Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
2,400 | Multi-Relational Learning at Scale with ADMM | stat.ML | Learning from multiple-relational data which contains noise, ambiguities, or
duplicate entities is essential to a wide range of applications such as
statistical inference based on Web Linked Data, recommender systems,
computational biology, and natural language processing. These tasks usually
require working with very ... | computer science |
2,401 | Hierarchical Compound Poisson Factorization | cs.LG | Non-negative matrix factorization models based on a hierarchical
Gamma-Poisson structure capture user and item behavior effectively in extremely
sparse data sets, making them the ideal choice for collaborative filtering
applications. Hierarchical Poisson factorization (HPF) in particular has proved
successful for scala... | computer science |
2,402 | Inverse Reinforcement Learning with Simultaneous Estimation of Rewards
and Dynamics | cs.AI | Inverse Reinforcement Learning (IRL) describes the problem of learning an
unknown reward function of a Markov Decision Process (MDP) from observed
behavior of an agent. Since the agent's behavior originates in its policy and
MDP policies depend on both the stochastic system dynamics as well as the
reward function, the ... | computer science |
2,403 | Weighted Spectral Cluster Ensemble | cs.LG | Clustering explores meaningful patterns in the non-labeled data sets. Cluster
Ensemble Selection (CES) is a new approach, which can combine individual
clustering results for increasing the performance of the final results.
Although CES can achieve better final results in comparison with individual
clustering algorithms... | computer science |
2,404 | Distributed Clustering of Linear Bandits in Peer to Peer Networks | cs.LG | We provide two distributed confidence ball algorithms for solving linear
bandit problems in peer to peer networks with limited communication
capabilities. For the first, we assume that all the peers are solving the same
linear bandit problem, and prove that our algorithm achieves the optimal
asymptotic regret rate of a... | computer science |
2,405 | Distributed Flexible Nonlinear Tensor Factorization | cs.LG | Tensor factorization is a powerful tool to analyse multi-way data. Compared
with traditional multi-linear methods, nonlinear tensor factorization models
are capable of capturing more complex relationships in the data. However, they
are computationally expensive and may suffer severe learning bias in case of
extreme dat... | computer science |
2,406 | Classifying Options for Deep Reinforcement Learning | cs.LG | In this paper we combine one method for hierarchical reinforcement learning -
the options framework - with deep Q-networks (DQNs) through the use of
different "option heads" on the policy network, and a supervisory network for
choosing between the different options. We utilise our setup to investigate the
effects of ar... | computer science |
2,407 | The Z-loss: a shift and scale invariant classification loss belonging to
the Spherical Family | cs.LG | Despite being the standard loss function to train multi-class neural
networks, the log-softmax has two potential limitations. First, it involves
computations that scale linearly with the number of output classes, which can
restrict the size of problems we are able to tackle with current hardware.
Second, it remains unc... | computer science |
2,408 | An expressive dissimilarity measure for relational clustering using
neighbourhood trees | stat.ML | Clustering is an underspecified task: there are no universal criteria for
what makes a good clustering. This is especially true for relational data,
where similarity can be based on the features of individuals, the relationships
between them, or a mix of both. Existing methods for relational clustering have
strong and ... | computer science |
2,409 | Active Diagnosis via AUC Maximization: An Efficient Approach for
Multiple Fault Identification in Large Scale, Noisy Networks | cs.LG | The problem of active diagnosis arises in several applications such as
disease diagnosis, and fault diagnosis in computer networks, where the goal is
to rapidly identify the binary states of a set of objects (e.g., faulty or
working) by sequentially selecting, and observing, (noisy) responses to binary
valued queries. ... | computer science |
2,410 | Hierarchical Affinity Propagation | cs.LG | Affinity propagation is an exemplar-based clustering algorithm that finds a
set of data-points that best exemplify the data, and associates each datapoint
with one exemplar. We extend affinity propagation in a principled way to solve
the hierarchical clustering problem, which arises in a variety of domains
including bi... | computer science |
2,411 | (weak) Calibration is Computationally Hard | cs.GT | We show that the existence of a computationally efficient calibration
algorithm, with a low weak calibration rate, would imply the existence of an
efficient algorithm for computing approximate Nash equilibria - thus implying
