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2,300 | Pooling homogeneous ensembles to build heterogeneous ensembles | cs.LG | In ensemble methods, the outputs of a collection of diverse classifiers are
combined in the expectation that the global prediction be more accurate than
the individual ones. Heterogeneous ensembles consist of predictors of different
types, which are likely to have different biases. If these biases are
complementary, th... | computer science |
2,301 | Learning to Make Predictions on Graphs with Autoencoders | cs.LG | We examine two fundamental tasks associated with graph representation
learning: link prediction and semi-supervised node classification. We present a
densely connected autoencoder architecture capable of learning a joint
representation of both local graph structure and available external node
features for the multi-tas... | computer science |
2,302 | Learning Optimal Policies from Observational Data | cs.AI | Choosing optimal (or at least better) policies is an important problem in
domains from medicine to education to finance and many others. One approach to
this problem is through controlled experiments/trials - but controlled
experiments are expensive. Hence it is important to choose the best policies on
the basis of obs... | computer science |
2,303 | Learning with Abandonment | stat.ML | Consider a platform that wants to learn a personalized policy for each user,
but the platform faces the risk of a user abandoning the platform if she is
dissatisfied with the actions of the platform. For example, a platform is
interested in personalizing the number of newsletters it sends, but faces the
risk that the u... | computer science |
2,304 | DID: Distributed Incremental Block Coordinate Descent for Nonnegative
Matrix Factorization | cs.LG | Nonnegative matrix factorization (NMF) has attracted much attention in the
last decade as a dimension reduction method in many applications. Due to the
explosion in the size of data, naturally the samples are collected and stored
distributively in local computational nodes. Thus, there is a growing need to
develop algo... | computer science |
2,305 | Variance Reduction Methods for Sublinear Reinforcement Learning | cs.AI | This work considers the problem of provably optimal reinforcement learning
for (episodic) finite horizon MDPs, i.e. how an agent learns to maximize
his/her (long term) reward in an uncertain environment. The main contribution
is in providing a novel algorithm --- Variance-reduced Upper Confidence
Q-learning (vUCQ) --- ... | computer science |
2,306 | Modeling reverse thinking for machine learning | cs.LG | Human inertial thinking schemes can be formed through learning, which are
then applied to quickly solve similar problems later. However, when problems
are significantly different, inertial thinking generally presents the solutions
that are definitely imperfect. In such cases, people will apply creative
thinking, such a... | computer science |
2,307 | Vector Quantization as Sparse Least Square Optimization | cs.LG | Vector quantization aims to form new vectors/matrices with shared values
close to the original. It could compress data with acceptable information loss,
and could be of great usefulness in areas like Image Processing, Pattern
Recognition and Machine Learning. In recent years, the importance of
quantization has been soa... | computer science |
2,308 | Hierarchical Imitation and Reinforcement Learning | cs.LG | We study the problem of learning policies over long time horizons. We present
a framework that leverages and integrates two key concepts. First, we utilize
hierarchical policy classes that enable planning over different time scales,
i.e., the high level planner proposes a sequence of subgoals for the low level
planner ... | computer science |
2,309 | Understanding the Loss Surface of Neural Networks for Binary
Classification | cs.LG | It is widely conjectured that the reason that training algorithms for neural
networks are successful because all local minima lead to similar performance,
for example, see (LeCun et al., 2015, Choromanska et al., 2015, Dauphin et al.,
2014). Performance is typically measured in terms of two metrics: training
performanc... | computer science |
2,310 | On the Power of Over-parametrization in Neural Networks with Quadratic
Activation | cs.LG | We provide new theoretical insights on why over-parametrization is effective
in learning neural networks. For a $k$ hidden node shallow network with
quadratic activation and $n$ training data points, we show as long as $ k \ge
\sqrt{2n}$, over-parametrization enables local search algorithms to find a
\emph{globally} op... | computer science |
2,311 | Improving Multi-Step Traffic Flow Prediction | cs.AI | In its simplest form, the traffic flow prediction problem is restricted to
predicting a single time-step into the future. Multi-step traffic flow
prediction extends this set-up to the case where predicting multiple time-steps
into the future based on some finite history is of interest. This problem is
