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2,000 | Exploring Deep and Recurrent Architectures for Optimal Control | cs.LG | Sophisticated multilayer neural networks have achieved state of the art
results on multiple supervised tasks. However, successful applications of such
multilayer networks to control have so far been limited largely to the
perception portion of the control pipeline. In this paper, we explore the
application of deep and ... | computer science |
2,001 | Robots that can adapt like animals | cs.RO | As robots leave the controlled environments of factories to autonomously
function in more complex, natural environments, they will have to respond to
the inevitable fact that they will become damaged. However, while animals can
quickly adapt to a wide variety of injuries, current robots cannot "think
outside the box" t... | computer science |
2,002 | Detect & Describe: Deep learning of bank stress in the news | cs.AI | News is a pertinent source of information on financial risks and stress
factors, which nevertheless is challenging to harness due to the sparse and
unstructured nature of natural text. We propose an approach based on
distributional semantics and deep learning with neural networks to model and
link text to a scarce set ... | computer science |
2,003 | Harnessing disordered quantum dynamics for machine learning | cs.AI | Quantum computer has an amazing potential of fast information processing.
However, realisation of a digital quantum computer is still a challenging
problem requiring highly accurate controls and key application strategies. Here
we propose a novel platform, quantum reservoir computing, to solve these issues
successfully... | computer science |
2,004 | An Approach to Stable Gradient Descent Adaptation of Higher-Order Neural
Units | cs.NE | Stability evaluation of a weight-update system of higher-order neural units
(HONUs) with polynomial aggregation of neural inputs (also known as classes of
polynomial neural networks) for adaptation of both feedforward and recurrent
HONUs by a gradient descent method is introduced. An essential core of the
approach is b... | computer science |
2,005 | Free energy-based reinforcement learning using a quantum processor | cs.LG | Recent theoretical and experimental results suggest the possibility of using
current and near-future quantum hardware in challenging sampling tasks. In this
paper, we introduce free energy-based reinforcement learning (FERL) as an
application of quantum hardware. We propose a method for processing a quantum
annealer's ... | computer science |
2,006 | Learning of Coordination Policies for Robotic Swarms | cs.RO | Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achi... | computer science |
2,007 | A Supervised Learning Concept for Reducing User Interaction in Passenger
Cars | cs.SY | In this article an automation system for human-machine-interfaces (HMI) for
setpoint adjustment using supervised learning is presented. We use HMIs of
multi-modal thermal conditioning systems in passenger cars as example for a
complex setpoint selection system. The goal is the reduction of interaction
complexity up to ... | computer science |
2,008 | Software Engineers vs. Machine Learning Algorithms: An Empirical Study
Assessing Performance and Reuse Tasks | cs.SE | Several papers have recently contained reports on applying machine learning
(ML) to the automation of software engineering (SE) tasks, such as project
management, modeling and development. However, there appear to be no approaches
comparing how software engineers fare against machine-learning algorithms as
applied to s... | computer science |
2,009 | SKYNET: an efficient and robust neural network training tool for machine
learning in astronomy | cs.LG | We present the first public release of our generic neural network training
algorithm, called SkyNet. This efficient and robust machine learning tool is
able to train large and deep feed-forward neural networks, including
autoencoders, for use in a wide range of supervised and unsupervised learning
applications, such as... | computer science |
2,010 | Variance Reduction for Faster Non-Convex Optimization | math.OC | We consider the fundamental problem in non-convex optimization of efficiently
reaching a stationary point. In contrast to the convex case, in the long
history of this basic problem, the only known theoretical results on
first-order non-convex optimization remain to be full gradient descent that
converges in $O(1/\varep... | computer science |
2,011 | Machine learning approximation algorithms for high-dimensional fully
nonlinear partial differential equations and second-order backward stochastic
differential equations | math.NA | High-dimensional partial differential equations (PDE) appear in a number of
models from the financial industry, such as in derivative pricing models,
credit valuation adjustment (CVA) models, or portfolio optimization models. The
PDEs in such applications are high-dimensional as the dimension corresponds to
the number ... | computer science |
2,012 | Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI
Big Data Analytics | cs.DC | In recent years, analyzing task-based fMRI (tfMRI) data has become an
essential tool for understanding brain function and networks. However, due to
the sheer size of tfMRI data, its intrinsic complex structure, and lack of
ground truth of underlying neural activities, modeling tfMRI data is hard and
challenging. Previo... | computer science |
2,013 | Residual Networks: Lyapunov Stability and Convex Decomposition | cs.LG | While training error of most deep neural networks degrades as the depth of
the network increases, residual networks appear to be an exception. We show
that the main reason for this is the Lyapunov stability of the gradient descent
algorithm: for an arbitrarily chosen step size, the equilibria of the gradient
descent ar... | computer science |
2,014 | Implementation of a Practical Distributed Calculation System with
Browsers and JavaScript, and Application to Distributed Deep Learning | cs.DC | Deep learning can achieve outstanding results in various fields. However, it
requires so significant computational power that graphics processing units
(GPUs) and/or numerous computers are often required for the practical
application. We have developed a new distributed calculation framework called
"Sashimi" that allow... | computer science |
2,015 | Predictive Business Process Monitoring with LSTM Neural Networks | stat.AP | Predictive business process monitoring methods exploit logs of completed
cases of a process in order to make predictions about running cases thereof.
