LG | Unsupervised deep learning is one of the most powerful representation
learning techniques. Restricted Boltzman machine, sparse coding, regularized
auto-encoders, and convolutional neural networks are pioneering building blocks
of deep learning. In this paper, we propose a new building block -- distributed
random models... | computer science |
5,404 | Sparse and Non-Negative BSS for Noisy Data | stat.ML | Non-negative blind source separation (BSS) has raised interest in various
fields of research, as testified by the wide literature on the topic of
non-negative matrix factorization (NMF). In this context, it is fundamental
that the sources to be estimated present some diversity in order to be
efficiently retrieved. Spar... | computer science |
5,405 | Bayesian Conditional Gaussian Network Classifiers with Applications to
Mass Spectra Classification | cs.LG | Classifiers based on probabilistic graphical models are very effective. In
continuous domains, maximum likelihood is usually used to assess the
predictions of those classifiers. When data is scarce, this can easily lead to
overfitting. In any probabilistic setting, Bayesian averaging (BA) provides
theoretically optimal... | computer science |
5,406 | Linear and Parallel Learning of Markov Random Fields | stat.ML | We introduce a new embarrassingly parallel parameter learning algorithm for
Markov random fields with untied parameters which is efficient for a large
class of practical models. Our algorithm parallelizes naturally over cliques
and, for graphs of bounded degree, its complexity is linear in the number of
cliques. Unlike... | computer science |
5,407 | A Stochastic View of Optimal Regret through Minimax Duality | cs.LG | We study the regret of optimal strategies for online convex optimization
games. Using von Neumann's minimax theorem, we show that the optimal regret in
this adversarial setting is closely related to the behavior of the empirical
minimization algorithm in a stochastic process setting: it is equal to the
maximum, over jo... | computer science |
5,408 | Matrix Completion from Noisy Entries | cs.LG | Given a matrix M of low-rank, we consider the problem of reconstructing it
from noisy observations of a small, random subset of its entries. The problem
arises in a variety of applications, from collaborative filtering (the `Netflix
problem') to structure-from-motion and positioning. We study a low complexity
algorithm... | computer science |
5,409 | Regularization Techniques for Learning with Matrices | cs.LG | There is growing body of learning problems for which it is natural to
organize the parameters into matrix, so as to appropriately regularize the
parameters under some matrix norm (in order to impose some more sophisticated
prior knowledge). This work describes and analyzes a systematic method for
constructing such matr... | computer science |
5,410 | Online and Batch Learning Algorithms for Data with Missing Features | cs.LG | We introduce new online and batch algorithms that are robust to data with
missing features, a situation that arises in many practical applications. In
the online setup, we allow for the comparison hypothesis to change as a
function of the subset of features that is observed on any given round,
extending the standard se... | computer science |
5,411 | Adaptive Evolutionary Clustering | cs.LG | In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being ro... | computer science |
5,412 | Robust Clustering Using Outlier-Sparsity Regularization | stat.ML | Notwithstanding the popularity of conventional clustering algorithms such as
K-means and probabilistic clustering, their clustering results are sensitive to
the presence of outliers in the data. Even a few outliers can compromise the
ability of these algorithms to identify meaningful hidden structures rendering
their o... | computer science |
5,413 | Clustering Partially Observed Graphs via Convex Optimization | cs.LG | This paper considers the problem of clustering a partially observed
unweighted graph---i.e., one where for some node pairs we know there is an edge
between them, for some others we know there is no edge, and for the remaining
we do not know whether or not there is an edge. We want to organize the nodes
into disjoint cl... | computer science |
5,414 | Preference elicitation and inverse reinforcement learning | stat.ML | We state the problem of inverse reinforcement learning in terms of preference
elicitation, resulting in a principled (Bayesian) statistical formulation. This
generalises previous work on Bayesian inverse reinforcement learning and allows
us to obtain a posterior distribution on the agent's preferences, policy and
optio... | computer science |
5,415 | Nonparametric Unsupervised Classification | cs.LG | Unsupervised classification methods learn a discriminative classifier from
unlabeled data, which has been proven to be an effective way of simultaneously
clustering the data and training a classifier from the data. Various
unsupervised classification methods obtain appealing results by the classifiers
learned in an uns... | computer science |
5,416 | Local stability and robustness of sparse dictionary learning in the
presence of noise | stat.ML | A popular approach within the signal processing and machine learning
communities consists in modelling signals as sparse linear combinations of
atoms selected from a learned dictionary. While this paradigm has led to
numerous empirical successes in various fields ranging from image to audio
processing, there have only ... | computer science |
5,417 | Graph-Based Approaches to Clustering Network-Constrained Trajectory Data | cs.LG | Even though clustering trajectory data attracted considerable attention in
the last few years, most of prior work assumed that moving objects can move
freely in an euclidean space and did not consider the eventual presence of an
underlying road network and its influence on evaluating the similarity between
trajectories... | computer science |
5,418 | Smooth Sparse Coding via Marginal Regression for Learning Sparse
Representations | stat.ML | We propose and analyze a novel framework for learning sparse representations,
based on two statistical techniques: kernel smoothing and marginal regression.
