Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013
As datasets become larger, more complex, and more available to diverse groups of analysts, it wou... more As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in this direction, its level of automation is limited by the difficulty of evaluating its finite sample performance and even its asymptotic consistency. Thus, we present here a general diagnostic procedure which directly and automatically evaluates the accuracy of the bootstrap's outputs, determining whether or not the bootstrap is performing satisfactorily when applied to a given dataset and estimator. We show that our proposed diagnostic is effective via an extensive empirical evaluation on a variety of estimators and simulated and real datasets, including a real-world query workload from Conviva, Inc. involving 1.7TB of data (i.e., approximately 0.5 billion data points).
Influence of small Constant-Power-Loads on the power supply system of an aircraft
2013 International Conference-Workshop Compatibility And Power Electronics, 2013
ABSTRACT Today most electrical consumers in an aircraft grid are electronic loads. Compared to re... more ABSTRACT Today most electrical consumers in an aircraft grid are electronic loads. Compared to resistive or motoric loads these loads have constant power consumption, which is independent of the supply voltage. This characteristic may have a negative effect on the stability of a power supply system. In addition real Constant-Power-Loads can have some other peculiarities. Depending of the topology or the supplied voltage type (AC or DC) the dynamic characteristic differs. This paper deals with a characterization of 4 types of Constant-Power-Loads in an actual and future aircraft grid. The investigations are focused to the dynamic characterization of different loads. Theoretical investigations are combined with measurements in an artificial aircraft grid with realistic supply voltage variations.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
Complex data can be grouped and interpreted in many different ways. Most existing clustering algo... more Complex data can be grouped and interpreted in many different ways. Most existing clustering algorithms, however, only find one clustering solution, and provide little guidance to data analysts who may not be satisfied with that single clustering and may wish to explore alternatives. We introduce a novel approach that provides several clustering solutions to the user for the purposes of exploratory data analysis. Our approach additionally captures the notion that alternative clusterings may reside in different subspaces (or views). We present an algorithm that simultaneously finds these subspaces and the corresponding clusterings. The algorithm is based on an optimization procedure that incorporates terms for cluster quality and novelty relative to previously discovered clustering solutions. We present a range of experiments that compare our approach to alternatives and explore the connections between simultaneous and iterative modes of discovery of multiple clusterings.
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linea... more Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binary classification informative vector machine (IVM) [6] to multiple classes. In contrast to earlier efficient approaches to kernel-based non-binary classification, our method is a principled approximation to Bayesian inference which yields valid uncertainty estimates and allows for hyperparameter estimation via marginal likelihood maximization. While most earlier proposals suggest fitting independent binary discriminants to heuristically chosen partitions of the data and combining these in a heuristic manner, our method operates jointly on the data for all classes. Crucially, we still achieve a linear scaling in both the number of classes and the number of training points. 1 Traditional methods such as cross-validation are not useful, because different covariance functions (kernels) should be used for every class leading to at least O(C) hyperparameters. 2 In the light of a confused anonymous referee, it seems necessary to clarify our scaling statements. Under the assumption that C < d n the dominant contribution to the scaling is O(n C d 2). As with any other method in this domain, there are additional O(d 3), O(d C 3) and other contributions which are subdominant under these assumptions. Especially, our method cannot be used in a large C domain without further modifications not discussed here. Unless otherwise said, claims about the scaling behaviour concentrate on the dominant term (under these assumptions) which is linear in the training set size n.
Abstract: Mean-field variational inference is a method for approximate Bayesian posterior inferen... more Abstract: Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by maximizing a lower bound on the marginal likelihood. This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution. Often not all integrals are in closed form, which is typically handled by using a lower bound. We present an alternative algorithm based on stochastic optimization that allows for direct optimization of ...
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013
As datasets become larger, more complex, and more available to diverse groups of analysts, it wou... more As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in this direction, its level of automation is limited by the difficulty of evaluating its finite sample performance and even its asymptotic consistency. Thus, we present here a general diagnostic procedure which directly and automatically evaluates the accuracy of the bootstrap's outputs, determining whether or not the bootstrap is performing satisfactorily when applied to a given dataset and estimator. We show that our proposed diagnostic is effective via an extensive empirical evaluation on a variety of estimators and simulated and real datasets, including a real-world query workload from Conviva, Inc. involving 1.7TB of data (i.e., approximately 0.5 billion data points).
Influence of small Constant-Power-Loads on the power supply system of an aircraft
2013 International Conference-Workshop Compatibility And Power Electronics, 2013
ABSTRACT Today most electrical consumers in an aircraft grid are electronic loads. Compared to re... more ABSTRACT Today most electrical consumers in an aircraft grid are electronic loads. Compared to resistive or motoric loads these loads have constant power consumption, which is independent of the supply voltage. This characteristic may have a negative effect on the stability of a power supply system. In addition real Constant-Power-Loads can have some other peculiarities. Depending of the topology or the supplied voltage type (AC or DC) the dynamic characteristic differs. This paper deals with a characterization of 4 types of Constant-Power-Loads in an actual and future aircraft grid. The investigations are focused to the dynamic characterization of different loads. Theoretical investigations are combined with measurements in an artificial aircraft grid with realistic supply voltage variations.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
Complex data can be grouped and interpreted in many different ways. Most existing clustering algo... more Complex data can be grouped and interpreted in many different ways. Most existing clustering algorithms, however, only find one clustering solution, and provide little guidance to data analysts who may not be satisfied with that single clustering and may wish to explore alternatives. We introduce a novel approach that provides several clustering solutions to the user for the purposes of exploratory data analysis. Our approach additionally captures the notion that alternative clusterings may reside in different subspaces (or views). We present an algorithm that simultaneously finds these subspaces and the corresponding clusterings. The algorithm is based on an optimization procedure that incorporates terms for cluster quality and novelty relative to previously discovered clustering solutions. We present a range of experiments that compare our approach to alternatives and explore the connections between simultaneous and iterative modes of discovery of multiple clusterings.
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linea... more Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binary classification informative vector machine (IVM) [6] to multiple classes. In contrast to earlier efficient approaches to kernel-based non-binary classification, our method is a principled approximation to Bayesian inference which yields valid uncertainty estimates and allows for hyperparameter estimation via marginal likelihood maximization. While most earlier proposals suggest fitting independent binary discriminants to heuristically chosen partitions of the data and combining these in a heuristic manner, our method operates jointly on the data for all classes. Crucially, we still achieve a linear scaling in both the number of classes and the number of training points. 1 Traditional methods such as cross-validation are not useful, because different covariance functions (kernels) should be used for every class leading to at least O(C) hyperparameters. 2 In the light of a confused anonymous referee, it seems necessary to clarify our scaling statements. Under the assumption that C < d n the dominant contribution to the scaling is O(n C d 2). As with any other method in this domain, there are additional O(d 3), O(d C 3) and other contributions which are subdominant under these assumptions. Especially, our method cannot be used in a large C domain without further modifications not discussed here. Unless otherwise said, claims about the scaling behaviour concentrate on the dominant term (under these assumptions) which is linear in the training set size n.
Abstract: Mean-field variational inference is a method for approximate Bayesian posterior inferen... more Abstract: Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by maximizing a lower bound on the marginal likelihood. This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution. Often not all integrals are in closed form, which is typically handled by using a lower bound. We present an alternative algorithm based on stochastic optimization that allows for direct optimization of ...
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