Existing methods in this space are tailor-made for specific prediction tasks.
Moreover, their relative accuracy is highly sensitive to the dataset at hand,
thus requiring... | computer science |
2,016 | Learning with hidden variables | cs.LG | Learning and inferring features that generate sensory input is a task
continuously performed by cortex. In recent years, novel algorithms and
learning rules have been proposed that allow neural network models to learn
such features from natural images, written text, audio signals, etc. These
networks usually involve de... | computer science |
2,017 | SparkNet: Training Deep Networks in Spark | stat.ML | Training deep networks is a time-consuming process, with networks for object
recognition often requiring multiple days to train. For this reason, leveraging
the resources of a cluster to speed up training is an important area of work.
However, widely-popular batch-processing computational frameworks like
MapReduce and ... | computer science |
2,018 | ParMAC: distributed optimisation of nested functions, with application
to learning binary autoencoders | cs.LG | Many powerful machine learning models are based on the composition of
multiple processing layers, such as deep nets, which gives rise to nonconvex
objective functions. A general, recent approach to optimise such "nested"
functions is the method of auxiliary coordinates (MAC). MAC introduces an
auxiliary coordinate for ... | computer science |
2,019 | Tradeoffs between Convergence Speed and Reconstruction Accuracy in
Inverse Problems | cs.NA | Solving inverse problems with iterative algorithms is popular, especially for
large data. Due to time constraints, the number of possible iterations is
usually limited, potentially affecting the achievable accuracy. Given an error
one is willing to tolerate, an important question is whether it is possible to
modify the... | computer science |
2,020 | Dense Associative Memory for Pattern Recognition | cs.NE | A model of associative memory is studied, which stores and reliably retrieves
many more patterns than the number of neurons in the network. We propose a
simple duality between this dense associative memory and neural networks
commonly used in deep learning. On the associative memory side of this duality,
a family of mo... | computer science |
2,021 | Learning to Pivot with Adversarial Networks | stat.ML | Several techniques for domain adaptation have been proposed to account for
differences in the distribution of the data used for training and testing. The
majority of this work focuses on a binary domain label. Similar problems occur
in a scientific context where there may be a continuous family of plausible
data genera... | computer science |
2,022 | Customer Lifetime Value Prediction Using Embeddings | cs.LG | We describe the Customer LifeTime Value (CLTV) prediction system deployed at
ASOS.com, a global online fashion retailer. CLTV prediction is an important
problem in e-commerce where an accurate estimate of future value allows
retailers to effectively allocate marketing spend, identify and nurture high
value customers an... | computer science |
2,023 | Byzantine-Tolerant Machine Learning | cs.DC | The growth of data, the need for scalability and the complexity of models
used in modern machine learning calls for distributed implementations. Yet, as
of today, distributed machine learning frameworks have largely ignored the
possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study
the robustness ... | computer science |
2,024 | Dance Dance Convolution | cs.LG | Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players
perform steps on a dance platform in synchronization with music as directed by
on-screen step charts. While many step charts are available in standardized
packs, players may grow tired of existing charts, or wish to dance to a song
for which no ... | computer science |
2,025 | Balanced Excitation and Inhibition are Required for High-Capacity,
Noise-Robust Neuronal Selectivity | cs.LG | Neurons and networks in the cerebral cortex must operate reliably despite
multiple sources of noise. To evaluate the impact of both input and output
noise, we determine the robustness of single-neuron stimulus selective
responses, as well as the robustness of attractor states of networks of neurons
performing memory ta... | computer science |
2,026 | Deep learning-based numerical methods for high-dimensional parabolic
partial differential equations and backward stochastic differential equations | math.NA | We propose a new algorithm for solving parabolic partial differential
equations (PDEs) and backward stochastic differential equations (BSDEs) in high
dimension, by making an analogy between the BSDE and reinforcement learning
with the gradient of the solution playing the role of the policy function, and
the loss functi... | computer science |
2,027 | Natasha 2: Faster Non-Convex Optimization Than SGD | math.OC | We design a stochastic algorithm to train any smooth neural network to
$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
More broadly, it finds $\varepsilon$-approximate local minima of any smooth
nonconvex function i... | computer science |
2,028 | Neon2: Finding Local Minima via First-Order Oracles | cs.LG | We propose a reduction for non-convex optimization that can (1) turn an
stationary-point finding algorithm into an local-minimum finding one, and (2)
replace the Hessian-vector product computations with only gradient
computations. It works both in the stochastic and the deterministic settings,
without hurting the algor... | computer science |
2,029 | Non-linear motor control by local learning in spiking neural networks | cs.LG | Learning weights in a spiking neural network with hidden neurons, using
local, stable and online rules, to control non-linear body dynamics is an open
problem. Here, we employ a supervised scheme, Feedback-based Online Local
Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
neurons with hidden l... | computer science |
2,030 | On Characterizing the Capacity of Neural Networks using Algebraic
Topology | cs.LG | The learnability of different neural architectures can be characterized
directly by computable measures of data complexity. In this paper, we reframe
the problem of architecture selection as understanding how data determines the
most expressive and generalizable architectures suited to that data, beyond
inductive bias.... | computer science |