The proposed approach provides a flexible framework for incorporating feature
similarity or temporal information present in data sets, via non-parametric
kernel sm... | computer science |
5,419 | Fast Conical Hull Algorithms for Near-separable Non-negative Matrix
Factorization | stat.ML | The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)
turns non-negative matrix factorization (NMF) into a tractable problem.
Recently, a new class of provably-correct NMF algorithms have emerged under
this assumption. In this paper, we reformulate the separable NMF problem as
that of finding the ext... | computer science |
5,420 | Unfolding Latent Tree Structures using 4th Order Tensors | cs.LG | Discovering the latent structure from many observed variables is an important
yet challenging learning task. Existing approaches for discovering latent
structures often require the unknown number of hidden states as an input. In
this paper, we propose a quartet based approach which is \emph{agnostic} to
this number. Th... | computer science |
5,421 | Feature Selection via L1-Penalized Squared-Loss Mutual Information | stat.ML | Feature selection is a technique to screen out less important features. Many
existing supervised feature selection algorithms use redundancy and relevancy
as the main criteria to select features. However, feature interaction,
potentially a key characteristic in real-world problems, has not received much
attention. As a... | computer science |
5,422 | Cost-Sensitive Tree of Classifiers | stat.ML | Recently, machine learning algorithms have successfully entered large-scale
real-world industrial applications (e.g. search engines and email spam
filters). Here, the CPU cost during test time must be budgeted and accounted
for. In this paper, we address the challenge of balancing the test-time cost
and the classifier ... | computer science |
5,423 | The Perturbed Variation | cs.LG | We introduce a new discrepancy score between two distributions that gives an
indication on their similarity. While much research has been done to determine
if two samples come from exactly the same distribution, much less research
considered the problem of determining if two finite samples come from similar
distributio... | computer science |
5,424 | Semi-Supervised Classification Through the Bag-of-Paths Group
Betweenness | stat.ML | This paper introduces a novel, well-founded, betweenness measure, called the
Bag-of-Paths (BoP) betweenness, as well as its extension, the BoP group
betweenness, to tackle semisupervised classification problems on weighted
directed graphs. The objective of semi-supervised classification is to assign a
label to unlabele... | computer science |
5,425 | Hilbert Space Embedding for Dirichlet Process Mixtures | stat.ML | This paper proposes a Hilbert space embedding for Dirichlet Process mixture
models via a stick-breaking construction of Sethuraman. Although Bayesian
nonparametrics offers a powerful approach to construct a prior that avoids the
need to specify the model size/complexity explicitly, an exact inference is
often intractab... | computer science |
5,426 | Fast SVM-based Feature Elimination Utilizing Data Radius, Hard-Margin,
Soft-Margin | stat.ML | Margin maximization in the hard-margin sense, proposed as feature elimination
criterion by the MFE-LO method, is combined here with data radius utilization
to further aim to lower generalization error, as several published bounds and
bound-related formulations pertaining to lowering misclassification risk (or
error) pe... | computer science |
5,427 | Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear
Multivariate Regression and Granger Causality | stat.ML | We propose a general matrix-valued multiple kernel learning framework for
high-dimensional nonlinear multivariate regression problems. This framework
allows a broad class of mixed norm regularizers, including those that induce
sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel
Hilbert Spaces. W... | computer science |
5,428 | Leveraging Side Observations in Stochastic Bandits | cs.LG | This paper considers stochastic bandits with side observations, a model that
accounts for both the exploration/exploitation dilemma and relationships
between arms. In this setting, after pulling an arm i, the decision maker also
observes the rewards for some other actions related to i. We will see that this
model is su... | computer science |
5,429 | Variational Dual-Tree Framework for Large-Scale Transition Matrix
Approximation | cs.LG | In recent years, non-parametric methods utilizing random walks on graphs have
been used to solve a wide range of machine learning problems, but in their
