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Institute for Mathematics and its Applications

Machine Learning:Theory and ComputationIMA Annual Thematic Program Workshop

March 26-30, 2012

Description:Over the past two decades, machine learning has evolved from a discipline studied primarily by computer scientists (in particular, in artificial intelligence) to a broader discipline also studied by statisticians, applied mathematicians, and engineers. Today, machine learning is routinely used in commercial systems ranging from speech recognition and computer vision to web mining. As the field has evolved, there has been an increased emphasis on understand-ing the statistical, theoretical, and computational underpinnings of machine learning. This workshop aims to bring together researchers from different disciplines to discuss recent trends and advances in the theoretical and compu-tational aspects of machine learning.

Topics to be discussed at the workshop include the interplay between machine learning (kernel learning, graphical models, online learning, active learning) with (a) statistical modeling and learning theory, (b) theoretical computer science, (c) numerical optimization, (d) topological methods, (e) tensor methods, and (f ) sparse methods. The topics of interest can be structured under three broad categories, depending on whether the focus is on data/geometry, models/algorithms, or analysis/theory. Several important ideas and abstractions, such as online learning, cut across all three categories. The goal of the workshop is to approach core topics in machine learning from all of these diverse perspectives.

Speakers:Deepak Agarwal (Yahoo! Inc.)�����

Anima Anandkumar (University of California, Irvine)��

Vincent��Blondel (Université Catholique de Louvain)�����

Constantine Caramanis (University of Texas at Austin)

Kathleen Carley (Carnegie Mellon University)

Gunnar Carlsson (Stanford University)

Venkat Chandrasekaran (University of California, Berkeley)��

Corrina Cortes (Google Inc.)

Jure Leskovec (Stanford University)

Lek-Heng Lim (University of Chicago)

Susan Murphy (University of Michigan)

Jennifer Neville (Purdue University)

Robert Nowak (University of Wisconsin-Madison)

Pradeep Ravikumar (University of Texas at Austin)

Ben Recht (University of Wisconsin-Madison)

Karl Rohe (University of Wisconsin-Madison)

Cynthia Rudin��(University of Wisconsin-Madison)

Xioatong Shen (University of Minnesota)

Yoram Singer (Google Inc.)

Alex Smola (Yahoo! Inc.)

Nati Srebro (Toyota Technological Institute at Chicago)����

Ambuj Tewari (University of Texas at Austin)

Jean-Philippe Vert (École Nationale Supérieure des Mines de Paris)

Rachel Ward (University of Texas at Austin)

Manfred Warmuth (University of California, Santa Cruz)

Steve Wright (University of Wisconsin-Madison)

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The IMA is an NSF-funded institute

www.ima.umn.edu/2011-2012/W3.26-30.12

Organizers:Arindam Banerjee (Computer Science and Engineering, University of Minnesota)

Inderjit S. Dhillon (Computer Sciences, University of Texas at Austin)

Bin Yu (Statistics/Electrical Engineering & Computer Science, University of California, Berkeley)��