2,031 | Effect of Tuned Parameters on a LSA MCQ Answering Model | cs.LG | This paper presents the current state of a work in progress, whose objective
is to better understand the effects of factors that significantly influence the
performance of Latent Semantic Analysis (LSA). A difficult task, which consists
in answering (French) biology Multiple Choice Questions, is used to test the
semant... | computer science |
2,032 | Uncovering the Riffled Independence Structure of Rankings | cs.LG | Representing distributions over permutations can be a daunting task due to
the fact that the number of permutations of $n$ objects scales factorially in
$n$. One recent way that has been used to reduce storage complexity has been to
exploit probabilistic independence, but as we argue, full independence
assumptions impo... | computer science |
2,033 | A Reduction of Imitation Learning and Structured Prediction to No-Regret
Online Learning | cs.LG | Sequential prediction problems such as imitation learning, where future
observations depend on previous predictions (actions), violate the common
i.i.d. assumptions made in statistical learning. This leads to poor performance
in theory and often in practice. Some recent approaches provide stronger
guarantees in this se... | computer science |
2,034 | Bayesian and L1 Approaches to Sparse Unsupervised Learning | cs.LG | The use of L1 regularisation for sparse learning has generated immense
research interest, with successful application in such diverse areas as signal
acquisition, image coding, genomics and collaborative filtering. While existing
work highlights the many advantages of L1 methods, in this paper we find that
L1 regularis... | computer science |
2,035 | Agnostic System Identification for Model-Based Reinforcement Learning | cs.LG | A fundamental problem in control is to learn a model of a system from
observations that is useful for controller synthesis. To provide good
performance guarantees, existing methods must assume that the real system is in
the class of models considered during learning. We present an iterative method
with strong guarantee... | computer science |
2,036 | Role-Dynamics: Fast Mining of Large Dynamic Networks | cs.SI | To understand the structural dynamics of a large-scale social, biological or
technological network, it may be useful to discover behavioral roles
representing the main connectivity patterns present over time. In this paper,
we propose a scalable non-parametric approach to automatically learn the
structural dynamics of ... | computer science |
2,037 | Variance-Based Rewards for Approximate Bayesian Reinforcement Learning | cs.LG | The explore{exploit dilemma is one of the central challenges in Reinforcement
Learning (RL). Bayesian RL solves the dilemma by providing the agent with
information in the form of a prior distribution over environments; however,
full Bayesian planning is intractable. Planning with the mean MDP is a common
myopic approxi... | computer science |
2,038 | Bayesian Model Averaging Using the k-best Bayesian Network Structures | cs.LG | We study the problem of learning Bayesian network structures from data. We
develop an algorithm for finding the k-best Bayesian network structures. We
propose to compute the posterior probabilities of hypotheses of interest by
Bayesian model averaging over the k-best Bayesian networks. We present
empirical results on s... | computer science |
2,039 | Scoring and Searching over Bayesian Networks with Causal and Associative
Priors | cs.AI | A significant theoretical advantage of search-and-score methods for learning
Bayesian Networks is that they can accept informative prior beliefs for each
possible network, thus complementing the data. In this paper, a method is
presented for assigning priors based on beliefs on the presence or absence of
certain paths ... | computer science |
2,040 | Adaptive learning rates and parallelization for stochastic, sparse,
non-smooth gradients | cs.LG | Recent work has established an empirically successful framework for adapting
learning rates for stochastic gradient descent (SGD). This effectively removes
all needs for tuning, while automatically reducing learning rates over time on
stationary problems, and permitting learning rates to grow appropriately in
non-stati... | computer science |
2,041 | Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed
Continuous And Discrete Variables | cs.LG | Recently developed techniques have made it possible to quickly learn accurate
probability density functions from data in low-dimensional continuous space. In
particular, mixtures of Gaussians can be fitted to data very quickly using an
accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999).
In thi... | computer science |
2,042 | Learning Bayesian Network Structure from Massive Datasets: The "Sparse
Candidate" Algorithm | cs.LG | Learning Bayesian networks is often cast as an optimization problem, where
the computational task is to find a structure that maximizes a statistically
motivated score. By and large, existing learning tools address this
optimization problem using standard heuristic search techniques. Since the
search space is extremely... | computer science |
2,043 | A Variational Approximation for Bayesian Networks with Discrete and
Continuous Latent Variables | cs.AI | We show how to use a variational approximation to the logistic function to
perform approximate inference in Bayesian networks containing discrete nodes
with continuous parents. Essentially, we convert the logistic function to a
Gaussian, which facilitates exact inference, and then iteratively adjust the
variational par... | computer science |
2,044 | A General Framework for Interacting Bayes-Optimally with Self-Interested
Agents using Arbitrary Parametric Model and Model Prior | cs.LG | Recent advances in Bayesian reinforcement learning (BRL) have shown that
Bayes-optimality is theoretically achievable by modeling the environment's
latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In
self-interested multi-agent environments, the transition dynamics are mainly
controlled by the other agent'... | computer science |
2,045 | Local Structure Discovery in Bayesian Networks | cs.LG | Learning a Bayesian network structure from data is an NP-hard problem and
thus exact algorithms are feasible only for small data sets. Therefore, network
structures for larger networks are usually learned with various heuristics.