simplest form they do not scale well due to the quadratic complexity. In this
paper, a new dual-tree based variational approach for approximating the
transition matrix... | computer science |
5,430 | Learning to Rank With Bregman Divergences and Monotone Retargeting | cs.LG | This paper introduces a novel approach for learning to rank (LETOR) based on
the notion of monotone retargeting. It involves minimizing a divergence between
all monotonic increasing transformations of the training scores and a
parameterized prediction function. The minimization is both over the
transformations as well ... | computer science |
5,431 | Exploiting compositionality to explore a large space of model structures | cs.LG | The recent proliferation of richly structured probabilistic models raises the
question of how to automatically determine an appropriate model for a dataset.
We investigate this question for a space of matrix decomposition models which
can express a variety of widely used models from unsupervised learning. To
enable mod... | computer science |
5,432 | Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits | cs.LG | We present and prove properties of a new offline policy evaluator for an
exploration learning setting which is superior to previous evaluators. In
particular, it simultaneously and correctly incorporates techniques from
importance weighting, doubly robust evaluation, and nonstationary policy
evaluation approaches. In a... | computer science |
5,433 | Lifted Relational Variational Inference | cs.LG | Hybrid continuous-discrete models naturally represent many real-world
applications in robotics, finance, and environmental engineering. Inference
with large-scale models is challenging because relational structures
deteriorate rapidly during inference with observations. The main contribution
of this paper is an efficie... | computer science |
5,434 | Active Imitation Learning via Reduction to I.I.D. Active Learning | cs.LG | In standard passive imitation learning, the goal is to learn a target policy
by passively observing full execution trajectories of it. Unfortunately,
generating such trajectories can require substantial expert effort and be
impractical in some cases. In this paper, we consider active imitation learning
with the goal of... | computer science |
5,435 | Tightening Fractional Covering Upper Bounds on the Partition Function
for High-Order Region Graphs | cs.LG | In this paper we present a new approach for tightening upper bounds on the
partition function. Our upper bounds are based on fractional covering bounds on
the entropy function, and result in a concave program to compute these bounds
and a convex program to tighten them. To solve these programs effectively for
general r... | computer science |
5,436 | A Spectral Algorithm for Latent Junction Trees | cs.LG | Latent variable models are an elegant framework for capturing rich
probabilistic dependencies in many applications. However, current approaches
typically parametrize these models using conditional probability tables, and
learning relies predominantly on local search heuristics such as Expectation
Maximization. Using te... | computer science |
5,437 | Unsupervised Joint Alignment and Clustering using Bayesian
Nonparametrics | cs.LG | Joint alignment of a collection of functions is the process of independently
transforming the functions so that they appear more similar to each other.
Typically, such unsupervised alignment algorithms fail when presented with
complex data sets arising from multiple modalities or make restrictive
assumptions about the ... | computer science |
5,438 | Sparse Q-learning with Mirror Descent | cs.LG | This paper explores a new framework for reinforcement learning based on
online convex optimization, in particular mirror descent and related
algorithms. Mirror descent can be viewed as an enhanced gradient method,
particularly suited to minimization of convex functions in highdimensional
spaces. Unlike traditional grad... | computer science |
5,439 | Value Function Approximation in Noisy Environments Using Locally
Smoothed Regularized Approximate Linear Programs | cs.LG | Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear
Programming (RALP) could produce value functions and policies which compared
favorably to established linear value function approximation techniques like
LSPI. RALP's success primarily stems from the ability to solve the feature
selection and va... | computer science |
5,440 | Fast Exact Inference for Recursive Cardinality Models | cs.LG | Cardinality potentials are a generally useful class of high order potential
that affect probabilities based on how many of D binary variables are active.
