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April 2018

LIDS Seminar Series: Jose M. F. Moura

April 24 @ 3:00 pm - 4:00 pm

Jose M F Moura (Carnegie Mellon University)

32-141

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SDSCon 2018

April 20

Bartos Theater, Media Lab

SDSCon 2018 is the second annual celebration of MIT’s statistics and data science community. Organized by MIT’s Statistics and Data Center (SDSC), the conference will feature presentations from established academic leaders, industry innovators, and rising stars in the field. Discussions will cover a wide range of theory and application, representing the latest research and breakthroughs in statistics and data science. SDSC is an MIT-wide focal point for advancing academic programs and research activities in statistics and data science. It was…

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Community-based and Peer-to-peer Electricity Markets

April 18 @ 2:00 pm - 3:00 pm

Pierre Pinson ( Technical University of Denmark )

MIT Building E18, Room 304

Abstract The deployment of distributed renewable generation capacities, new ICT capabilities, as well as a more proactive role of consumers, are all motivating rethinking electricity markets in a more distributed and consumer-centric fashion. After motivating the design of various forms of consumer-centric electricity markets, we will focus on two alternative constructs (which could actually be unified) consisting in community-based and peer-to-peer electricity markets. The mathematical framework for these markets will be described, with focus on negotiation and clearing algorithms in…

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Stochastics and Statistics Seminars: Testing degree corrections in Stochastic Block Models

April 13 @ 11:00 am - 12:00 pm

Subhabrata Sen (Microsoft )

MIT Building E18, Room 304

Abstract:  The community detection problem has attracted signicant attention in re- cent years, and it has been studied extensively under the framework of a Stochas- tic Block Model (SBM). However, it is well-known that SBMs fit real data very poorly, and various extensions have been suggested to replicate characteristics of real data. The recovered community assignments are often sensitive to the model used, and this naturally begs the following question:  Given a network with community structure, how to decide whether…

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Finding Online Extremists in Social Networks

April 10 @ 3:00 pm - 4:00 pm

Tauhid Zaman (MIT)

32-141

Abstract Online extremists in social networks pose a new form of threat to the general public. These extremists range from cyber bullies who harass innocent users to terrorist organizations such as ISIS that use social networks to spread propaganda. Currently, social networks suspend the accounts of such extremists in response to user complaints, but these extremist users simply create new accounts and continue their activities. In this talk, we present a new set of operational capabilities to help authorities mitigate…

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Optimality of Spectral Methods for Ranking, Community Detections and Beyond

April 6 @ 11:00 am - 12:00 pm

Jianqing Fan (Princeton University)

E18-304

Abstract: Spectral methods have been widely used for a large class of challenging problems, ranging from top-K ranking via pairwise comparisons, community detection, factor analysis, among others. Analyses of these spectral methods require super-norm perturbation analysis of top eigenvectors. This allows us to UNIFORMLY approximate elements in eigenvectors by linear functions of the observed random matrix that can be analyzed further. We first establish such an infinity-norm pertubation bound for top eigenvectors and apply the idea to several challenging problems…

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MIT Policy Hackathon: Data to Decisions

April 6 @ 8:00 am - April 8 @ 5:00 pm

Hackathon

48-hour hackathon convened by MIT’s Institute for Data, Systems, and Society that aims to address some of today’s most relevant societal challenges while fostering an interdisciplinary spirit.

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Computational Social Science: Exciting Progress and Future Challenges

April 3 @ 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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March 2018

Statistical theory for deep neural networks with ReLU activation function

March 23 @ 11:00 am - 12:00 pm

Johannes Schmidt-Hieber (Leiden)

Abstract: The universal approximation theorem states that neural networks are capable of approximating any continuous function up to a small error that depends on the size of the network. The expressive power of a network does, however, not guarantee that deep networks perform well on data. For that, control of the statistical estimation risk is needed. In the talk, we derive statistical theory for fitting deep neural networks to data generated from the multivariate nonparametric regression model. It is shown…

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LIDS Seminar Series – Lizhong Zheng

March 20 @ 3:00 pm - 4:00 pm

Lizhong Zheng (MIT)

32-141

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