Another approach to scaling up the structure learning is local learning. In
local learning... | computer science |
2,046 | New Advances and Theoretical Insights into EDML | cs.AI | EDML is a recently proposed algorithm for learning MAP parameters in Bayesian
networks. In this paper, we present a number of new advances and insights on
the EDML algorithm. First, we provide the multivalued extension of EDML,
originally proposed for Bayesian networks over binary variables. Next, we
identify a simplif... | computer science |
2,047 | Dynamic Teaching in Sequential Decision Making Environments | cs.LG | We describe theoretical bounds and a practical algorithm for teaching a model
by demonstration in a sequential decision making environment. Unlike previous
efforts that have optimized learners that watch a teacher demonstrate a static
policy, we focus on the teacher as a decision maker who can dynamically choose
differ... | computer science |
2,048 | Temporal Autoencoding Restricted Boltzmann Machine | stat.ML | Much work has been done refining and characterizing the receptive fields
learned by deep learning algorithms. A lot of this work has focused on the
development of Gabor-like filters learned when enforcing sparsity constraints
on a natural image dataset. Little work however has investigated how these
filters might expan... | computer science |
2,049 | Discovering Structure in High-Dimensional Data Through Correlation
Explanation | cs.LG | We introduce a method to learn a hierarchy of successively more abstract
representations of complex data based on optimizing an information-theoretic
objective. Intuitively, the optimization searches for a set of latent factors
that best explain the correlations in the data as measured by multivariate
mutual informatio... | computer science |
2,050 | Spectral Ranking using Seriation | cs.LG | We describe a seriation algorithm for ranking a set of items given pairwise
comparisons between these items. Intuitively, the algorithm assigns similar
rankings to items that compare similarly with all others. It does so by
constructing a similarity matrix from pairwise comparisons, using seriation
methods to reorder t... | computer science |
2,051 | PAC-Bayes Analysis of Multi-view Learning | cs.LG | This paper presents eight PAC-Bayes bounds to analyze the generalization
performance of multi-view classifiers. These bounds adopt data dependent
Gaussian priors which emphasize classifiers with high view agreements. The
center of the prior for the first two bounds is the origin, while the center of
the prior for the t... | computer science |
2,052 | Learning Discriminative Metrics via Generative Models and Kernel
Learning | cs.LG | Metrics specifying distances between data points can be learned in a
discriminative manner or from generative models. In this paper, we show how to
unify generative and discriminative learning of metrics via a kernel learning
framework. Specifically, we learn local metrics optimized from parametric
generative models. T... | computer science |
2,053 | Sparse Nested Markov models with Log-linear Parameters | cs.LG | Hidden variables are ubiquitous in practical data analysis, and therefore
modeling marginal densities and doing inference with the resulting models is an
important problem in statistics, machine learning, and causal inference.
Recently, a new type of graphical model, called the nested Markov model, was
developed which ... | computer science |
2,054 | MCODE: Multivariate Conditional Outlier Detection | cs.AI | Outlier detection aims to identify unusual data instances that deviate from
expected patterns. The outlier detection is particularly challenging when
outliers are context dependent and when they are defined by unusual
combinations of multiple outcome variable values. In this paper, we develop and
study a new conditiona... | computer science |
2,055 | Hinge-Loss Markov Random Fields and Probabilistic Soft Logic | cs.LG | A fundamental challenge in developing high-impact machine learning
technologies is balancing the need to model rich, structured domains with the
ability to scale to big data. Many important problem areas are both richly
structured and large scale, from social and biological networks, to knowledge
graphs and the Web, to... | computer science |
