General Schedule

Invited Talks

Trevor Hastie, Stanford University

Regularization Paths and Coordinate Descent

Abstract
In a statistical world faced with an explosion of data, regularization has become an important ingredient. In many problems, we have many more variables than observations, and the lasso penalty and its hybrids have become increasingly useful. This talk presents some effective algorithms based on coordinate descent for fitting large scale regularization paths for a variety of problems. Joint work with Rob Tibshirani and Jerome Friedman

Michael Schwarz, Yahoo! Research

Internet Advertising and Optimal Auction Design

Abstract
We characterize the optimal (revenue maximizing) auction for sponsored search advertising. We show that a search engine's optimal reserve price is independent of the number of bidders. Using simulations, we consider the changes that result from a search engine's choice of reserve price and from changes in the number of participating advertisers.

Jitendra Malik, UC Berkeley

The Future of Image Search

There are billions of images on the Internet. Today, searching for a desired image is largely based on textual data such as filename or associated text on the web page; not much use is made of the image content. There are good reasons for this. The field of content-based image retrieval, which emerged during the 1990s, focused primarily on color and texture cues. These were easier to model than shape, but they turned out to be much less useful than originally hoped. I shall review some of the recent developments in the field of visual object recognition in the computer vision community that offer greater promise. Much better image features for characterizing shape, advances in machine learning techniques, and the availability of large amounts of training data lie at the heart of these approaches.