the unlikely conclusion that every problem in PPAD is solvable in polynomial
time. | computer science |
2,412 | TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing
Multiple Ratings | cs.LG | This paper revisits the problem of analyzing multiple ratings given by
different judges. Different from previous work that focuses on distilling the
true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic
insights into our in-house well-trained judges. We generalize the well-known
DawidSkene model... | computer science |
2,413 | A Generalized Loop Correction Method for Approximate Inference in
Graphical Models | cs.AI | Belief Propagation (BP) is one of the most popular methods for inference in
probabilistic graphical models. BP is guaranteed to return the correct answer
for tree structures, but can be incorrect or non-convergent for loopy graphical
models. Recently, several new approximate inference algorithms based on cavity
distrib... | computer science |
2,414 | Mixture-of-Parents Maximum Entropy Markov Models | cs.LG | We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a
class of directed graphical models extending MEMMs. The MoP-MEMM allows
tractable incorporation of long-range dependencies between nodes by restricting
the conditional distribution of each node to be a mixture of distributions
given the parent... | computer science |
2,415 | Accuracy Bounds for Belief Propagation | cs.AI | The belief propagation (BP) algorithm is widely applied to perform
approximate inference on arbitrary graphical models, in part due to its
excellent empirical properties and performance. However, little is known
theoretically about when this algorithm will perform well. Using recent
analysis of convergence and stabilit... | computer science |
2,416 | MAP Estimation, Linear Programming and Belief Propagation with Convex
Free Energies | cs.AI | Finding the most probable assignment (MAP) in a general graphical model is
known to be NP hard but good approximations have been attained with max-product
belief propagation (BP) and its variants. In particular, it is known that using
BP on a single-cycle graph or tree reweighted BP on an arbitrary graph will
give the ... | computer science |
2,417 | Imitation Learning with a Value-Based Prior | cs.LG | The goal of imitation learning is for an apprentice to learn how to behave in
a stochastic environment by observing a mentor demonstrating the correct
behavior. Accurate prior knowledge about the correct behavior can reduce the
need for demonstrations from the mentor. We present a novel approach to
encoding prior knowl... | computer science |
2,418 | Output Space Search for Structured Prediction | cs.LG | We consider a framework for structured prediction based on search in the
space of complete structured outputs. Given a structured input, an output is
produced by running a time-bounded search procedure guided by a learned cost
function, and then returning the least cost output uncovered during the search.
This framewor... | computer science |
2,419 | Advances in exact Bayesian structure discovery in Bayesian networks | cs.LG | We consider a Bayesian method for learning the Bayesian network structure
from complete data. Recently, Koivisto and Sood (2004) presented an algorithm
that for any single edge computes its marginal posterior probability in O(n
2^n) time, where n is the number of attributes; the number of parents per
attribute is bound... | computer science |
2,420 | Chi-square Tests Driven Method for Learning the Structure of Factored
MDPs | cs.LG | SDYNA is a general framework designed to address large stochastic
reinforcement learning problems. Unlike previous model based methods in FMDPs,
it incrementally learns the structure and the parameters of a RL problem using
supervised learning techniques. Then, it integrates decision-theoric planning
algorithms based o... | computer science |
2,421 | Identifying the Relevant Nodes Without Learning the Model | cs.LG | We propose a method to identify all the nodes that are relevant to compute
all the conditional probability distributions for a given set of nodes. Our
method is simple, effcient, consistent, and does not require learning a
Bayesian network first. Therefore, our method can be applied to
high-dimensional databases, e.g. ... | computer science |
2,422 | A compact, hierarchical Q-function decomposition | cs.LG | Previous work in hierarchical reinforcement learning has faced a dilemma:
either ignore the values of different possible exit states from a subroutine,
thereby risking suboptimal behavior, or represent those values explicitly
thereby incurring a possibly large representation cost because exit values
refer to nonlocal a... | computer science |
2,423 | On the Number of Samples Needed to Learn the Correct Structure of a
Bayesian Network | cs.LG | Bayesian Networks (BNs) are useful tools giving a natural and compact
representation of joint probability distributions. In many applications one
