884,477 research outputs found
Information-based objective functions for active data selection
Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness
On Measure Concentration of Random Maximum A-Posteriori Perturbations
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful
approach for inference and learning in high dimensional complex models. By
maximizing a randomly perturbed potential function, MAP perturbations generate
unbiased samples from the Gibbs distribution. Unfortunately, the computational
cost of generating so many high-dimensional random variables can be
prohibitive. More efficient algorithms use sequential sampling strategies based
on the expected value of low dimensional MAP perturbations. This paper develops
new measure concentration inequalities that bound the number of samples needed
to estimate such expected values. Applying the general result to MAP
perturbations can yield a more efficient algorithm to approximate sampling from
the Gibbs distribution. The measure concentration result is of general interest
and may be applicable to other areas involving expected estimations
Efficient Optimization of Performance Measures by Classifier Adaptation
In practical applications, machine learning algorithms are often needed to
learn classifiers that optimize domain specific performance measures.
Previously, the research has focused on learning the needed classifier in
isolation, yet learning nonlinear classifier for nonlinear and nonsmooth
performance measures is still hard. In this paper, rather than learning the
needed classifier by optimizing specific performance measure directly, we
circumvent this problem by proposing a novel two-step approach called as CAPO,
namely to first train nonlinear auxiliary classifiers with existing learning
methods, and then to adapt auxiliary classifiers for specific performance
measures. In the first step, auxiliary classifiers can be obtained efficiently
by taking off-the-shelf learning algorithms. For the second step, we show that
the classifier adaptation problem can be reduced to a quadratic program
problem, which is similar to linear SVMperf and can be efficiently solved. By
exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear
classifier which optimizes a large variety of performance measures including
all the performance measure based on the contingency table and AUC, whilst
keeping high computational efficiency. Empirical studies show that CAPO is
effective and of high computational efficiency, and even it is more efficient
than linear SVMperf.Comment: 30 pages, 5 figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence, 201
Similarity Learning for High-Dimensional Sparse Data
A good measure of similarity between data points is crucial to many tasks in
machine learning. Similarity and metric learning methods learn such measures
automatically from data, but they do not scale well respect to the
dimensionality of the data. In this paper, we propose a method that can learn
efficiently similarity measure from high-dimensional sparse data. The core idea
is to parameterize the similarity measure as a convex combination of rank-one
matrices with specific sparsity structures. The parameters are then optimized
with an approximate Frank-Wolfe procedure to maximally satisfy relative
similarity constraints on the training data. Our algorithm greedily
incorporates one pair of features at a time into the similarity measure,
providing an efficient way to control the number of active features and thus
reduce overfitting. It enjoys very appealing convergence guarantees and its
time and memory complexity depends on the sparsity of the data instead of the
dimension of the feature space. Our experiments on real-world high-dimensional
datasets demonstrate its potential for classification, dimensionality reduction
and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS 2015). Matlab code:
https://github.com/bellet/HDS
Classical Verification of Quantum Computations
We present the first protocol allowing a classical computer to interactively
verify the result of an efficient quantum computation. We achieve this by
constructing a measurement protocol, which enables a classical verifier to use
a quantum prover as a trusted measurement device. The protocol forces the
prover to behave as follows: the prover must construct an n qubit state of his
choice, measure each qubit in the Hadamard or standard basis as directed by the
verifier, and report the measurement results to the verifier. The soundness of
this protocol is enforced based on the assumption that the learning with errors
problem is computationally intractable for efficient quantum machines
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination () and methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data
Engineering. 14 pages, 5 figure
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