Bohrer Lecture: Liza Levina
BIO: Liza Levina is the Vijay Nair Collegiate Professor and Chair of Statistics at the University of Michigan, as well as affiliated faculty at the Michigan Institute for Data Science and the Center for the Study of Complex Systems. She received her PhD in Statistics from UC Berkeley in 2002, and has been at the University of Michigan since. She is well known for her work on high-dimensional inference and statistical network analysis. She is a recipient of the ASA Noether Young Scholar Award, a fellow of the ASA and the IMS, and a Web of Science Highly Cited Researcher. She was an invited speaker at the 2018 International Congress of Mathematicians and a 2019 IMS Medallion lecturer.
Talk Title: Hierarchical community detection by recursive partitioning
Talk Abstract: Community detection in networks has been extensively studied in the form of finding a single partition into a “correct” number of communities. In large networks, however, a multi-scale hierarchy of communities is much more realistic. We show that a hierarchical tree of communities, obviously more interpretable, is also potentially more accurate and more computationally efficient. We construct this tree with a simple top-down recursive algorithm, at each step splitting the nodes into two communities with a non-iterative spectral algorithm, until a stopping rule suggests there are no more communities. The algorithm is model-free, extremely fast, and requires no tuning other than selecting a stopping rule. We propose a natural model for this setting, a binary tree stochastic block model, and prove that the algorithm correctly recovers the entire community tree under relatively mild assumptions. As a by-product, we obtain explicit and intuitive results for fitting the stochastic block model under model misspecification. We illustrate the algorithm on a statistics papers dataset constructing a highly interpretable tree of statistics research communities, and on a network based on gene co-occurrence in research papers on anemia. Joint work with Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen van de Berge, Purnamrita Sarkar, and Peter Bickel.
Wijsman Lecture: Dean Foster
BIO: Dean received 3 of his 4 degrees from the University of Maryland in the 1980s. Up until a few years ago he had been in academia all his life. But he then left the ivory tower to join Amazon in NYC. His current research interests are mostly around machine learning and optimization. In this talk, he'll touch on some of the work he did on individual sequences (in particular on-line least squares) and calibration.
Talk Title: Falsifiability, calibration and a bit of Reinforcement Learning
Talk Abstract: Multi-armed bandits in the real world likely don't follow any model theory considers. So, how can we tell if we are actually performing well when watching a system play a bandit problem? I'll introduce an idea I'll call "falsifiable bandits". Such a bandit comes with a certificate of performance: showing this certificate is wrong is good enough to say the system is not solving the bandit problem well. On the other hand, any system which actually plays an optimal strategy should easily construct such a certificate.
To get to this result, we'll start with traditional definitions of on-line regret. I'll then talk about betting as a way of showing a system is behaving poorly. Using a magical calibration variable these two ideas can be forced to agree. This allows a system to use an algorithm that provides some minimal protection from being falsified.
Past Bohrer Workshop Keynote Speakers
- 1994 Mark Schervish, Carnegie Mellon University
- 1995 Dan Naiman, Johns Hopkins University
- 1997 Ross Leadbetter, University of North Carolina
- 1998 Dennis Karney, University of Kansas
- 1999 Erich Lehmann, University of California Berkeley
- 2000 David Bartholomew, London School of Economics
- 2001 Gary Koch, University of North Carolina
- 2002 Robert Serfling, University of Texas Arlington
- 2003 Peter Bickel, University of California Berkeley
- 2004 Peter Imrey, Cleveland Clinic Foundation
- 2005 John Marden, University of Illinois
- 2006 Raymond Carroll, Texas A&M University
- 2007 Mary Ellen Bock, Purdue University
- 2008 Ker-Chau Li, UCLA
- 2010 Zhiliang Ying, Columbia University
- 2011 Minge Xie, Rutgers University
- 2012 Xuming He, University of Michigan
- 2013 Yuhong Yang, University of Minnesota
- Sky Andrecheck, Cleveland Indians
- 2014 Hua-Hua Chang, University of Illinois at Urbana-Champaign
- 2015 Cun-Hui Zhang, Rutgers University
- 2016 Lawrence Brown, University of Pennsylvania
- 2017 Edward George, University of Pennsylvania
- 2018 Regina Liu, Rutgers University
Past Wijsman Lecturers
- 2014 Zhiliang Ying, Columbia University
- 2015 John Lafferty, University of Chicago
- 2016 Xihong Lin, Harvard University
- 2017 Nancy Reid, University of Toronto
- 2018 Michael Kosorok, University of North Carolina at Chapel Hill