significantly mor... | computer science |
2,312 | On Discrimination Discovery and Removal in Ranked Data using Causal
Graph | cs.LG | Predictive models learned from historical data are widely used to help
companies and organizations make decisions. However, they may digitally
unfairly treat unwanted groups, raising concerns about fairness and
discrimination. In this paper, we study the fairness-aware ranking problem
which aims to discover discriminat... | computer science |
2,313 | A Multi-Objective Deep Reinforcement Learning Framework | cs.LG | This paper presents a new multi-objective deep reinforcement learning (MODRL)
framework based on deep Q-networks. We propose linear and non-linear methods to
develop the MODRL framework that includes both single-policy and multi-policy
strategies. The experimental results on a deep sea treasure environment
indicate tha... | computer science |
2,314 | Deep Neural Network Compression with Single and Multiple Level
Quantization | cs.LG | Network quantization is an effective solution to compress deep neural
networks for practical usage. Existing network quantization methods cannot
sufficiently exploit the depth information to generate low-bit compressed
network. In this paper, we propose two novel network quantization approaches,
single-level network qu... | computer science |
2,315 | Hierarchical Reinforcement Learning: Approximating Optimal Discounted
TSP Using Local Policies | cs.LG | In this work, we provide theoretical guarantees for reward decomposition in
deterministic MDPs. Reward decomposition is a special case of Hierarchical
Reinforcement Learning, that allows one to learn many policies in parallel and
combine them into a composite solution. Our approach builds on mapping this
problem into a... | computer science |
2,316 | Soft-Robust Actor-Critic Policy-Gradient | cs.LG | Robust Reinforcement Learning aims to derive an optimal behavior that
accounts for model uncertainty in dynamical systems. However, previous studies
have shown that by considering the worst case scenario, robust policies can be
overly conservative. Our \textit{soft-robust} framework is an attempt to
overcome this issue... | computer science |
2,317 | Imitation Learning with Concurrent Actions in 3D Games | cs.AI | In this work we describe a novel deep reinforcement learning neural network
architecture that allows multiple actions to be selected at every time-step.
Multi-action policies allows complex behaviors to be learnt that are otherwise
hard to achieve when using single action selection techniques. This work
describes an al... | computer science |
2,318 | Setting up a Reinforcement Learning Task with a Real-World Robot | cs.LG | Reinforcement learning is a promising approach to developing hard-to-engineer
adaptive solutions for complex and diverse robotic tasks. However, learning
with real-world robots is often unreliable and difficult, which resulted in
their low adoption in reinforcement learning research. This difficulty is
worsened by the ... | computer science |
2,319 | Regret Bounds for Opportunistic Channel Access | stat.ML | We consider the task of opportunistic channel access in a primary system
composed of independent Gilbert-Elliot channels where the secondary (or
opportunistic) user does not dispose of a priori information regarding the
statistical characteristics of the system. It is shown that this problem may be
cast into the framew... | computer science |
2,320 | Feature Selection with Conjunctions of Decision Stumps and Learning from
Microarray Data | cs.LG | One of the objectives of designing feature selection learning algorithms is
to obtain classifiers that depend on a small number of attributes and have
verifiable future performance guarantees. There are few, if any, approaches
that successfully address the two goals simultaneously. Performance guarantees
become crucial... | computer science |
2,321 | On the Estimation of Coherence | stat.ML | Low-rank matrix approximations are often used to help scale standard machine
learning algorithms to large-scale problems. Recently, matrix coherence has
been used to characterize the ability to extract global information from a
subset of matrix entries in the context of these low-rank approximations and
other sampling-... | computer science |
2,322 | Rapid Feature Learning with Stacked Linear Denoisers | cs.LG | We investigate unsupervised pre-training of deep architectures as feature
generators for "shallow" classifiers. Stacked Denoising Autoencoders (SdA),
when used as feature pre-processing tools for SVM classification, can lead to
significant improvements in accuracy - however, at the price of a substantial
increase in co... | computer science |
2,323 | Efficient Optimal Learning for Contextual Bandits | cs.LG | We address the problem of learning in an online setting where the learner
repeatedly observes features, selects among a set of actions, and receives
reward for the action taken. We provide the first efficient algorithm with an