Maximum a posteriori (MAP) inference for cardinality potential models is
well-understood, with efficient computations taking O(DlogD) time. Yet
efficient marginalizat... | computer science |
5,441 | Latent Composite Likelihood Learning for the Structured Canonical
Correlation Model | stat.ML | Latent variable models are used to estimate variables of interest quantities
which are observable only up to some measurement error. In many studies, such
variables are known but not precisely quantifiable (such as "job satisfaction"
in social sciences and marketing, "analytical ability" in educational testing,
or "inf... | computer science |
5,442 | Active Learning with Distributional Estimates | cs.LG | Active Learning (AL) is increasingly important in a broad range of
applications. Two main AL principles to obtain accurate classification with few
labeled data are refinement of the current decision boundary and exploration of
poorly sampled regions. In this paper we derive a novel AL scheme that balances
these two pri... | computer science |
5,443 | Fast Graph Construction Using Auction Algorithm | cs.LG | In practical machine learning systems, graph based data representation has
been widely used in various learning paradigms, ranging from unsupervised
clustering to supervised classification. Besides those applications with
natural graph or network structure data, such as social network analysis and
relational learning, ... | computer science |
5,444 | LSBN: A Large-Scale Bayesian Structure Learning Framework for Model
Averaging | cs.LG | The motivation for this paper is to apply Bayesian structure learning using
Model Averaging in large-scale networks. Currently, Bayesian model averaging
algorithm is applicable to networks with only tens of variables, restrained by
its super-exponential complexity. We present a novel framework, called
LSBN(Large-Scale ... | computer science |
5,445 | Matrix reconstruction with the local max norm | stat.ML | We introduce a new family of matrix norms, the "local max" norms,
generalizing existing methods such as the max norm, the trace norm (nuclear
norm), and the weighted or smoothed weighted trace norms, which have been
extensively used in the literature as regularizers for matrix reconstruction
problems. We show that this... | computer science |
5,446 | Content-boosted Matrix Factorization Techniques for Recommender Systems | stat.ML | Many businesses are using recommender systems for marketing outreach.
Recommendation algorithms can be either based on content or driven by
collaborative filtering. We study different ways to incorporate content
information directly into the matrix factorization approach of collaborative
filtering. These content-booste... | computer science |
5,447 | Supervised Learning with Similarity Functions | cs.LG | We address the problem of general supervised learning when data can only be
accessed through an (indefinite) similarity function between data points.
Existing work on learning with indefinite kernels has concentrated solely on
binary/multi-class classification problems. We propose a model that is generic
enough to hand... | computer science |
5,448 | Initialization of Self-Organizing Maps: Principal Components Versus
Random Initialization. A Case Study | stat.ML | The performance of the Self-Organizing Map (SOM) algorithm is dependent on
the initial weights of the map. The different initialization methods can
broadly be classified into random and data analysis based initialization
approach. In this paper, the performance of random initialization (RI) approach
is compared to that... | computer science |
5,449 | Reducing statistical time-series problems to binary classification | cs.LG | We show how binary classification methods developed to work on i.i.d. data
can be used for solving statistical problems that are seemingly unrelated to
classification and concern highly-dependent time series. Specifically, the
problems of time-series clustering, homogeneity testing and the three-sample
problem are addr... | computer science |
5,450 | Nested Hierarchical Dirichlet Processes | stat.ML | We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical
topic modeling. The nHDP is a generalization of the nested Chinese restaurant
process (nCRP) that allows each word to follow its own path to a topic node
according to a document-specific distribution on a shared tree. This alleviates
the rigid, ... | computer science |
5,451 | Estimating Vector Fields on Manifolds and the Embedding of Directed
Graphs | stat.ML | This paper considers the problem of embedding directed graphs in Euclidean
space while retaining directional information. We model a directed graph as a
finite set of observations from a diffusion on a manifold endowed with a vector
field. This is the first generative model of its kind for directed graphs. We
introduce... | computer science |
5,452 | Improved graph Laplacian via geometric self-consistency | stat.ML | We address the problem of setting the kernel bandwidth used by Manifold
Learning algorithms to construct the graph Laplacian. Exploiting the connection
between manifold geometry, represented by the Riemannian metric, and the
Laplace-Beltrami operator, we set the bandwidth by optimizing the Laplacian's
ability to preser... | computer science |
5,453 | Feature Selection for Linear SVM with Provable Guarantees | stat.ML | We give two provably accurate feature-selection techniques for the linear
SVM. The algorithms run in deterministic and randomized time respectively. Our
algorithms can be used in an unsupervised or supervised setting. The supervised
approach is based on sampling features from support vectors. We prove that the
margin i... | computer science |
5,454 | Transductive Learning for Multi-Task Copula Processes | cs.LG | We tackle the problem of multi-task learning with copula process.
