The Experts below are selected from a list of 130185 Experts worldwide ranked by ideXlab platform
Albert Guillen I Fabregas - One of the best experts on this subject based on the ideXlab platform.
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Importance Sampling for Coded-Modulation Error Probability Estimation
IEEE Transactions on Communications, 2020Co-Authors: Josep Font-segura, Alfonso Martinez, Albert Guillen I FabregasAbstract:This paper proposes an efficient simulation method based on importance sampling to estimate the random-coding error Probability of coded modulation. The technique is valid for complex-valued modulations over Gaussian channels, channels with memory, and naturally extends to fading channels. The simulation method is built on two nested importance samplers to respectively estimate the pairwise error Probability and generate the channel input and output. The effect of the respective number of samples on the overall bias and variance of the estimate of the error Probability is characterized. For a memoryless channel, the estimator is shown to be consistent and with a small variance, growing with the square root of the code length, rather than the exponential growth of a standard Monte Carlo estimator.
Peter Bierhorst - One of the best experts on this subject based on the ideXlab platform.
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certifying quantum randomness by Probability Estimation
Physical Review A, 2018Co-Authors: Yanbao Zhang, Emanuel Knill, Peter BierhorstAbstract:We introduce Probability Estimation, a broadly applicable framework to certify randomness in a finite sequence of measurement results without assuming that these results are independent and identically distributed. Probability Estimation can take advantage of verifiable physical constraints, and the certification is with respect to classical side information. Examples include randomness from single-photon measurements and device-independent randomness from Bell tests. Advantages of Probability Estimation include adaptability to changing experimental conditions, unproblematic early stopping when goals are achieved, optimal randomness rates, applicability to Bell tests with small violations, and unsurpassed finite-data efficiency. We greatly reduce latencies for producing random bits and formulate an associated rate-tradeoff problem of independent interest. We also show that the latency is determined by an information-theoretic measure of nonlocality rather than the Bell violation.
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quantum randomness generation by Probability Estimation with classical side information
arXiv: Quantum Physics, 2017Co-Authors: Emanuel Knill, Yanbao Zhang, Peter BierhorstAbstract:We develop a framework for certifying randomness from Bell-test trials based on directly estimating the Probability of the measurement outcomes with adaptive test supermartingales. The number of trials need not be predetermined, and one can stop performing trials early, as soon as the desired amount of randomness is extractable. It can be used with arbitrary, partially known and time-dependent probabilities for the random settings choices. Furthermore, it is suitable for application to experimental configurations with low Bell violation per trial, such as current optical loophole-free Bell tests. It is possible to adapt to time-varying experimental parameters. We formulate the framework for the general situation where the trial Probability distributions are constrained to a known set. Randomness expansion with logarithmic settings entropy is possible for many relevant configurations. We implement Probability Estimation numerically and apply it to a representative settings-conditional outcome Probability distribution from an atomic loophole-free Bell test [Rosenfeld et al., Phys. Rev. Lett. 119:010402 (2017), arXiv:1611.04604 (2016)] to illustrate trade-offs between the amount of randomness, error, settings entropy, unknown settings biases, and number of trials. We then show that Probability Estimation yields more randomness from the loophole-free Bell-test data analyzed in [Bierhorst et al., arXiv:1702.05178 (2017)] and tolerates adversarial settings Probability biases.
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quantum randomness generation by Probability Estimation with classical side information
arXiv: Quantum Physics, 2017Co-Authors: Emanuel Knill, Yanbao Zhang, Peter BierhorstAbstract:We develop a framework for certifying randomness from Bell-test trials based on directly estimating the Probability of the measurement outcomes with adaptive test supermartingales. The number of trials need not be predetermined, and one can stop performing trials early, as soon as the desired amount of randomness is extractable. It can be used with arbitrary, partially known and time-dependent probabilities for the random settings choices. Furthermore, it is suitable for application to experimental configurations with low Bell violation per trial, such as current optical loophole-free Bell tests. It is possible to adapt to time-varying experimental parameters. We formulate the framework for the general situation where the trial Probability distributions are constrained to a known set. Randomness expansion with logarithmic settings entropy is possible for many relevant configurations. We implement Probability Estimation numerically and apply it to a representative settings-conditional Probability distribution of the outcomes from an atomic loophole-free Bell test [Rosenfeld et al., Phys. Rev. Lett. 119:010402 (2017), arXiv:1611.04604 (2016)] to illustrate trade-offs between the amount of randomness, error, settings entropy, unknown settings biases, and number of trials. We then show that Probability Estimation yields more randomness from the loophole-free Bell-test data analyzed in [Bierhorst et al., arXiv:1702.05178 (2017)] and tolerates adversarial settings Probability biases.