optimal regret. Our algorithm uses a cost sensitive classification learner as
an oracle and h... | computer science |
2,324 | A review and comparison of strategies for multi-step ahead time series
forecasting based on the NN5 forecasting competition | stat.ML | Multi-step ahead forecasting is still an open challenge in time series
forecasting. Several approaches that deal with this complex problem have been
proposed in the literature but an extensive comparison on a large number of
tasks is still missing. This paper aims to fill this gap by reviewing existing
strategies for m... | computer science |
2,325 | Overlapping Mixtures of Gaussian Processes for the Data Association
Problem | stat.ML | In this work we introduce a mixture of GPs to address the data association
problem, i.e. to label a group of observations according to the sources that
generated them. Unlike several previously proposed GP mixtures, the novel
mixture has the distinct characteristic of using no gating function to
determine the associati... | computer science |
2,326 | Improving parameter learning of Bayesian nets from incomplete data | cs.LG | This paper addresses the estimation of parameters of a Bayesian network from
incomplete data. The task is usually tackled by running the
Expectation-Maximization (EM) algorithm several times in order to obtain a high
log-likelihood estimate. We argue that choosing the maximum log-likelihood
estimate (as well as the max... | computer science |
2,327 | UPAL: Unbiased Pool Based Active Learning | stat.ML | In this paper we address the problem of pool based active learning, and
provide an algorithm, called UPAL, that works by minimizing the unbiased
estimator of the risk of a hypothesis in a given hypothesis space. For the
space of linear classifiers and the squared loss we show that UPAL is
equivalent to an exponentially... | computer science |
2,328 | Learning High-Dimensional Mixtures of Graphical Models | stat.ML | We consider unsupervised estimation of mixtures of discrete graphical models,
where the class variable corresponding to the mixture components is hidden and
each mixture component over the observed variables can have a potentially
different Markov graph structure and parameters. We propose a novel approach
for estimati... | computer science |
2,329 | An Online Learning-based Framework for Tracking | cs.LG | We study the tracking problem, namely, estimating the hidden state of an
object over time, from unreliable and noisy measurements. The standard
framework for the tracking problem is the generative framework, which is the
basis of solutions such as the Bayesian algorithm and its approximation, the
particle filters. Howe... | computer science |
2,330 | Real-Time Scheduling via Reinforcement Learning | cs.LG | Cyber-physical systems, such as mobile robots, must respond adaptively to
dynamic operating conditions. Effective operation of these systems requires
that sensing and actuation tasks are performed in a timely manner.
Additionally, execution of mission specific tasks such as imaging a room must
be balanced against the n... | computer science |
2,331 | Causal Conclusions that Flip Repeatedly and Their Justification | cs.LG | Over the past two decades, several consistent procedures have been designed
to infer causal conclusions from observational data. We prove that if the true
causal network might be an arbitrary, linear Gaussian network or a discrete
Bayes network, then every unambiguous causal conclusion produced by a
consistent method f... | computer science |
2,332 | Irregular-Time Bayesian Networks | cs.AI | In many fields observations are performed irregularly along time, due to
either measurement limitations or lack of a constant immanent rate. While
discrete-time Markov models (as Dynamic Bayesian Networks) introduce either
inefficient computation or an information loss to reasoning about such
processes, continuous-time... | computer science |
2,333 | Modeling Events with Cascades of Poisson Processes | cs.LG | We present a probabilistic model of events in continuous time in which each
event triggers a Poisson process of successor events. The ensemble of observed
events is thereby modeled as a superposition of Poisson processes. Efficient
inference is feasible under this model with an EM algorithm. Moreover, the EM
algorithm ... | computer science |
2,334 | Bayesian Inference in Monte-Carlo Tree Search | cs.LG | Monte-Carlo Tree Search (MCTS) methods are drawing great interest after
yielding breakthrough results in computer Go. This paper proposes a Bayesian
approach to MCTS that is inspired by distributionfree approaches such as UCT
[13], yet significantly differs in important respects. The Bayesian framework
allows potential... | computer science |
2,335 | Primal View on Belief Propagation | cs.LG | It is known that fixed points of loopy belief propagation (BP) correspond to
stationary points of the Bethe variational problem, where we minimize the Bethe
free energy subject to normalization and marginalization constraints.