needs to learn a Bayesian Network (BN) from data. In this context, it is
important to understand the number of samples needed in order to guarantee a
successful learning. Pre... | computer science |
2,424 | A Non-Parametric Bayesian Method for Inferring Hidden Causes | cs.LG | We present a non-parametric Bayesian approach to structure learning with
hidden causes. Previous Bayesian treatments of this problem define a prior over
the number of hidden causes and use algorithms such as reversible jump Markov
chain Monte Carlo to move between solutions. In contrast, we assume that the
number of hi... | computer science |
2,425 | Incremental Model-based Learners With Formal Learning-Time Guarantees | cs.LG | Model-based learning algorithms have been shown to use experience efficiently
when learning to solve Markov Decision Processes (MDPs) with finite state and
action spaces. However, their high computational cost due to repeatedly solving
an internal model inhibits their use in large-scale problems. We propose a
method ba... | computer science |
2,426 | On the Convergence Properties of Optimal AdaBoost | cs.LG | AdaBoost is one of the most popular machine-learning algorithms. It is simple
to implement and often found very effective by practitioners, while still being
mathematically elegant and theoretically sound. AdaBoost's behavior in
practice, and in particular the test-error behavior, has puzzled many eminent
researchers f... | computer science |
2,427 | A Robust Independence Test for Constraint-Based Learning of Causal
Structure | cs.AI | Constraint-based (CB) learning is a formalism for learning a causal network
with a database D by performing a series of conditional-independence tests to
infer structural information. This paper considers a new test of independence
that combines ideas from Bayesian learning, Bayesian network inference, and
classical hy... | computer science |
2,428 | Large-Sample Learning of Bayesian Networks is NP-Hard | cs.LG | In this paper, we provide new complexity results for algorithms that learn
discrete-variable Bayesian networks from data. Our results apply whenever the
learning algorithm uses a scoring criterion that favors the simplest model able
to represent the generative distribution exactly. Our results therefore hold
whenever t... | computer science |
2,429 | Reasoning about Bayesian Network Classifiers | cs.LG | Bayesian network classifiers are used in many fields, and one common class of
classifiers are naive Bayes classifiers. In this paper, we introduce an
approach for reasoning about Bayesian network classifiers in which we
explicitly convert them into Ordered Decision Diagrams (ODDs), which are then
used to reason about t... | computer science |
2,430 | Approximate Inference and Constrained Optimization | cs.LG | Loopy and generalized belief propagation are popular algorithms for
approximate inference in Markov random fields and Bayesian networks. Fixed
points of these algorithms correspond to extrema of the Bethe and Kikuchi free
energy. However, belief propagation does not always converge, which explains
the need for approach... | computer science |
2,431 | On Local Optima in Learning Bayesian Networks | cs.LG | This paper proposes and evaluates the k-greedy equivalence search algorithm
(KES) for learning Bayesian networks (BNs) from complete data. The main
characteristic of KES is that it allows a trade-off between greediness and
randomness, thus exploring different good local optima. When greediness is set
at maximum, KES co... | computer science |
2,432 | Focus of Attention for Linear Predictors | stat.ML | We present a method to stop the evaluation of a prediction process when the
result of the full evaluation is obvious. This trait is highly desirable in
prediction tasks where a predictor evaluates all its features for every example
in large datasets. We observe that some examples are easier to classify than
others, a p... | computer science |
2,433 | A Bayesian Approach to Learning Bayesian Networks with Local Structure | cs.LG | Recently several researchers have investigated techniques for using data to
learn Bayesian networks containing compact representations for the conditional
probability distributions (CPDs) stored at each node. The majority of this work
has concentrated on using decision-tree representations for the CPDs. In
addition, re... | computer science |
2,434 | Learning Equivalence Classes of Bayesian Networks Structures | cs.AI | Approaches to learning Bayesian networks from data typically combine a
scoring function with a heuristic search procedure. Given a Bayesian network
structure, many of the scoring functions derived in the literature return a
score for the entire equivalence class to which the structure belongs. When
using such a scoring... | computer science |
2,435 | Learning Bayesian Networks with Local Structure | cs.AI | In this paper we examine a novel addition to the known methods for learning
Bayesian networks from data that improves the quality of the learned networks.
