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PrivacyPreserving Data Mining
, 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
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A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decisiontree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose anovel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.
Consistent Estimates Based on Partially Consistent Observations
 Econometrica
, 1948
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Cited by 349 (0 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.
Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.
Much ado about two: reconsidering retransformation and the twopart model in health econometrics
 Journal of Health Economics
, 1998
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How to Tell When Simpler, More Unified, or Less Ad Hoc Theories will Provide More Accurate Predictions. The British Journal for the Philosophy of Science 45
, 1994
"... Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a bacgkround theory. In this paper, we describe a result due to Akaike [1973], which ..."
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Cited by 117 (32 self)
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Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a bacgkround theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific
Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells
 J. Neumphysiol
, 1998
"... such as the orientation of a line in the visual field or the location of Two main goals for reconstruction are approached in this the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which paper. The first goal is technical and ..."
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Cited by 107 (6 self)
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such as the orientation of a line in the visual field or the location of Two main goals for reconstruction are approached in this the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which paper. The first goal is technical and is exemplified by the the physical variables are estimated from observed neural activity. population vector method applied to motor cortical activities Reconstruction is useful first in quantifying how much information during various reaching tasks (Georgopoulos et al. 1986, 1989; about the physical variables is present in the population and, second, Schwartz 1994) and the template matching method applied to in providing insight into how the brain might use distributed represen disparity selective cells in the visual cortex (Lehky and Sejnowtations in solving related computational problems such as visual ob ski 1990) and hippocampal place cells during rapid learning of ject recognition and spatial navigation. Two classes of reconstruction place fields in a novel environment (Wilson and McNaughton methods, namely, probabilistic or Bayesian methods and basis func 1993). In these examples, reconstruction extracts information tion methods, are discussed. They include important existing methods from noisy neuronal population activity and transforms it to a
The Surprise Element: Jumps in Interest Rates
 Journal of Econometrics
, 2002
"... Abstract. That information surprises result in discontinuous interest rates is no surprise to participants in the bond markets. We develop a class of PoissonGaussian models of the Fed Funds rate to capture surprise effects, and show that these models offer a good statistical description of short ra ..."
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Cited by 104 (2 self)
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Abstract. That information surprises result in discontinuous interest rates is no surprise to participants in the bond markets. We develop a class of PoissonGaussian models of the Fed Funds rate to capture surprise effects, and show that these models offer a good statistical description of short rate behavior, and are useful in understanding many empirical phenomena. Estimators are used based on analytical derivations of the characteristic functions and moments of jumpdiffusion stochastic processes for a range of jump distributions, and are extended to discretetime models. Jump (Poisson) processes capture empirical features of the data which would not be captured by Gaussian models, and there is strong evidence that existing models would be wellenhanced by jump and ARCHtype processes. The analytical and empirical methods in the paper support many applications, such as testing for Fed intervention effects, which are shown to be an important source of surprise jumps in interest rates. The jump model is shown to mitigate the nonlinearity of interest rate drifts, so prevalent in purediffusion models. Dayofweek effects are modelled explicitly, and the jump model provides evidence of bond market overreaction, rejecting the martingale hypothesis for interest rates. Jump models mixed with Markov switching processes predicate that conditioning on regime is important in determining short rate behavior.
Approximate NView Stereo
 in Proc. European Conf. on Computer Vision
, 2000
"... . This paper introduces a new multiview reconstruction problem called approximate Nview stereo. The goal of this problem is to recover a oneparameter family of volumes that are increasingly tighter supersets of an unknown, arbitrarilyshaped 3D scene. By studying 3D shapes that reproduce the in ..."
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Cited by 75 (5 self)
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. This paper introduces a new multiview reconstruction problem called approximate Nview stereo. The goal of this problem is to recover a oneparameter family of volumes that are increasingly tighter supersets of an unknown, arbitrarilyshaped 3D scene. By studying 3D shapes that reproduce the input photographs up to a special image transformation called a shuffle transformation,we prove that (1) these shapes can be organized hierarchically into nested supersets of the scene, and (2) they can be computed using a simple algorithm called Approximate Space Carving that is provablycorrect for arbitrary discrete scenes (i.e., for unknown, arbitrarilyshaped Lambertian scenes that are defined by a finite set of voxels and are viewed from N arbitrarilydistributed viewpoints inside or around them). The approach is specifically designed to attack practical reconstruction problems, including (1) recovering shape from images with inaccurate calibration information, and (2) building ...