Foster Provost - One of the best experts on this subject based on the ideXlab platform.
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Active Sampling for Class Probability Estimation and Ranking
Machine Learning, 2004Co-Authors: Maytal Saar-tsechansky, Foster ProvostAbstract:In many cost-sensitive environments class Probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class Probability estimates; however, it often is very costly to obtain training data with class labels. Active learning acquires data incrementally, at each phase identifying especially useful additional data for labeling, and can be used to economize on examples needed for learning. We outline the critical features of an active learner and present a sampling-based active learning method for estimating class probabilities and class-based rankings. BOOTSTRAP-LV identifies particularly informative new data for learning based on the variance in Probability estimates, and uses weighted sampling to account for a potential example's informative value for the rest of the input space. We show empirically that the method reduces the number of data items that must be obtained and labeled, across a wide variety of domains. We investigate the contribution of the components of the algorithm and show that each provides valuable information to help identify informative examples. We also compare BOOTSTRAP-LV with UNCERTAINTY SAMPLING, an existing active learning method designed to maximize classification accuracy. The results show that BOOTSTRAP-LV uses fewer examples to exhibit a certain Estimation accuracy and provide insights to the behavior of the algorithms. Finally, we experiment with another new active sampling algorithm drawing from both UNCERTAINTY SAMPLING and BOOTSTRAP-LV and show that it is significantly more competitive with BOOTSTRAP-LV compared to UNCERTAINTY SAMPLING. The analysis suggests more general implications for improving existing active sampling algorithms for classification.
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active sampling for class Probability Estimation and ranking
Social Science Research Network, 2001Co-Authors: Maytal Saartsechansky, Foster ProvostAbstract:In many cost-sensitive environments class Probability estimates are used by decisionmakers to evaluate the expected utility from a set of alternatives. Supervisedlearning can be used to build class Probability estimates; however, it often is verycostly to obtain training data with class labels. Active sampling acquires data incrementally,at each phase identifying especially useful additional data for labeling,and can be used to economize on examples needed for learning. We outline thecritical features for an active sampling approach and present an active samplingmethod for estimating class probabilities and ranking. BOOTSTRAP-LV identifies particularlyinformative new data for learning based on the variance in Probability estimates,and by accounting for a particular data item's informative value for therest of the input space. We show empirically that the method reduces the numberof data items that must be obtained and labeled, across a wide variety of domains.We investigate the contribution of the components of the algorithm and show thateach provides valuable information to help identify informative examples. We alsocompare BOOTSTRAP-LV with UNCERTAINTY SAMPLING,a n existing active samplingmethod designed to maximize classification accuracy. The results show that BOOTSTRAP-LV uses fewer examples to exhibit a certain class Probability Estimation accuracyand provide insights on the behavior of the algorithms. Finally, to further ourunderstanding of the contributions made by the elements of BOOTSTRAP-LV, we experimentwith a new active sampling algorithm drawing from both UNCERTAINIYSAMPLING and BOOTSTRAP-LV and show that it is significantly more competitivewith BOOTSTRAP-LV compared to UNCERTAINTY SAMPLING. The analysis suggestsmore general implications for improving existing active sampling algorithms forclassification.
Josep Font-segura - One of the best experts on this subject based on the ideXlab platform.
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Importance Sampling for Coded-Modulation Error Probability Estimation
IEEE Transactions on Communications, 2020Co-Authors: Josep Font-segura, Alfonso Martinez, Albert Guillen I FabregasAbstract:This paper proposes an efficient simulation method based on importance sampling to estimate the random-coding error Probability of coded modulation. The technique is valid for complex-valued modulations over Gaussian channels, channels with memory, and naturally extends to fading channels. The simulation method is built on two nested importance samplers to respectively estimate the pairwise error Probability and generate the channel input and output. The effect of the respective number of samples on the overall bias and variance of the estimate of the error Probability is characterized. For a memoryless channel, the estimator is shown to be consistent and with a small variance, growing with the square root of the code length, rather than the exponential growth of a standard Monte Carlo estimator.
Ian H Witten - One of the best experts on this subject based on the ideXlab platform.
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one class classification by combining density and class Probability Estimation
European conference on Machine Learning, 2008Co-Authors: Kathryn Hempstalk, Eibe Frank, Ian H WittenAbstract:One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density Estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class Probability Estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class Probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class Probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.