Our approach explicitly represents and learns the local structure in the
conditional probability tables (CPTs), that quantify these networks. This
increases the spac... | computer science |
2,436 | Estimating Continuous Distributions in Bayesian Classifiers | cs.LG | When modeling a probability distribution with a Bayesian network, we are
faced with the problem of how to handle continuous variables. Most previous
work has either solved the problem by discretizing, or assumed that the data
are generated by a single Gaussian. In this paper we abandon the normality
assumption and inst... | computer science |
2,437 | Taming the Curse of Dimensionality: Discrete Integration by Hashing and
Optimization | cs.LG | Integration is affected by the curse of dimensionality and quickly becomes
intractable as the dimensionality of the problem grows. We propose a randomized
algorithm that, with high probability, gives a constant-factor approximation of
a general discrete integral defined over an exponentially large set. This
algorithm r... | computer science |
2,438 | Learning Gaussian Networks | cs.AI | We describe algorithms for learning Bayesian networks from a combination of
user knowledge and statistical data. The algorithms have two components: a
scoring metric and a search procedure. The scoring metric takes a network
structure, statistical data, and a user's prior knowledge, and returns a score
proportional to ... | computer science |
2,439 | An Improved EM algorithm | cs.LG | In this paper, we firstly give a brief introduction of expectation
maximization (EM) algorithm, and then discuss the initial value sensitivity of
expectation maximization algorithm. Subsequently, we give a short proof of EM's
convergence. Then, we implement experiments with the expectation maximization
algorithm (We im... | computer science |
2,440 | Robust Logistic Regression using Shift Parameters (Long Version) | cs.AI | Annotation errors can significantly hurt classifier performance, yet datasets
are only growing noisier with the increased use of Amazon Mechanical Turk and
techniques like distant supervision that automatically generate labels. In this
paper, we present a robust extension of logistic regression that incorporates
the po... | computer science |
2,441 | Transductive Rademacher Complexity and its Applications | cs.LG | We develop a technique for deriving data-dependent error bounds for
transductive learning algorithms based on transductive Rademacher complexity.
Our technique is based on a novel general error bound for transduction in terms
of transductive Rademacher complexity, together with a novel bounding technique
for Rademacher... | computer science |
2,442 | Efficient Markov Network Structure Discovery Using Independence Tests | cs.LG | We present two algorithms for learning the structure of a Markov network from
data: GSMN* and GSIMN. Both algorithms use statistical independence tests to
infer the structure by successively constraining the set of structures
consistent with the results of these tests. Until very recently, algorithms for
structure lear... | computer science |
2,443 | Learning to Make Predictions In Partially Observable Environments
Without a Generative Model | cs.LG | When faced with the problem of learning a model of a high-dimensional
environment, a common approach is to limit the model to make only a restricted
set of predictions, thereby simplifying the learning problem. These partial
models may be directly useful for making decisions or may be combined together
to form a more c... | computer science |
2,444 | Properties of Bethe Free Energies and Message Passing in Gaussian Models | cs.LG | We address the problem of computing approximate marginals in Gaussian
probabilistic models by using mean field and fractional Bethe approximations.
We define the Gaussian fractional Bethe free energy in terms of the moment
parameters of the approximate marginals, derive a lower and an upper bound on
the fractional Beth... | computer science |
2,445 | Efficient Multi-Start Strategies for Local Search Algorithms | cs.LG | Local search algorithms applied to optimization problems often suffer from
getting trapped in a local optimum. The common solution for this deficiency is
to restart the algorithm when no progress is observed. Alternatively, one can
start multiple instances of a local search algorithm, and allocate
computational resourc... | computer science |
2,446 | Generalization and Exploration via Randomized Value Functions | stat.ML | We propose randomized least-squares value iteration (RLSVI) -- a new
reinforcement learning algorithm designed to explore and generalize efficiently
via linearly parameterized value functions. We explain why versions of
least-squares value iteration that use Boltzmann or epsilon-greedy exploration
can be highly ineffic... | computer science |
2,447 | Better Optimism By Bayes: Adaptive Planning with Rich Models | cs.AI | The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ... | computer science |
2,448 | Student-t Processes as Alternatives to Gaussian Processes | stat.ML | We investigate the Student-t process as an alternative to the Gaussian
process as a nonparametric prior over functions. We derive closed form
expressions for the marginal likelihood and predictive distribution of a
Student-t process, by integrating away an inverse Wishart process prior over
the covariance kernel of a G... | computer science |
2,449 | Unsupervised Ranking of Multi-Attribute Objects Based on Principal
Curves | cs.LG | Unsupervised ranking faces one critical challenge in evaluation applications,
that is, no ground truth is available. When PageRank and its variants show a
good solution in related subjects, they are applicable only for ranking from
link-structure data. In this work, we focus on unsupervised ranking from
multi-attribute... | computer science |
