I have enjoyed reading the work of each of these authors over the years, so it is a real pleasure to have this opportunity to contribute to the discussion of this collaboration. The geometry of LARS furnishes an elegant bridge between the Lasso and Stagewise regression, methods that I would not have suspected to be so related. Toward my own interests, LARS offers a rather different way to const...

Data mining has brought a new philosophy in data analysis that is primarily driven by computational efficiency and predictive performance. In this paper we attempt to show that while this new philosophy opens new horizons, these new techniques may themselves gain much when coupled with traditional statistical reasoning. For example, I recently introduced some sociologists of the University of G...

Starting with a review of some classical quantitative methods for modeling customer behavior in the brand choice situation, some new methods are explained which are based on recently developed techniques from data mining and artificial intelligence: boosting and/or stacking neural network models. The main advantage of these new methods is the gain in predictive performance that is often achieve...

Two experiments examined the impact of causal relations between features on categorization by adults and 5-6-year-old children. Participants learned about artificial categories containing instances with two causally related features and two non-causal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression w...

Social and personality psychologists have always embraced a creative diversity of methods, ranging from participant observation (Festinger, Riecken, & Schachter, 1956) to psychometric test construction (Osgood, Suci, & Tannenbaum, 1957), using verbal, behavioral, and physiological measures in laboratory and field settings. We have invented new research designs (Campbell & Stanley, 1966) and new...

Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three calibration methods for correcting this distortion: Platt Scaling, Isotonic Regre...

In the existing methods for solving matrix completion, such as singular value thresholding (SVT), soft-impute and fixed point continuation (FPCA) algorithms, it is typically required to repeatedly implement singular value decompositions (SVD) of matrices. When the size of the matrix in question is large, the computational complexity of finding a solution is costly. To reduce this expensive comp...

In the existing methods for solving matrix completion, such as singular value thresholding (SVT), soft-impute and fixed point continuation (FPCA) algorithms, it is typically required to repeatedly implement singular value decompositions (SVD) of matrices. When the size of the matrix in question is large, the computational complexity of finding a solution is costly. To reduce this expensive comp...

In this paper, we consider the problem of structure recovery in Markov Network over Gaussian variables, that is equivalent to finding the zero-pattern of the sparse inverse covariance matrix. Recently proposed l1-regularized optimization methods result into convex problems that can be solved optimally and efficiently. However, the accuracy such methods can be quite sensitive to the choice of re...

People seem to have a rather rich repertoire of tactics for regulating moods and dealing with unpleasant events. The current work examines one such tactic. It suggests that in order to render a disappointing reality more palatable, people sometimes change the perceived probabilities of relevant events post-facto, so that the disappointing reality appears almost inevitable and the more positive ...