Unfortunately, this does not entirely explain BP because BP is a dual rather
than primal algo... | computer science |
2,336 | Modeling Multiple Annotator Expertise in the Semi-Supervised Learning
Scenario | cs.LG | Learning algorithms normally assume that there is at most one annotation or
label per data point. However, in some scenarios, such as medical diagnosis and
on-line collaboration,multiple annotations may be available. In either case,
obtaining labels for data points can be expensive and time-consuming (in some
circumsta... | computer science |
2,337 | A Convex Formulation for Learning Task Relationships in Multi-Task
Learning | cs.LG | Multi-task learning is a learning paradigm which seeks to improve the
generalization performance of a learning task with the help of some other
related tasks. In this paper, we propose a regularization formulation for
learning the relationships between tasks in multi-task learning. This
formulation can be viewed as a n... | computer science |
2,338 | Learning Feature Hierarchies with Centered Deep Boltzmann Machines | stat.ML | Deep Boltzmann machines are in principle powerful models for extracting the
hierarchical structure of data. Unfortunately, attempts to train layers jointly
(without greedy layer-wise pretraining) have been largely unsuccessful. We
propose a modification of the learning algorithm that initially recenters the
output of t... | computer science |
2,339 | Transforming Graph Representations for Statistical Relational Learning | stat.ML | Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we exami... | computer science |
2,340 | The threshold EM algorithm for parameter learning in bayesian network
with incomplete data | cs.AI | Bayesian networks (BN) are used in a big range of applications but they have
one issue concerning parameter learning. In real application, training data are
always incomplete or some nodes are hidden. To deal with this problem many
learning parameter algorithms are suggested foreground EM, Gibbs sampling and
RBE algori... | computer science |
2,341 | Interpolating Conditional Density Trees | cs.LG | Joint distributions over many variables are frequently modeled by decomposing
them into products of simpler, lower-dimensional conditional distributions,
such as in sparsely connected Bayesian networks. However, automatically
learning such models can be very computationally expensive when there are many
datapoints and ... | computer science |
2,342 | Discriminative Probabilistic Models for Relational Data | cs.LG | In many supervised learning tasks, the entities to be labeled are related to
each other in complex ways and their labels are not independent. For example,
in hypertext classification, the labels of linked pages are highly correlated.
A standard approach is to classify each entity independently, ignoring the
correlation... | computer science |
2,343 | IPF for Discrete Chain Factor Graphs | cs.LG | Iterative Proportional Fitting (IPF), combined with EM, is commonly used as
an algorithm for likelihood maximization in undirected graphical models. In
this paper, we present two iterative algorithms that generalize upon IPF. The
first one is for likelihood maximization in discrete chain factor graphs, which
we define ... | computer science |
2,344 | Automated Variational Inference in Probabilistic Programming | stat.ML | We present a new algorithm for approximate inference in probabilistic
programs, based on a stochastic gradient for variational programs. This method
is efficient without restrictions on the probabilistic program; it is
particularly practical for distributions which are not analytically tractable,
including highly struc... | computer science |
2,345 | Conditions Under Which Conditional Independence and Scoring Methods Lead
to Identical Selection of Bayesian Network Models | cs.AI | It is often stated in papers tackling the task of inferring Bayesian network
structures from data that there are these two distinct approaches: (i) Apply
conditional independence tests when testing for the presence or otherwise of
edges; (ii) Search the model space using a scoring metric. Here I argue that
for complete... | computer science |
2,346 | Learning the Dimensionality of Hidden Variables | cs.LG | A serious problem in learning probabilistic models is the presence of hidden
variables. These variables are not observed, yet interact with several of the
observed variables. Detecting hidden variables poses two problems: determining
the relations to other variables in the model and determining the number of
states of ... | computer science |
2,347 | Multivariate Information Bottleneck | cs.LG | The Information bottleneck method is an unsupervised non-parametric data
organization technique. Given a joint distribution P(A,B), this method
constructs a new variable T that extracts partitions, or clusters, over the
values of A that are informative about B. The information bottleneck has
already been applied to doc... | computer science |
2,348 | Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures | cs.LG | A novel method for estimating Bayesian network (BN) parameters from data is
presented which provides improved performance on test data. Previous research
has shown the value of representing conditional probability distributions
(CPDs) via neural networks(Neal 1992), noisy-OR gates (Neal 1992, Diez 1993)and
decision tre... | computer science |
2,349 | Improved learning of Bayesian networks | cs.LG | The search space of Bayesian Network structures is usually defined as Acyclic
Directed Graphs (DAGs) and the search is done by local transformations of DAGs.
