We present a method for nonparametric regression that performs bandwidth selectionand variable selection simultaneously. The approach is based on the technique of incrementally decreasing the bandwidth in directions wherethe gradient of the estimator with respect to bandwidth is large. When the unknown function satisfies a sparsity condition, our approach avoids the curse of dimensionality, achieving the optimal minimax rateof convergence, up to logarithmic factors, as if the relevant variables wereknown in advance. The method--called rodeo (regularization of derivative expectation operator)--conducts a sequence of hypothesis tests, and is easy to implement. A modified version that replaces hard with soft thresholding effectively solves a sequence of lasso problems.

We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the function isabove and below a given threshold. We develop experiment selection methodsbased on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 α confidence intervals for seven cosmological parameters. Experimentation showsthat the algorithm reduces the computation necessary for the parameter estimation problem by an order of magnitude.

This article summarizes the Conference on Automated Learning and Discovery (CONALD), which took place in June 1998 at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists concerned with decision making based on data. One of the meeting's focal points was the identification of promising research topics, which are discussed toward the end of this article.

At the same time, we are witnessing a healthy increase in research activities on issues related to automated learning and discovery. Although the broad topic of automated change. The progressing computerization of learning and discovery is inherently professional and private life, paired with a cross-disciplinary in nature--it falls right into sharp increase in memory, processing, and networking the intersection of disciplines such as statistics, capabilities of today's computers, computer science, cognitive psychology, robotics, makes it increasingly possible to gather and and its users such as medicine, social sciences, analyze vast amounts of data. For the first time, and public policy--these fields have people all around the world are connected to mostly studied this topic in isolation. Where is each other electronically through the internet, the field, and where is it going?