2,056 | Constructive Preference Elicitation by Setwise Max-margin Learning | stat.ML | In this paper we propose an approach to preference elicitation that is
suitable to large configuration spaces beyond the reach of existing
state-of-the-art approaches. Our setwise max-margin method can be viewed as a
generalization of max-margin learning to sets, and can produce a set of
"diverse" items that can be use... | computer science |
2,057 | Deep, Convolutional, and Recurrent Models for Human Activity Recognition
using Wearables | cs.LG | Human activity recognition (HAR) in ubiquitous computing is beginning to
adopt deep learning to substitute for well-established analysis techniques that
rely on hand-crafted feature extraction and classification techniques. From
these isolated applications of custom deep architectures it is, however,
difficult to gain ... | computer science |
2,058 | Sum-Product Networks: A New Deep Architecture | cs.LG | The key limiting factor in graphical model inference and learning is the
complexity of the partition function. We thus ask the question: what are
general conditions under which the partition function is tractable? The answer
leads to a new kind of deep architecture, which we call sum-product networks
(SPNs). SPNs are d... | computer science |
2,059 | Efficient Probabilistic Inference with Partial Ranking Queries | cs.LG | Distributions over rankings are used to model data in various settings such
as preference analysis and political elections. The factorial size of the space
of rankings, however, typically forces one to make structural assumptions, such
as smoothness, sparsity, or probabilistic independence about these underlying
distri... | computer science |
2,060 | Learning Determinantal Point Processes | cs.LG | Determinantal point processes (DPPs), which arise in random matrix theory and
quantum physics, are natural models for subset selection problems where
diversity is preferred. Among many remarkable properties, DPPs offer tractable
algorithms for exact inference, including computing marginal probabilities and
sampling; ho... | computer science |
2,061 | Graphical Models for Bandit Problems | cs.LG | We introduce a rich class of graphical models for multi-armed bandit problems
that permit both the state or context space and the action space to be very
large, yet succinctly specify the payoffs for any context-action pair. Our main
result is an algorithm for such models whose regret is bounded by the number of
parame... | computer science |
2,062 | Causal discovery of linear acyclic models with arbitrary distributions | stat.ML | An important task in data analysis is the discovery of causal relationships
between observed variables. For continuous-valued data, linear acyclic causal
models are commonly used to model the data-generating process, and the
inference of such models is a well-studied problem. However, existing methods
have significant ... | computer science |
2,063 | Near-Optimal BRL using Optimistic Local Transitions | cs.AI | Model-based Bayesian Reinforcement Learning (BRL) allows a found
formalization of the problem of acting optimally while facing an unknown
environment, i.e., avoiding the exploration-exploitation dilemma. However,
algorithms explicitly addressing BRL suffer from such a combinatorial explosion
that a large body of work r... | computer science |
2,064 | Continuous Inverse Optimal Control with Locally Optimal Examples | cs.LG | Inverse optimal control, also known as inverse reinforcement learning, is the
problem of recovering an unknown reward function in a Markov decision process
from expert demonstrations of the optimal policy. We introduce a probabilistic
inverse optimal control algorithm that scales gracefully with task
dimensionality, an... | computer science |
2,065 | Machine Learning that Matters | cs.LG | Much of current machine learning (ML) research has lost its connection to
problems of import to the larger world of science and society. From this
perspective, there exist glaring limitations in the data sets we investigate,
the metrics we employ for evaluation, and the degree to which results are
communicated back to ... | computer science |
2,066 | Reading Dependencies from Polytree-Like Bayesian Networks | cs.AI | We present a graphical criterion for reading dependencies from the minimal