But the space of Bayesian Networks is ordered by DAG Markov model inclusion and
it is natural to consider that a good search policy should take this into
account.... | computer science |
2,350 | Maximum Likelihood Bounded Tree-Width Markov Networks | cs.LG | Chow and Liu (1968) studied the problem of learning a maximumlikelihood
Markov tree. We generalize their work to more complexMarkov networks by
considering the problem of learning a maximumlikelihood Markov network of
bounded complexity. We discuss howtree-width is in many ways the appropriate
measure of complexity and... | computer science |
2,351 | The Optimal Reward Baseline for Gradient-Based Reinforcement Learning | cs.LG | There exist a number of reinforcement learning algorithms which learnby
climbing the gradient of expected reward. Their long-runconvergence has been
proved, even in partially observableenvironments with non-deterministic
actions, and without the need fora system model. However, the variance of the
gradient estimator ha... | computer science |
2,352 | Statistical Modeling in Continuous Speech Recognition (CSR)(Invited
Talk) | cs.LG | Automatic continuous speech recognition (CSR) is sufficiently mature that a
variety of real world applications are now possible including large vocabulary
transcription and interactive spoken dialogues. This paper reviews the
evolution of the statistical modelling techniques which underlie current-day
systems, specific... | computer science |
2,353 | Dynamic Bayesian Multinets | cs.LG | In this work, dynamic Bayesian multinets are introduced where a Markov chain
state at time t determines conditional independence patterns between random
variables lying within a local time window surrounding t. It is shown how
information-theoretic criterion functions can be used to induce sparse,
discriminative, and c... | computer science |
2,354 | Being Bayesian about Network Structure | cs.LG | In many domains, we are interested in analyzing the structure of the
underlying distribution, e.g., whether one variable is a direct parent of the
other. Bayesian model-selection attempts to find the MAP model and use its
structure to answer these questions. However, when the amount of available data
is modest, there m... | computer science |
2,355 | Gaussian Process Networks | cs.AI | In this paper we address the problem of learning the structure of a Bayesian
network in domains with continuous variables. This task requires a procedure
for comparing different candidate structures. In the Bayesian framework, this
is done by evaluating the {em marginal likelihood/} of the data given a
candidate struct... | computer science |
2,356 | Tractable Bayesian Learning of Tree Belief Networks | cs.LG | In this paper we present decomposable priors, a family of priors over
structure and parameters of tree belief nets for which Bayesian learning with
complete observations is tractable, in the sense that the posterior is also
decomposable and can be completely determined analytically in polynomial time.
This follows from... | computer science |
2,357 | Adaptive Importance Sampling for Estimation in Structured Domains | cs.AI | Sampling is an important tool for estimating large, complex sums and
integrals over high dimensional spaces. For instance, important sampling has
been used as an alternative to exact methods for inference in belief networks.
Ideally, we want to have a sampling distribution that provides optimal-variance
estimators. In ... | computer science |
2,358 | Dynamic Trees: A Structured Variational Method Giving Efficient
Propagation Rules | cs.LG | Dynamic trees are mixtures of tree structured belief networks. They solve
some of the problems of fixed tree networks at the cost of making exact
inference intractable. For this reason approximate methods such as sampling or
mean field approaches have been used. However, mean field approximations assume
a factorized di... | computer science |
2,359 | A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks | cs.AI | This paper extends the work in [Suzuki, 1996] and presents an efficient
depth-first branch-and-bound algorithm for learning Bayesian network
structures, based on the minimum description length (MDL) principle, for a
given (consistent) variable ordering. The algorithm exhaustively searches
through all network structures... | computer science |
2,360 | Model-Based Hierarchical Clustering | cs.LG | We present an approach to model-based hierarchical clustering by formulating
an objective function based on a Bayesian analysis. This model organizes the
data into a cluster hierarchy while specifying a complex feature-set
partitioning that is a key component of our model. Features can have either a
unique distribution... | computer science |
2,361 | Variational Approximations between Mean Field Theory and the Junction
Tree Algorithm | cs.LG | Recently, variational approximations such as the mean field approximation
have received much interest. We extend the standard mean field method by using
an approximating distribution that factorises into cluster potentials. This
includes undirected graphs, directed acyclic graphs and junction trees. We
derive generaliz... | computer science |
2,362 | Multi-class Generalized Binary Search for Active Inverse Reinforcement
Learning | cs.LG | This paper addresses the problem of learning a task from demonstration. We
adopt the framework of inverse reinforcement learning, where tasks are
represented in the form of a reward function. Our contribution is a novel
active learning algorithm that enables the learning agent to query the expert
for more informative d... | computer science |
2,363 | Comparing Bayesian Network Classifiers | cs.LG | In this paper, we empirically evaluate algorithms for learning four types of
Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN
augmented Naive-Bayes and general BNs, where the latter two are learned using
two variants of a conditional-independence (CI) based BN-learning algorithm.