Multivariable prediction in spatial and spatial-temporal processes such as
natural resource estimation and pollution monitoring have been typically
addressed using techniques based on Gaussian processes and co-Kriging. While
the Gaussian prior assumption... | computer science |
5,455 | Iterative Neural Autoregressive Distribution Estimator (NADE-k) | stat.ML | Training of the neural autoregressive density estimator (NADE) can be viewed
as doing one step of probabilistic inference on missing values in data. We
propose a new model that extends this inference scheme to multiple steps,
arguing that it is easier to learn to improve a reconstruction in $k$ steps
rather than to lea... | computer science |
5,456 | Separable Cosparse Analysis Operator Learning | cs.LG | The ability of having a sparse representation for a certain class of signals
has many applications in data analysis, image processing, and other research
fields. Among sparse representations, the cosparse analysis model has recently
gained increasing interest. Many signals exhibit a multidimensional structure,
e.g. ima... | computer science |
5,457 | Variational inference of latent state sequences using Recurrent Networks | stat.ML | Recent advances in the estimation of deep directed graphical models and
recurrent networks let us contribute to the removal of a blind spot in the area
of probabilistc modelling of time series. The proposed methods i) can infer
distributed latent state-space trajectories with nonlinear transitions, ii)
scale to large d... | computer science |
5,458 | Model-based Reinforcement Learning and the Eluder Dimension | stat.ML | We consider the problem of learning to optimize an unknown Markov decision
process (MDP). We show that, if the MDP can be parameterized within some known
function class, we can obtain regret bounds that scale with the dimensionality,
rather than cardinality, of the system. We characterize this dependence
explicitly as ... | computer science |
5,459 | Reducing the Effects of Detrimental Instances | stat.ML | Not all instances in a data set are equally beneficial for inducing a model
of the data. Some instances (such as outliers or noise) can be detrimental.
However, at least initially, the instances in a data set are generally
considered equally in machine learning algorithms. Many current approaches for
handling noisy and... | computer science |
5,460 | Exploring Algorithmic Limits of Matrix Rank Minimization under Affine
Constraints | cs.LG | Many applications require recovering a matrix of minimal rank within an
affine constraint set, with matrix completion a notable special case. Because
the problem is NP-hard in general, it is common to replace the matrix rank with
the nuclear norm, which acts as a convenient convex surrogate. While elegant
theoretical c... | computer science |
5,461 | Predictive Entropy Search for Efficient Global Optimization of Black-box
Functions | stat.ML | We propose a novel information-theoretic approach for Bayesian optimization
called Predictive Entropy Search (PES). At each iteration, PES selects the next
evaluation point that maximizes the expected information gained with respect to
the global maximum. PES codifies this intractable acquisition function in terms
of t... | computer science |
5,462 | Equivalence of Learning Algorithms | cs.LG | The purpose of this paper is to introduce a concept of equivalence between
machine learning algorithms. We define two notions of algorithmic equivalence,
namely, weak and strong equivalence. These notions are of paramount importance
for identifying when learning prop erties from one learning algorithm can be
transferre... | computer science |
5,463 | Generative Adversarial Networks | stat.ML | We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train two models: a generative
model G that captures the data distribution, and a discriminative model D that
estimates the probability that a sample came from the training data rather than
G. The training... | computer science |
5,464 | Mondrian Forests: Efficient Online Random Forests | stat.ML | Ensembles of randomized decision trees, usually referred to as random
forests, are widely used for classification and regression tasks in machine
learning and statistics. Random forests achieve competitive predictive
performance and are computationally efficient to train and test, making them
excellent candidates for r... | computer science |
5,465 | Online Optimization for Large-Scale Max-Norm Regularization | stat.ML | Max-norm regularizer has been extensively studied in the last decade as it
promotes an effective low-rank estimation for the underlying data. However,
such max-norm regularized problems are typically formulated and solved in a
batch manner, which prevents it from processing big data due to possible memory
budget. In th... | computer science |