2,450 | Becoming More Robust to Label Noise with Classifier Diversity | stat.ML | It is widely known in the machine learning community that class noise can be
(and often is) detrimental to inducing a model of the data. Many current
approaches use a single, often biased, measurement to determine if an instance
is noisy. A biased measure may work well on certain data sets, but it can also
be less effe... | computer science |
2,451 | Matroid Bandits: Fast Combinatorial Optimization with Learning | cs.LG | A matroid is a notion of independence in combinatorial optimization which is
closely related to computational efficiency. In particular, it is well known
that the maximum of a constrained modular function can be found greedily if and
only if the constraints are associated with a matroid. In this paper, we bring
togethe... | computer science |
2,452 | Data generator based on RBF network | stat.ML | There are plenty of problems where the data available is scarce and
expensive. We propose a generator of semi-artificial data with similar
properties to the original data which enables development and testing of
different data mining algorithms and optimization of their parameters. The
generated data allow a large scal... | computer science |
2,453 | Robust Subspace Outlier Detection in High Dimensional Space | cs.AI | Rare data in a large-scale database are called outliers that reveal
significant information in the real world. The subspace-based outlier detection
is regarded as a feasible approach in very high dimensional space. However, the
outliers found in subspaces are only part of the true outliers in high
dimensional space, in... | computer science |
2,454 | Learning from networked examples | cs.AI | Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample because two or more training
examples may share some common objects, and hence share the features of these
shared objects. We ... | computer science |
2,455 | Two-Stage Metric Learning | cs.LG | In this paper, we present a novel two-stage metric learning algorithm. We
first map each learning instance to a probability distribution by computing its
similarities to a set of fixed anchor points. Then, we define the distance in
the input data space as the Fisher information distance on the associated
statistical ma... | computer science |
2,456 | Approximate Policy Iteration Schemes: A Comparison | cs.AI | We consider the infinite-horizon discounted optimal control problem
formalized by Markov Decision Processes. We focus on several approximate
variations of the Policy Iteration algorithm: Approximate Policy Iteration,
Conservative Policy Iteration (CPI), a natural adaptation of the Policy Search
by Dynamic Programming a... | computer science |
2,457 | Gaussian Approximation of Collective Graphical Models | cs.LG | The Collective Graphical Model (CGM) models a population of independent and
identically distributed individuals when only collective statistics (i.e.,
counts of individuals) are observed. Exact inference in CGMs is intractable,
and previous work has explored Markov Chain Monte Carlo (MCMC) and MAP
approximations for le... | computer science |
2,458 | Learning to Act Greedily: Polymatroid Semi-Bandits | cs.LG | Many important optimization problems, such as the minimum spanning tree and
minimum-cost flow, can be solved optimally by a greedy method. In this work, we
study a learning variant of these problems, where the model of the problem is
unknown and has to be learned by interacting repeatedly with the environment in
the ba... | computer science |
2,459 | A New Intelligence Based Approach for Computer-Aided Diagnosis of Dengue
Fever | stat.ML | Identification of the influential clinical symptoms and laboratory features
that help in the diagnosis of dengue fever in early phase of the illness would
aid in designing effective public health management and virological
surveillance strategies. Keeping this as our main objective we develop in this
paper, a new compu... | computer science |
2,460 | Generative Moment Matching Networks | cs.LG | We consider the problem of learning deep generative models from data. We
formulate a method that generates an independent sample via a single
feedforward pass through a multilayer perceptron, as in the recently proposed
generative adversarial networks (Goodfellow et al., 2014). Training a
generative adversarial network... | computer science |
2,461 | Policy Gradient for Coherent Risk Measures | cs.AI | Several authors have recently developed risk-sensitive policy gradient
methods that augment the standard expected cost minimization problem with a
measure of variability in cost. These studies have focused on specific
risk-measures, such as the variance or conditional value at risk (CVaR). In
this work, we extend the p... | computer science |
2,462 | Exact Hybrid Covariance Thresholding for Joint Graphical Lasso | cs.LG | This paper considers the problem of estimating multiple related Gaussian
graphical models from a $p$-dimensional dataset consisting of different
classes. Our work is based upon the formulation of this problem as group
graphical lasso. This paper proposes a novel hybrid covariance thresholding
algorithm that can effecti... | computer science |
2,463 | Interactive Restless Multi-armed Bandit Game and Swarm Intelligence
Effect | cs.AI | We obtain the conditions for the emergence of the swarm intelligence effect
in an interactive game of restless multi-armed bandit (rMAB). A player competes
with multiple agents. Each bandit has a payoff that changes with a probability
$p_{c}$ per round. The agents and player choose one of three options: (1)
Exploit (a ... | computer science |
2,464 | Active Model Aggregation via Stochastic Mirror Descent | stat.ML | We consider the problem of learning convex aggregation of models, that is as
good as the best convex aggregation, for the binary classification problem.