Experime... | computer science |
2,364 | Data Analysis with Bayesian Networks: A Bootstrap Approach | cs.LG | In recent years there has been significant progress in algorithms and methods
for inducing Bayesian networks from data. However, in complex data analysis
problems, we need to go beyond being satisfied with inducing networks with high
scores. We need to provide confidence measures on features of these networks:
Is the e... | computer science |
2,365 | A Bayesian Network Classifier that Combines a Finite Mixture Model and a
Naive Bayes Model | cs.LG | In this paper we present a new Bayesian network model for classification that
combines the naive-Bayes (NB) classifier and the finite-mixture (FM)
classifier. The resulting classifier aims at relaxing the strong assumptions on
which the two component models are based, in an attempt to improve on their
classification pe... | computer science |
2,366 | Learning Bayesian Networks with Restricted Causal Interactions | cs.AI | A major problem for the learning of Bayesian networks (BNs) is the
exponential number of parameters needed for conditional probability tables.
Recent research reduces this complexity by modeling local structure in the
probability tables. We examine the use of log-linear local models. While
log-linear models in this con... | computer science |
2,367 | The Bayesian Structural EM Algorithm | cs.LG | In recent years there has been a flurry of works on learning Bayesian
networks from data. One of the hard problems in this area is how to effectively
learn the structure of a belief network from incomplete data- that is, in the
presence of missing values or hidden variables. In a recent paper, I introduced
an algorithm... | computer science |
2,368 | Learning Mixtures of DAG Models | cs.LG | We describe computationally efficient methods for learning mixtures in which
each component is a directed acyclic graphical model (mixtures of DAGs or
MDAGs). We argue that simple search-and-score algorithms are infeasible for a
variety of problems, and introduce a feasible approach in which parameter and
structure sea... | computer science |
2,369 | Multiple decision trees | cs.LG | This paper describes experiments, on two domains, to investigate the effect
of averaging over predictions of multiple decision trees, instead of using a
single tree. Other authors have pointed out theoretical and commonsense reasons
for preferring the multiple tree approach. Ideally, we would like to consider
predictio... | computer science |
2,370 | An Algorithm for Training Polynomial Networks | cs.LG | We consider deep neural networks, in which the output of each node is a
quadratic function of its inputs. Similar to other deep architectures, these
networks can compactly represent any function on a finite training set. The
main goal of this paper is the derivation of an efficient layer-by-layer
algorithm for training... | computer science |
2,371 | Online Learning under Delayed Feedback | cs.LG | Online learning with delayed feedback has received increasing attention
recently due to its several applications in distributed, web-based learning
problems. In this paper we provide a systematic study of the topic, and analyze
the effect of delay on the regret of online learning algorithms. Somewhat
surprisingly, it t... | computer science |
2,372 | KL-based Control of the Learning Schedule for Surrogate Black-Box
Optimization | cs.LG | This paper investigates the control of an ML component within the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box
optimization. The known CMA-ES weakness is its sample complexity, the number of
evaluations of the objective function needed to approximate the global optimum.
This weakness is... | computer science |
2,373 | Efficient Learning of Generalized Linear and Single Index Models with
Isotonic Regression | cs.AI | Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide
powerful generalizations of linear regression, where the target variable is
assumed to be a (possibly unknown) 1-dimensional function of a linear
predictor. In general, these problems entail non-convex estimation procedures,
and, in practice, itera... | computer science |
2,374 | Bayesian Inference with Posterior Regularization and applications to
Infinite Latent SVMs | cs.LG | Existing Bayesian models, especially nonparametric Bayesian methods, rely on
specially conceived priors to incorporate domain knowledge for discovering
improved latent representations. While priors can affect posterior
distributions through Bayes' rule, imposing posterior regularization is
arguably more direct and in s... | computer science |
2,375 | Information fusion in multi-task Gaussian processes | stat.ML | This paper evaluates heterogeneous information fusion using multi-task
Gaussian processes in the context of geological resource modeling.