directed independence map G of a graphoid p when G is a polytree and p
satisfies composition and weak transitivity. We prove that the criterion is
sound and complete. We argue that assuming composition and weak transitivity is
not too restrictiv... | computer science |
2,067 | Consensus ranking under the exponential model | cs.LG | We analyze the generalized Mallows model, a popular exponential model over
rankings. Estimating the central (or consensus) ranking from data is NP-hard.
We obtain the following new results: (1) We show that search methods can
estimate both the central ranking pi0 and the model parameters theta exactly.
The search is n!... | computer science |
2,068 | Improved Dynamic Schedules for Belief Propagation | cs.LG | Belief propagation and its variants are popular methods for approximate
inference, but their running time and even their convergence depend greatly on
the schedule used to send the messages. Recently, dynamic update schedules have
been shown to converge much faster on hard networks than static schedules,
namely the res... | computer science |
2,069 | How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical
Model for Adaptive Crowdsourcing and Aptitude Testing | cs.LG | We propose a new probabilistic graphical model that jointly models the
difficulties of questions, the abilities of participants and the correct
answers to questions in aptitude testing and crowdsourcing settings. We devise
an active learning/adaptive testing scheme based on a greedy minimization of
expected model entro... | computer science |
2,070 | Demand-Driven Clustering in Relational Domains for Predicting Adverse
Drug Events | cs.LG | Learning from electronic medical records (EMR) is challenging due to their
relational nature and the uncertain dependence between a patient's past and
future health status. Statistical relational learning is a natural fit for
analyzing EMRs but is less adept at handling their inherent latent structure,
such as connecti... | computer science |
2,071 | Bounded Planning in Passive POMDPs | cs.LG | In Passive POMDPs actions do not affect the world state, but still incur
costs. When the agent is bounded by information-processing constraints, it can
only keep an approximation of the belief. We present a variational principle
for the problem of maintaining the information which is most useful for
minimizing the cost... | computer science |
2,072 | Apprenticeship Learning for Model Parameters of Partially Observable
Environments | cs.LG | We consider apprenticeship learning, i.e., having an agent learn a task by
observing an expert demonstrating the task in a partially observable
environment when the model of the environment is uncertain. This setting is
useful in applications where the explicit modeling of the environment is
difficult, such as a dialog... | computer science |
2,073 | Approximate Separability for Weak Interaction in Dynamic Systems | cs.LG | One approach to monitoring a dynamic system relies on decomposition of the
system into weakly interacting subsystems. An earlier paper introduced a notion
of weak interaction called separability, and showed that it leads to exact
propagation of marginals for prediction. This paper addresses two questions
left open by t... | computer science |
2,074 | Structured Priors for Structure Learning | cs.LG | Traditional approaches to Bayes net structure learning typically assume
little regularity in graph structure other than sparseness. However, in many
cases, we expect more systematicity: variables in real-world systems often
group into classes that predict the kinds of probabilistic dependencies they
participate in. Her... | computer science |
2,075 | Compiling Relational Database Schemata into Probabilistic Graphical
Models | cs.AI | Instead of requiring a domain expert to specify the probabilistic
dependencies of the data, in this work we present an approach that uses the
relational DB schema to automatically construct a Bayesian graphical model for
a database. This resulting model contains customized distributions for columns,
latent variables th... | computer science |
2,076 | Making Early Predictions of the Accuracy of Machine Learning
Applications | cs.LG | The accuracy of machine learning systems is a widely studied research topic.
