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The Stata Center (32-141)
32 Vassar Street, Cambridge, MA 02139 United States

May 2018

IDSS Distinguished Seminar – Conflict in Networks: The Rise and Fall of Empires

May 1, 2018 @ 4:00 pm - 5:00 pm

Sanjeev Goyal (University of Cambridge)

MIT Building 32, Room 141

Abstract In the study of war, a recurring observation is that conflict between two opponents is shaped by third parties. The actions of these parties are in turn influenced by other proximate players. These considerations lead us to propose a model of conflict in a network. We study the influence of resources, technology, and the network of connections on the dynamics of war and the prospects of peace. Bio Sanjeev Goyal is Professor of Economics at the University of Cambridge…

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April 2018

Computational Social Science: Exciting Progress and Future Challenges

April 3, 2018 @ 4:00 pm - 5:00 pm

Duncan Watts (Microsoft Research, NYC)

MIT Building 32, Room 141

 Abstract The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: “computational social science.” In this talk I highlight two areas of research that would not have been possible just a handful of years ago: first, using “big data” to study social contagion on networks; and second, using virtual labs to extend the scale, duration, and…

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February 2018

Machine Learning and Causal Inference

February 6, 2018 @ 4:00 pm - 5:00 pm

Susan Athey (Stanford University)

MIT Building 32, Room 141

Abstract: This talk will review a series of recent papers that develop new methods based on machine learning methods to approach problems of causal inference, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments, environments with unconfoundedness, instrumental variables, and panel data will be considered. Bio: Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford,…

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December 2017

IDSS Distinguished Seminar – Essential Concepts of Causal Inference: A Remarkable History

December 12, 2017 @ 4:30 pm - 5:30 pm

Donald Rubin (Harvard University)

MIT Building 32, Room 141

  Abstract I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding, to me at least, was tied to…

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Regularized Nonlinear Acceleration

December 5, 2017 @ 4:00 pm - 5:00 pm

Alexandre d’Aspremont (École Normale Supérieure)

MIT Building 32, Room 141

We describe a convergence acceleration technique for generic optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method. The weights in this average are computed via a simple linear system, whose solution can be updated online. This acceleration scheme runs in parallel to the base algorithm, providing improved estimates of the solution on the fly, while the original optimization method is running. Numerical experiments are detailed on classical classification…

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November 2017

Comparison Lemmas, Non-Smooth Convex Optimization and Structured Signal Recovery

November 29, 2017 @ 4:00 pm - 5:00 pm

Babak Hassibi (California Institute of Technology)

MIT Building 32, Room 141

In the past couple of decades, non-smooth convex optimization has emerged as a powerful tool for the recovery of structured signals (sparse, low rank, finite constellation, etc.) from possibly noisy measurements in a variety applications in statistics, signal processing and machine learning. While the algorithms (basis pursuit, LASSO, etc.) are often fairly well established, rigorous frameworks for the exact analysis of the performance of such methods are only just emerging. The talk will introduce and describe a fairly general theory…

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Quantum Limits on the Information Carried by Electromagnetic Radiation

November 14, 2017 @ 4:00 pm - 5:00 pm

Massimo Franceschetti (University of California, San Diego)

MIT Building 32, Room 141

In many practical applications information is conveyed by means of electromagnetic radiation and a natural question concerns the fundamental limits of this process. Identifying information with entropy, one can ask about the maximum amount of entropy associated to the propagating wave. The standard statistical physics approach to compute entropy is to take the logarithm of the number of possible energy states of a system. Since any continuum field can assume an uncountably infinite number of energy configurations, the approach underlying…

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Social Network Experiments – Nicholas Christakis (Yale University)

November 7, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

The Institute of Data, Systems, and Society host monthly talks by academic and industry leaders from around the world for the IDSS Distinguished Lecture series.

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October 2017

Regularized Nonlinear Acceleration

October 24, 2017 @ 4:00 pm - 5:00 pm

Alexandre Tsybakov (École Normale Supérieure)

MIT Building 32, Room 141

We describe a convergence acceleration technique for generic optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method. The weights in this average are computed via a simple linear system, whose solution can be updated online. This acceleration scheme runs in parallel to the base algorithm, providing improved estimates of the solution on the fly, while the original optimization method is running. Numerical experiments are detailed on classical classification…

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The Maps Inside Your Head

October 17, 2017 @ 4:00 pm - 5:00 pm

Vijay Balasubramanian (University of Pennsylvania)

MIT Building 32, Room 141

How do our brains make sense of a complex and unpredictable world? In this talk, I will discuss an information theory approach to the neural topography of information processing in the brain. First I will review the brain's architecture, and how neural circuits map out the sensory and cognitive worlds. Then I will describe how highly complex sensory and cognitive tasks are carried out by the cooperative action of many specialized neurons and circuits, each of which has a simple…

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IDSS Distinguished Seminar Series: Latanya Sweeney (Harvard University)

October 3, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

How Technology Design will Dictate Our Civic Future

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September 2017

Networking for Big Data: Theory and Optimization for NDN

September 19, 2017 @ 4:00 pm - 5:00 pm

Edmund Yeh (Northeastern University)

MIT Building 32, Room 141

The advent of Big Data is stimulating the development of new networking architectures which facilitate the acquisition, transmission, storage, and computation of data. In particular, Named Data Networking (NDN) is an emerging content-centric networking architecture which focuses on enabling end users to obtain the data they want, rather than to communicate with specific nodes. By naming content instead of their locations, NDN transforms data into a first-class network entity. In this talk, we present a new analytical and design framework…

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LIDS Seminar Series: Edmund Yeh (Northeastern University)

September 19, 2017 @ 4:00 pm - 5:00 pm

MIT Building 32, Room 141

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Fast and Slow Learning from Reviews

September 12, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

Speaker: Daron Acemoglu (MIT) Many online platforms present summaries of reviews by previous users. Even though such reviews could be useful, previous users leaving reviews are typically a selected sample of those who have purchased the good in question, and may consequently have a biased assessment. In this paper, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the…

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