5,466 | Scheduled denoising autoencoders | cs.LG | We present a representation learning method that learns features at multiple
different levels of scale. Working within the unsupervised framework of
denoising autoencoders, we observe that when the input is heavily corrupted
during training, the network tends to learn coarse-grained features, whereas
when the input is ... | computer science |
5,467 | Smoothed Gradients for Stochastic Variational Inference | stat.ML | Stochastic variational inference (SVI) lets us scale up Bayesian computation
to massive data. It uses stochastic optimization to fit a variational
distribution, following easy-to-compute noisy natural gradients. As with most
traditional stochastic optimization methods, SVI takes precautions to use
unbiased stochastic g... | computer science |
5,468 | From Stochastic Mixability to Fast Rates | cs.LG | Empirical risk minimization (ERM) is a fundamental learning rule for
statistical learning problems where the data is generated according to some
unknown distribution $\mathsf{P}$ and returns a hypothesis $f$ chosen from a
fixed class $\mathcal{F}$ with small loss $\ell$. In the parametric setting,
depending upon $(\ell... | computer science |
5,469 | Simultaneous Model Selection and Optimization through Parameter-free
Stochastic Learning | cs.LG | Stochastic gradient descent algorithms for training linear and kernel
predictors are gaining more and more importance, thanks to their scalability.
While various methods have been proposed to speed up their convergence, the
model selection phase is often ignored. In fact, in theoretical works most of
the time assumptio... | computer science |
5,470 | An Incremental Reseeding Strategy for Clustering | stat.ML | In this work we propose a simple and easily parallelizable algorithm for
multiway graph partitioning. The algorithm alternates between three basic
components: diffusing seed vertices over the graph, thresholding the diffused
seeds, and then randomly reseeding the thresholded clusters. We demonstrate
experimentally that... | computer science |
5,471 | Freeze-Thaw Bayesian Optimization | stat.ML | In this paper we develop a dynamic form of Bayesian optimization for machine
learning models with the goal of rapidly finding good hyperparameter settings.
Our method uses the partial information gained during the training of a machine
learning model in order to decide whether to pause training and start a new
model, o... | computer science |
5,472 | Personalized Medical Treatments Using Novel Reinforcement Learning
Algorithms | cs.LG | In both the fields of computer science and medicine there is very strong
interest in developing personalized treatment policies for patients who have
variable responses to treatments. In particular, I aim to find an optimal
personalized treatment policy which is a non-deterministic function of the
patient specific cova... | computer science |
5,473 | Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy
Posterior Sampling Algorithm | cs.LG | We study Bayesian optimal control of a general class of smoothly
parameterized Markov decision problems. Since computing the optimal control is
computationally expensive, we design an algorithm that trades off performance
for computational efficiency. The algorithm is a lazy posterior sampling method
that maintains a d... | computer science |
5,474 | Distributed Stochastic Optimization of the Regularized Risk | stat.ML | Many machine learning algorithms minimize a regularized risk, and stochastic
optimization is widely used for this task. When working with massive data, it
is desirable to perform stochastic optimization in parallel. Unfortunately,
many existing stochastic optimization algorithms cannot be parallelized
efficiently. In t... | computer science |
5,475 | Scalable Latent Tree Model and its Application to Health Analytics | cs.LG | We present an integrated approach to structure and parameter estimation in
latent tree graphical models, where some nodes are hidden. Our overall approach
follows a "divide-and-conquer" strategy that learns models over small groups of
variables and iteratively merges into a global solution. The structure learning
invol... | computer science |
5,476 | An Entropy Search Portfolio for Bayesian Optimization | stat.ML | Bayesian optimization is a sample-efficient method for black-box global
optimization. How- ever, the performance of a Bayesian optimization method very
much depends on its exploration strategy, i.e. the choice of acquisition
function, and it is not clear a priori which choice will result in superior
performance. While ... | computer science |
5,477 | The Sample Complexity of Learning Linear Predictors with the Squared
Loss | cs.LG | In this short note, we provide tight sample complexity bounds for learning