Established techniques such as cross-validation predict the accuracy on unseen
data of the classifier produced by applying a given learning method to a given
training data set. However, they do not predict whether incurring the cost of
obtaini... | computer science |
2,077 | Efficient Approximations for the Marginal Likelihood of Incomplete Data
Given a Bayesian Network | cs.LG | We discuss Bayesian methods for learning Bayesian networks when data sets are
incomplete. In particular, we examine asymptotic approximations for the
marginal likelihood of incomplete data given a Bayesian network. We consider
the Laplace approximation and the less accurate but more efficient BIC/MDL
approximation. We ... | computer science |
2,078 | Asymptotic Model Selection for Directed Networks with Hidden Variables | cs.LG | We extend the Bayesian Information Criterion (BIC), an asymptotic
approximation for the marginal likelihood, to Bayesian networks with hidden
variables. This approximation can be used to select models given large samples
of data. The standard BIC as well as our extension punishes the complexity of a
model according to ... | computer science |
2,079 | Estimating the Maximum Expected Value: An Analysis of (Nested) Cross
Validation and the Maximum Sample Average | stat.ML | We investigate the accuracy of the two most common estimators for the maximum
expected value of a general set of random variables: a generalization of the
maximum sample average, and cross validation. No unbiased estimator exists and
we show that it is non-trivial to select a good estimator without knowledge
about the ... | computer science |
2,080 | Testing Hypotheses by Regularized Maximum Mean Discrepancy | cs.LG | Do two data samples come from different distributions? Recent studies of this
fundamental problem focused on embedding probability distributions into
sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to
compare distributions by the distance between their embeddings. We show that
Regularized Ma... | computer science |
2,081 | Feature and Variable Selection in Classification | cs.LG | The amount of information in the form of features and variables avail- able
to machine learning algorithms is ever increasing. This can lead to classifiers
that are prone to overfitting in high dimensions, high di- mensional models do
not lend themselves to interpretable results, and the CPU and memory resources
necess... | computer science |
2,082 | Counterfactual Estimation and Optimization of Click Metrics for Search
Engines | cs.LG | Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different... | computer science |
2,083 | K-NS: Section-Based Outlier Detection in High Dimensional Space | cs.AI | Finding rare information hidden in a huge amount of data from the Internet is
a necessary but complex issue. Many researchers have studied this issue and
have found effective methods to detect anomaly data in low dimensional space.