Working in the stream based active learning setting, where the active learner
has to make a decision on-the-fly, if it wants to query for the label of the
point curren... | computer science |
2,465 | Large-scale Validation of Counterfactual Learning Methods: A Test-Bed | cs.LG | The ability to perform effective off-policy learning would revolutionize the
process of building better interactive systems, such as search engines and
recommendation systems for e-commerce, computational advertising and news.
Recent approaches for off-policy evaluation and learning in these settings
appear promising. ... | computer science |
2,466 | Active Search for Sparse Signals with Region Sensing | stat.ML | Autonomous systems can be used to search for sparse signals in a large space;
e.g., aerial robots can be deployed to localize threats, detect gas leaks, or
respond to distress calls. Intuitively, search algorithms may increase
efficiency by collecting aggregate measurements summarizing large contiguous
regions. However... | computer science |
2,467 | Inferring Cognitive Models from Data using Approximate Bayesian
Computation | cs.HC | An important problem for HCI researchers is to estimate the parameter values
of a cognitive model from behavioral data. This is a difficult problem, because
of the substantial complexity and variety in human behavioral strategies. We
report an investigation into a new approach using approximate Bayesian
computation (AB... | computer science |
2,468 | Asynchronous Stochastic Gradient MCMC with Elastic Coupling | stat.ML | We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling
for problems where we can leverage (stochastic) gradients to define continuous
dynamics which explore the target distribution. We outline a solution strategy
for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling
(SGHMC) ... | computer science |
2,469 | Measuring the non-asymptotic convergence of sequential Monte Carlo
samplers using probabilistic programming | cs.AI | A key limitation of sampling algorithms for approximate inference is that it
is difficult to quantify their approximation error. Widely used sampling
schemes, such as sequential importance sampling with resampling and
Metropolis-Hastings, produce output samples drawn from a distribution that may
be far from the target ... | computer science |
2,470 | Interactive Elicitation of Knowledge on Feature Relevance Improves
Predictions in Small Data Sets | cs.AI | Providing accurate predictions is challenging for machine learning algorithms
when the number of features is larger than the number of samples in the data.
Prior knowledge can improve machine learning models by indicating relevant
variables and parameter values. Yet, this prior knowledge is often tacit and
only availab... | computer science |
2,471 | Prediction with a Short Memory | cs.LG | We consider the problem of predicting the next observation given a sequence
of past observations, and consider the extent to which accurate prediction
requires complex algorithms that explicitly leverage long-range dependencies.
Perhaps surprisingly, our positive results show that for a broad class of
sequences, there ... | computer science |
2,472 | Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and
Length-Scale Cool Down | cs.LG | Bayesian Optimization (BO) has become a core method for solving expensive
black-box optimization problems. While much research focussed on the choice of
the acquisition function, we focus on online length-scale adaption and the
choice of kernel function. Instead of choosing hyperparameters in view of
maximum likelihood... | computer science |
2,473 | Knowledge Elicitation via Sequential Probabilistic Inference for
High-Dimensional Prediction | cs.AI | Prediction in a small-sized sample with a large number of covariates, the
"small n, large p" problem, is challenging. This setting is encountered in
multiple applications, such as precision medicine, where obtaining additional
samples can be extremely costly or even impossible, and extensive research
effort has recentl... | computer science |
2,474 | Technical Report: A Generalized Matching Pursuit Approach for
Graph-Structured Sparsity | cs.LG | Sparsity-constrained optimization is an important and challenging problem
that has wide applicability in data mining, machine learning, and statistics.