linear predictors with respect to the squared loss. Our focus is on an agnostic
setting, where no assumptions are made on the data distribution. This contrasts
with standard results in the literature, which either make distributional
assumptions... | computer science |
5,478 | Generalized Dantzig Selector: Application to the k-support norm | stat.ML | We propose a Generalized Dantzig Selector (GDS) for linear models, in which
any norm encoding the parameter structure can be leveraged for estimation. We
investigate both computational and statistical aspects of the GDS. Based on
conjugate proximal operator, a flexible inexact ADMM framework is designed for
solving GDS... | computer science |
5,479 | Semi-Supervised Learning with Deep Generative Models | cs.LG | The ever-increasing size of modern data sets combined with the difficulty of
obtaining label information has made semi-supervised learning one of the
problems of significant practical importance in modern data analysis. We
revisit the approach to semi-supervised learning with generative models and
develop new models th... | computer science |
5,480 | Predicting the Future Behavior of a Time-Varying Probability
Distribution | stat.ML | We study the problem of predicting the future, though only in the
probabilistic sense of estimating a future state of a time-varying probability
distribution. This is not only an interesting academic problem, but solving
this extrapolation problem also has many practical application, e.g. for
training classifiers that ... | computer science |
5,481 | Noise-adaptive Margin-based Active Learning and Lower Bounds under
Tsybakov Noise Condition | stat.ML | We present a simple noise-robust margin-based active learning algorithm to
find homogeneous (passing the origin) linear separators and analyze its error
convergence when labels are corrupted by noise. We show that when the imposed
noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, and others
1999; Tsyb... | computer science |
5,482 | Divide-and-Conquer Learning by Anchoring a Conical Hull | stat.ML | We reduce a broad class of machine learning problems, usually addressed by EM
or sampling, to the problem of finding the $k$ extremal rays spanning the
conical hull of a data point set. These $k$ "anchors" lead to a global solution
and a more interpretable model that can even outperform EM and sampling on
generalizatio... | computer science |
5,483 | Reinforcement and Imitation Learning via Interactive No-Regret Learning | cs.LG | Recent work has demonstrated that problems-- particularly imitation learning
and structured prediction-- where a learner's predictions influence the
input-distribution it is tested on can be naturally addressed by an interactive
approach and analyzed using no-regret online learning. These approaches to
imitation learni... | computer science |
5,484 | When is it Better to Compare than to Score? | stat.ML | When eliciting judgements from humans for an unknown quantity, one often has
the choice of making direct-scoring (cardinal) or comparative (ordinal)
measurements. In this paper we study the relative merits of either choice,
providing empirical and theoretical guidelines for the selection of a
measurement scheme. We pro... | computer science |
5,485 | Online learning in MDPs with side information | cs.LG | We study online learning of finite Markov decision process (MDP) problems
when a side information vector is available. The problem is motivated by
applications such as clinical trials, recommendation systems, etc. Such
applications have an episodic structure, where each episode corresponds to a
patient/customer. Our ob... | computer science |
5,486 | Thompson Sampling for Learning Parameterized Markov Decision Processes | stat.ML | We consider reinforcement learning in parameterized Markov Decision Processes
(MDPs), where the parameterization may induce correlation across transition
probabilities or rewards. Consequently, observing a particular state transition
might yield useful information about other, unobserved, parts of the MDP. We
present a... | computer science |
5,487 | Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian
Process Hyper-Parameters | stat.ML | Bayesian optimisation has gained great popularity as a tool for optimising
the parameters of machine learning algorithms and models. Somewhat ironically,
setting up the hyper-parameters of Bayesian optimisation methods is notoriously
hard. While reasonable practical solutions have been advanced, they can often
fail to ... | computer science |
5,488 | Gradient-based kernel dimension reduction for supervised learning | stat.ML | This paper proposes a novel kernel approach to linear dimension reduction for
supervised learning. The purpose of the dimension reduction is to find
directions in the input space to explain the output as effectively as possible.