However, as the dimension increases, most of these existing methods perform
poorly in de... | computer science |
2,084 | FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test | cs.AI | The maximum mean discrepancy (MMD) is a recently proposed test statistic for
two-sample test. Its quadratic time complexity, however, greatly hampers its
availability to large-scale applications. To accelerate the MMD calculation, in
this study we propose an efficient method called FastMMD. The core idea of
FastMMD is ... | computer science |
2,085 | Cheaper and Better: Selecting Good Workers for Crowdsourcing | stat.ML | Crowdsourcing provides a popular paradigm for data collection at scale. We
study the problem of selecting subsets of workers from a given worker pool to
maximize the accuracy under a budget constraint. One natural question is
whether we should hire as many workers as the budget allows, or restrict on a
small number of ... | computer science |
2,086 | Collaborative Filtering Bandits | cs.LG | Classical collaborative filtering, and content-based filtering methods try to
learn a static recommendation model given training data. These approaches are
far from ideal in highly dynamic recommendation domains such as news
recommendation and computational advertisement, where the set of items and
users is very fluid.... | computer science |
2,087 | Low-Cost Learning via Active Data Procurement | cs.GT | We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showin... | computer science |
2,088 | Sequential Feature Explanations for Anomaly Detection | cs.AI | In many applications, an anomaly detection system presents the most anomalous
data instance to a human analyst, who then must determine whether the instance
is truly of interest (e.g. a threat in a security setting). Unfortunately, most
anomaly detectors provide no explanation about why an instance was considered
anoma... | computer science |
2,089 | A Meta-Analysis of the Anomaly Detection Problem | cs.AI | This article provides a thorough meta-analysis of the anomaly detection
problem. To accomplish this we first identify approaches to benchmarking
anomaly detection algorithms across the literature and produce a large corpus
of anomaly detection benchmarks that vary in their construction across several
dimensions we deem... | computer science |
2,090 | Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior | cs.LG | Learning the network structure underlying data is an important problem in
machine learning. This paper introduces a novel prior to study the inference of
scale-free networks, which are widely used to model social and biological
networks. The prior not only favors a desirable global node degree
distribution, but also ta... | computer science |
2,091 | Interpolating Convex and Non-Convex Tensor Decompositions via the
Subspace Norm | cs.LG | We consider the problem of recovering a low-rank tensor from its noisy
observation. Previous work has shown a recovery guarantee with signal to noise
ratio $O(n^{\lceil K/2 \rceil /2})$ for recovering a $K$th order rank one
tensor of size $n\times \cdots \times n$ by recursive unfolding. In this paper,
we first improve... | computer science |
2,092 | Block-Wise MAP Inference for Determinantal Point Processes with
Application to Change-Point Detection | cs.LG | Existing MAP inference algorithms for determinantal point processes (DPPs)
need to calculate determinants or conduct eigenvalue decomposition generally at
the scale of the full kernel, which presents a great challenge for real-world
applications. In this paper, we introduce a class of DPPs, called BwDPPs, that
are char... | computer science |
2,093 | Transfer Learning Across Patient Variations with Hidden Parameter Markov
Decision Processes | stat.ML | Due to physiological variation, patients diagnosed with the same condition
may exhibit divergent, but related, responses to the same treatments. Hidden
Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning
problem by embedding these tasks into a low-dimensional space. However, the
original formul... | computer science |
2,094 | Overcoming catastrophic forgetting in neural networks | cs.LG | The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this lim... | computer science |
2,095 | Factored Contextual Policy Search with Bayesian Optimization | cs.LG | Scarce data is a major challenge to scaling robot learning to truly complex
tasks, as we need to generalize locally learned policies over different
"contexts". Bayesian optimization approaches to contextual policy search (CPS)
offer data-efficient policy learning that generalize over a context space. We
propose to impr... | computer science |
2,096 | Task-Guided and Path-Augmented Heterogeneous Network Embedding for
Author Identification | cs.LG | In this paper, we study the problem of author identification under
double-blind review setting, which is to identify potential authors given
information of an anonymized paper. Different from existing approaches that
rely heavily on feature engineering, we propose to use network embedding
approach to address the proble... | computer science |
2,097 | Learning Representations by Stochastic Meta-Gradient Descent in Neural
Networks | cs.LG | Representations are fundamental to artificial intelligence. The performance
of a learning system depends on the type of representation used for
representing the data. Typically, these representations are hand-engineered
using domain knowledge. More recently, the trend is to learn these
representations through stochasti... | computer science |
2,098 | Knowledge Completion for Generics using Guided Tensor Factorization | cs.AI | Given a knowledge base (KB) rich in facts about common nouns or generics,
such as "all trees produce oxygen" or "some animals live in forests", we
consider the problem of deriving additional such facts at a high precision.
While this problem has received much attention for named entity KBs such as
Freebase, little emph... | computer science |
2,099 | Encapsulating models and approximate inference programs in probabilistic
modules | cs.AI | This paper introduces the probabilistic module interface, which allows
encapsulation of complex probabilistic models with latent variables alongside
custom stochastic approximate inference machinery, and provides a
platform-agnostic abstraction barrier separating the model internals from the
host probabilistic inferenc... | computer science |
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