Specifically, it empirically demonstrates that information integration across
heterogeneous information sources leads to superior estimates of all the
quantities being modeled, compa... | computer science |
2,376 | The Kernel Pitman-Yor Process | cs.LG | In this work, we propose the kernel Pitman-Yor process (KPYP) for
nonparametric clustering of data with general spatial or temporal
interdependencies. The KPYP is constructed by first introducing an infinite
sequence of random locations. Then, based on the stick-breaking construction of
the Pitman-Yor process, we defin... | computer science |
2,377 | An Efficient Message-Passing Algorithm for the M-Best MAP Problem | cs.AI | Much effort has been directed at algorithms for obtaining the highest
probability configuration in a probabilistic random field model known as the
maximum a posteriori (MAP) inference problem. In many situations, one could
benefit from having not just a single solution, but the top M most probable
solutions known as th... | computer science |
2,378 | Hilbert Space Embeddings of POMDPs | cs.LG | A nonparametric approach for policy learning for POMDPs is proposed. The
approach represents distributions over the states, observations, and actions as
embeddings in feature spaces, which are reproducing kernel Hilbert spaces.
Distributions over states given the observations are obtained by applying the
kernel Bayes' ... | computer science |
2,379 | Learning STRIPS Operators from Noisy and Incomplete Observations | cs.LG | Agents learning to act autonomously in real-world domains must acquire a
model of the dynamics of the domain in which they operate. Learning domain
dynamics can be challenging, especially where an agent only has partial access
to the world state, and/or noisy external sensors. Even in standard STRIPS
domains, existing ... | computer science |
2,380 | Closed-Form Learning of Markov Networks from Dependency Networks | cs.LG | Markov networks (MNs) are a powerful way to compactly represent a joint
probability distribution, but most MN structure learning methods are very slow,
due to the high cost of evaluating candidates structures. Dependency networks
(DNs) represent a probability distribution as a set of conditional probability
distributio... | computer science |
2,381 | An Improved Admissible Heuristic for Learning Optimal Bayesian Networks | cs.AI | Recently two search algorithms, A* and breadth-first branch and bound
(BFBnB), were developed based on a simple admissible heuristic for learning
Bayesian network structures that optimize a scoring function. The heuristic
represents a relaxation of the learning problem such that each variable chooses
optimal parents in... | computer science |
2,382 | Advances in Learning Bayesian Networks of Bounded Treewidth | cs.AI | This work presents novel algorithms for learning Bayesian network structures
with bounded treewidth. Both exact and approximate methods are developed. The
exact method combines mixed-integer linear programming formulations for
structure learning and treewidth computation. The approximate method consists
in uniformly sa... | computer science |
2,383 | ExpertBayes: Automatically refining manually built Bayesian networks | cs.AI | Bayesian network structures are usually built using only the data and
starting from an empty network or from a naive Bayes structure. Very often, in
some domains, like medicine, a prior structure knowledge is already known. This
structure can be automatically or manually refined in search for better
performance models.... | computer science |
2,384 | Notes on hierarchical ensemble methods for DAG-structured taxonomies | cs.AI | Several real problems ranging from text classification to computational
biology are characterized by hierarchical multi-label classification tasks.
