Error message

  • Notice: Undefined index: attributes in context_preprocess_menu_link() (line 247 of /home/web/drupal-7.59/sites/all/modules/context/context.module).
  • Warning: in_array() expects parameter 2 to be array, null given in context_preprocess_menu_link() (line 247 of /home/web/drupal-7.59/sites/all/modules/context/context.module).

Invited Speakers

UC Berkeley
On the Computational and Statistical Interface and "Big Data"

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the statistical and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent
from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results or concentration theorems can be invoked. We present several research vignettes on topics at the computation/statistics interface, an interface that we aim to characterize in terms of theoretical tradeoffs between statistical risk, amount of data and "externalities" such as computation, communication and privacy.
[Joint work with Venkat Chandrasekaran, John Duchi, Martin Wainwright and Yuchen Zhang.]

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics, and has received the ACM/AAAI Allen Newell Award. He is a Fellow of the AAAI, ACM, ASA, CSS, IMS, IEEE and SIAM.

Tel Aviv University
Implementing the "Wisdom of the Crowd"

The “wisdom of the crowds” has become a hot topic in the last decade with the rapid adaptation of the Internet. At the core of the phenomena is that users do not only consume information but also produce it. This dual role of the users leads to a fundamental design question, how to incentivize the users to explore (produce new information) rather than exploit (use existing information).

We provide a first step in understanding this new aspect of the classical tradeoff between exploration and exploitation in the face of agents’ incentives. Our abstraction studies a novel model in which agents arrive sequentially one after the other and each in turn chooses one action from a fixed set of actions to maximize his expected rewards given the information he possesses at the time of arrival. (More concretely, each agent is a two-arm bandit, maximizing his own utility given the information he has observed.)

The information that becomes available affects the incentives of an agent to explore and generate new information. We characterize the optimal disclosure policy of a planner whose goal is to maximizes social welfare. The planner's optimal policy is characterized and shown to be intuitive and very simple to implement. As the number of agents increases the social welfare converges to the optimal welfare of the unconstrained mechanism and the regret is bounded by a constant.

[Based on a joint work with Ilan Kremer and Motty Perry.]

Prof. Yishay Mansour got his PhD from MIT in 1990, following it he was a postdoctoral fellow in Harvard and a Research Staff Member in IBM T. J. Watson Research Center. Since 1992 he is at Tel-Aviv University, where he is currently a Professor of Computer Science and has serves as the first head of the Blavatnik School of Computer Science during 2000-2002. He is currently the director of the Israeli Center of Research Excellence in Algorithms.

Prof. Mansour has a part-time position at MicroSoft Reaserch in Israel, and has held visiting positions with Bell Labs, AT&T research Labs, IBM Research, and Google Research. He has mentored start-ups as Riverhead, which was acquired by Cisco, Ghoonet and Verix.

Prof. Mansour has published over 50 journal papers and over 100 proceeding papers in various areas of computer science with special emphasis on communication networks, machine learning, and algorithmic game theory, and has supervised over a dozen graduate students in those areas.

Prof. Mansour is currently an associate editor in a number of distinguished journals and has been on numerous conference program committees. He was both the program chair of COLT (1998) and served on the COLT steering committee.