In this paper, we focus on sparsity-constrained optimization in cases where the
cost function is a general nonlinear function and, in particular, the sparsity
constrain... | computer science |
2,475 | Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective
Functions | stat.ML | A key drawback of the current generation of artificial decision-makers is
that they do not adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, will optimize behavior with respect to an
objective func... | computer science |
2,476 | Towards Adaptive Training of Agent-based Sparring Partners for Fighter
Pilots | stat.ML | A key requirement for the current generation of artificial decision-makers is
that they should adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, must optimize behavior with respect to an
objective ... | computer science |
2,477 | Dynamical Kinds and their Discovery | stat.ML | We demonstrate the possibility of classifying causal systems into kinds that
share a common structure without first constructing an explicit dynamical model
or using prior knowledge of the system dynamics. The algorithmic ability to
determine whether arbitrary systems are governed by causal relations of the
same form o... | computer science |
2,478 | An Alternative Softmax Operator for Reinforcement Learning | cs.AI | A softmax operator applied to a set of values acts somewhat like the
maximization function and somewhat like an average. In sequential decision
making, softmax is often used in settings where it is necessary to maximize
utility but also to hedge against problems that arise from putting all of one's
weight behind a sing... | computer science |
2,479 | Non-Deterministic Policy Improvement Stabilizes Approximated
Reinforcement Learning | cs.AI | This paper investigates a type of instability that is linked to the greedy
policy improvement in approximated reinforcement learning. We show empirically
that non-deterministic policy improvement can stabilize methods like LSPI by
controlling the improvements' stochasticity. Additionally we show that a
suitable represe... | computer science |
2,480 | A Sparse Nonlinear Classifier Design Using AUC Optimization | cs.AI | AUC (Area under the ROC curve) is an important performance measure for
applications where the data is highly imbalanced. Learning to maximize AUC
performance is thus an important research problem. Using a max-margin based
surrogate loss function, AUC optimization problem can be approximated as a
pairwise rankSVM learni... | computer science |
2,481 | Adaptive Lambda Least-Squares Temporal Difference Learning | cs.LG | Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in
the field of reinforcement learning. However, setting TD's $\lambda$ parameter,
which controls the timescale of TD updates, is generally left up to the
practitioner. We formalize the $\lambda$ selection problem as a bias-variance
trade-off wher... | computer science |
2,482 | Unbiased Offline Evaluation of Contextual-bandit-based News Article
Recommendation Algorithms | cs.LG | Contextual bandit algorithms have become popular for online recommendation
systems such as Digg, Yahoo! Buzz, and news recommendation in general.
\emph{Offline} evaluation of the effectiveness of new algorithms in these
applications is critical for protecting online user experiences but very
challenging due to their "p... | computer science |
2,483 | Algorithm Runtime Prediction: Methods & Evaluation | cs.AI | Perhaps surprisingly, it is possible to predict how long an algorithm will
take to run on a previously unseen input, using machine learning techniques to
build a model of the algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to algorithm
analysis, portfolio... | computer science |
2,484 | Data Fusion by Matrix Factorization | cs.LG | For most problems in science and engineering we can obtain data sets that
describe the observed system from various perspectives and record the behavior
of its individual components. Heterogeneous data sets can be collectively mined
by data fusion. Fusion can focus on a specific target relation and exploit
directly ass... | computer science |
2,485 | Multi-Task Policy Search | stat.ML | Learning policies that generalize across multiple tasks is an important and
challenging research topic in reinforcement learning and robotics. Training
individual policies for every single potential task is often impractical,
especially for continuous task variations, requiring more principled approaches
to share and t... | computer science |
2,486 | Learning Markov networks with context-specific independences | cs.AI | Learning the Markov network structure from data is a problem that has
received considerable attention in machine learning, and in many other
application fields. This work focuses on a particular approach for this purpose
called independence-based learning. Such approach guarantees the learning of
the correct structure ... | computer science |
2,487 | Efficient Reinforcement Learning in Deterministic Systems with Value
Function Generalization | cs.LG | We consider the problem of reinforcement learning over episodes of a
finite-horizon deterministic system and as a solution propose optimistic
constraint propagation (OCP), an algorithm designed to synthesize efficient