The proposed method uses an estimator for the gradient of regression function,
based on the... | computer science |
5,489 | Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under
High Mobility Conditions | cs.LG | A nonlinear channel estimator using complex Least Square Support Vector
Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long
Term Evolution (LTE) downlink under high mobility conditions. The estimation
algorithm makes use of the reference signals to estimate the total frequency
response of the ... | computer science |
5,490 | Trace Lasso: a trace norm regularization for correlated designs | cs.LG | Using the $\ell_1$-norm to regularize the estimation of the parameter vector
of a linear model leads to an unstable estimator when covariates are highly
correlated. In this paper, we introduce a new penalty function which takes into
account the correlation of the design matrix to stabilize the estimation. This
norm, ca... | computer science |
5,491 | Structured sparsity through convex optimization | cs.LG | Sparse estimation methods are aimed at using or obtaining parsimonious
representations of data or models. While naturally cast as a combinatorial
optimization problem, variable or feature selection admits a convex relaxation
through the regularization by the $\ell_1$-norm. In this paper, we consider
situations where we... | computer science |
5,492 | Reconstruction of sequential data with density models | cs.LG | We introduce the problem of reconstructing a sequence of multidimensional
real vectors where some of the data are missing. This problem contains
regression and mapping inversion as particular cases where the pattern of
missing data is independent of the sequence index. The problem is hard because
it involves possibly m... | computer science |
5,493 | Bias Plus Variance Decomposition for Survival Analysis Problems | cs.LG | Bias - variance decomposition of the expected error defined for regression
and classification problems is an important tool to study and compare different
algorithms, to find the best areas for their application. Here the
decomposition is introduced for the survival analysis problem. In our
experiments, we study bias -... | computer science |
5,494 | Learning Item Trees for Probabilistic Modelling of Implicit Feedback | cs.LG | User preferences for items can be inferred from either explicit feedback,
such as item ratings, or implicit feedback, such as rental histories. Research
in collaborative filtering has concentrated on explicit feedback, resulting in
the development of accurate and scalable models. However, since explicit
feedback is oft... | computer science |
5,495 | A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel
Learning | cs.LG | We consider the problem of simultaneously learning to linearly combine a very
large number of kernels and learn a good predictor based on the learnt kernel.
When the number of kernels $d$ to be combined is very large, multiple kernel
learning methods whose computational cost scales linearly in $d$ are
intractable. We p... | computer science |
5,496 | Variable Selection for Latent Dirichlet Allocation | cs.LG | In latent Dirichlet allocation (LDA), topics are multinomial distributions
over the entire vocabulary. However, the vocabulary usually contains many words
that are not relevant in forming the topics. We adopt a variable selection
method widely used in statistical modeling as a dimension reduction tool and
combine it wi... | computer science |
5,497 | Convex Relaxation for Combinatorial Penalties | stat.ML | In this paper, we propose an unifying view of several recently proposed
structured sparsity-inducing norms. We consider the situation of a model
simultaneously (a) penalized by a set- function de ned on the support of the
unknown parameter vector which represents prior knowledge on supports, and (b)
regularized in Lp-n... | computer science |
5,498 | Graph-based Learning with Unbalanced Clusters | stat.ML | Graph construction is a crucial step in spectral clustering (SC) and
graph-based semi-supervised learning (SSL). Spectral methods applied on
standard graphs such as full-RBF, $\epsilon$-graphs and $k$-NN graphs can lead
to poor performance in the presence of proximal and unbalanced data. This is
because spectral method... | computer science |
5,499 | Approximate Dynamic Programming By Minimizing Distributionally Robust
Bounds | stat.ML | Approximate dynamic programming is a popular method for solving large Markov
decision processes. This paper describes a new class of approximate dynamic
programming (ADP) methods- distributionally robust ADP-that address the curse
of dimensionality by minimizing a pessimistic bound on the policy loss. This
approach tur... | computer science |
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