Most of the methods presented in literature focused on tree-structured
taxonomies, but only few on taxonomies structured according to a Directed
Acyclic Graph (DAG). In thi... | computer science |
2,385 | Efficient Learning in Large-Scale Combinatorial Semi-Bandits | cs.LG | A stochastic combinatorial semi-bandit is an online learning problem where at
each step a learning agent chooses a subset of ground items subject to
combinatorial constraints, and then observes stochastic weights of these items
and receives their sum as a payoff. In this paper, we consider efficient
learning in large-s... | computer science |
2,386 | Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based
Search | cs.LG | Bayesian model-based reinforcement learning is a formally elegant approach to
learning optimal behaviour under model uncertainty, trading off exploration and
exploitation in an ideal way. Unfortunately, finding the resulting
Bayes-optimal policies is notoriously taxing, since the search space becomes
enormous. In this ... | computer science |
2,387 | Learning Bayesian Network Parameters with Prior Knowledge about
Context-Specific Qualitative Influences | cs.AI | We present a method for learning the parameters of a Bayesian network with
prior knowledge about the signs of influences between variables. Our method
accommodates not just the standard signs, but provides for context-specific
signs as well. We show how the various signs translate into order constraints
on the network ... | computer science |
2,388 | Ordering-Based Search: A Simple and Effective Algorithm for Learning
Bayesian Networks | cs.LG | One of the basic tasks for Bayesian networks (BNs) is that of learning a
network structure from data. The BN-learning problem is NP-hard, so the
standard solution is heuristic search. Many approaches have been proposed for
this task, but only a very small number outperform the baseline of greedy
hill-climbing with tabu... | computer science |
2,389 | MOB-ESP and other Improvements in Probability Estimation | cs.LG | A key prerequisite to optimal reasoning under uncertainty in intelligent
systems is to start with good class probability estimates. This paper improves
on the current best probability estimation trees (Bagged-PETs) and also
presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using
several benchmark ... | computer science |
2,390 | Meta-Learning of Exploration/Exploitation Strategies: The Multi-Armed
Bandit Case | cs.AI | The exploration/exploitation (E/E) dilemma arises naturally in many subfields
of Science. Multi-armed bandit problems formalize this dilemma in its canonical
form. Most current research in this field focuses on generic solutions that can
be applied to a wide range of problems. However, in practice, it is often the
case... | computer science |
2,391 | Learning AMP Chain Graphs and some Marginal Models Thereof under
Faithfulness: Extended Version | stat.ML | This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP)
interpretation. In particular, we present a constraint based algorithm for
learning an AMP chain graph a given probability distribution is faithful to.
Moreover, we show that the extension of Meek's conjecture to AMP chain graphs
does not hold... | computer science |
2,392 | A Greedy Approximation of Bayesian Reinforcement Learning with Probably
Optimistic Transition Model | cs.AI | Bayesian Reinforcement Learning (RL) is capable of not only incorporating
domain knowledge, but also solving the exploration-exploitation dilemma in a
natural way. As Bayesian RL is intractable except for special cases, previous
work has proposed several approximation methods. However, these methods are
usually too sen... | computer science |
2,393 | SparsityBoost: A New Scoring Function for Learning Bayesian Network
Structure | cs.LG | We give a new consistent scoring function for structure learning of Bayesian
networks. In contrast to traditional approaches to scorebased 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 show... | computer science |
2,394 | Bethe-ADMM for Tree Decomposition based Parallel MAP Inference | cs.AI | We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexa... | computer science |
2,395 | Cyclic Causal Discovery from Continuous Equilibrium Data | cs.LG | We propose a method for learning cyclic causal models from a combination of
observational and interventional equilibrium data. Novel aspects of the
proposed method are its ability to work with continuous data (without assuming
linearity) and to deal with feedback loops. Within the context of biochemical
reactions, we a... | computer science |
2,396 | Identifying Finite Mixtures of Nonparametric Product Distributions and
Causal Inference of Confounders | cs.LG | We propose a kernel method to identify finite mixtures of nonparametric
product distributions. It is based on a Hilbert space embedding of the joint
distribution. The rank of the constructed tensor is equal to the number of
mixture components. We present an algorithm to recover the components by
partitioning the data p... | computer science |
2,397 | Sample complexity of learning Mahalanobis distance metrics | cs.LG | Metric learning seeks a transformation of the feature space that enhances
prediction quality for the given task at hand. In this work we provide
PAC-style sample complexity rates for supervised metric learning. We give
matching lower- and upper-bounds showing that the sample complexity scales with
the representation di... | computer science |
2,398 | Algorithmic Connections Between Active Learning and Stochastic Convex
Optimization | cs.LG | Interesting theoretical associations have been established by recent papers
between the fields of active learning and stochastic convex optimization due to
the common role of feedback in sequential querying mechanisms. In this paper,
we continue this thread in two parts by exploiting these relations for the
first time ... | computer science |
2,399 | An Experimental Comparison of Hybrid Algorithms for Bayesian Network
Structure Learning | stat.ML | We present a novel hybrid algorithm for Bayesian network structure learning,
called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian
network and then performs a Bayesian-scoring greedy hill-climbing search to
orient the edges. It is based on a subroutine called HPC, that combines ideas
from increment... | computer science |
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