exploration and value function generalization. We establish that when the true
value function lies wit... | computer science |
2,488 | A Sparse and Adaptive Prior for Time-Dependent Model Parameters | stat.ML | We consider the scenario where the parameters of a probabilistic model are
expected to vary over time. We construct a novel prior distribution that
promotes sparsity and adapts the strength of correlation between parameters at
successive timesteps, based on the data. We derive approximate variational
inference procedur... | computer science |
2,489 | Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery
EEG in Rehabilitation Training | stat.ML | Current studies about motor imagery based rehabilitation training systems for
stroke subjects lack an appropriate analytic method, which can achieve a
considerable classification accuracy, at the same time detects gradual changes
of imagery patterns during rehabilitation process and disinters potential
mechanisms about... | computer science |
2,490 | Provable Bounds for Learning Some Deep Representations | cs.LG | We give algorithms with provable guarantees that learn a class of deep nets
in the generative model view popularized by Hinton and others. Our generative
model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$
for some $\gamma <1$ and each edge has a random edge weight in $[-1,1]$. Our
algorithm... | computer science |
2,491 | Generalized Thompson Sampling for Contextual Bandits | cs.LG | Thompson Sampling, one of the oldest heuristics for solving multi-armed
bandits, has recently been shown to demonstrate state-of-the-art performance.
The empirical success has led to great interests in theoretical understanding
of this heuristic. In this paper, we approach this problem in a way very
different from exis... | computer science |
2,492 | Scalable Recommendation with Poisson Factorization | cs.IR | We develop a Bayesian Poisson matrix factorization model for forming
recommendations from sparse user behavior data. These data are large user/item
matrices where each user has provided feedback on only a small subset of items,
either explicitly (e.g., through star ratings) or implicitly (e.g., through
views or purchas... | computer science |
2,493 | Anytime Belief Propagation Using Sparse Domains | stat.ML | Belief Propagation has been widely used for marginal inference, however it is
slow on problems with large-domain variables and high-order factors. Previous
work provides useful approximations to facilitate inference on such models, but
lacks important anytime properties such as: 1) providing accurate and
consistent mar... | computer science |
2,494 | Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis | cs.AI | Non-linear dimensionality reduction techniques such as manifold learning
algorithms have become a common way for processing and analyzing
high-dimensional patterns that often have attached a target that corresponds to
the value of an unknown function. Their application to new points consists in
two steps: first, embedd... | computer science |
2,495 | Sparse Linear Dynamical System with Its Application in Multivariate
Clinical Time Series | cs.AI | Linear Dynamical System (LDS) is an elegant mathematical framework for
modeling and learning multivariate time series. However, in general, it is
difficult to set the dimension of its hidden state space. A small number of
hidden states may not be able to model the complexities of a time series, while
a large number of ... | computer science |
2,496 | Test Set Selection using Active Information Acquisition for Predictive
Models | cs.AI | In this paper, we consider active information acquisition when the prediction
model is meant to be applied on a targeted subset of the population. The goal
is to label a pre-specified fraction of customers in the target or test set by
iteratively querying for information from the non-target or training set. The
number ... | computer science |
2,497 | Graph Kernels via Functional Embedding | cs.LG | We propose a representation of graph as a functional object derived from the
power iteration of the underlying adjacency matrix. The proposed functional
representation is a graph invariant, i.e., the functional remains unchanged
under any reordering of the vertices. This property eliminates the difficulty
of handling e... | computer science |
2,498 | Learning Probabilistic Programs | cs.AI | We develop a technique for generalising from data in which models are
samplers represented as program text. We establish encouraging empirical
results that suggest that Markov chain Monte Carlo probabilistic programming
inference techniques coupled with higher-order probabilistic programming
languages are now sufficien... | computer science |
2,499 | Feature Selection in Conditional Random Fields for Map Matching of GPS
Trajectories | stat.ML | Map matching of the GPS trajectory serves the purpose of recovering the
original route on a road network from a sequence of noisy GPS observations. It
is a fundamental technique to many Location Based Services. However, map
matching of a low sampling rate on urban road network is still a challenging
task. In this paper... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.