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November 2019

Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox

November 13, 2019 @ 4:00 pm - 5:00 pm

Xiao-Li Meng (Harvard University)

E18-304

Abstract: Advances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed,this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman, 2013). We first use the modeling-what-we-ignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory, which is essentially the same…

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SES PhD Admissions Info Session

November 12, 2019 @ 4:00 pm - 5:30 pm

Ali Jadbabaie (MIT)

E18-304

Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student who introduce the program and answer your questions. Please register in advance.

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SDP Relaxation for Learning Discrete Structures: Optimal Rates, Hidden Integrality, and Semirandom Robustness

November 8, 2019 @ 11:00 am - 12:00 pm

Yudong Chen (Cornell University)

E18-304

Abstract: We consider the problems of learning discrete structures from network data under statistical settings. Popular examples include various block models, Z2 synchronization and mixture models. Semidefinite programming (SDP) relaxation has emerged as a versatile and robust approach to these problems. We show that despite being a relaxation, SDP achieves the optimal Bayes error rate in terms of distance to the target solution. Moreover, SDP relaxation is provably robust under the so-called semirandom model, which frustrates many existing algorithms. Our…

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Probabilistic Inference and Learning with Stein’s Method

November 6, 2019 @ 4:00 pm - 5:00 pm

Lester Mackey (Microsoft Research)

37-212

IDS.190 – Topics in Bayesian Modeling and Computation **PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** Speaker: Lester Mackey (Microsoft Research) Abstract: Stein’s method is a powerful tool from probability theory for bounding the distance between probability distributions.  In this talk, I’ll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures.  I’ll highlight applications to Markov chain sampler selection, goodness-of-fit testing, variational…

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One-shot Information Theory via Poisson Processes

November 6, 2019 @ 4:00 pm - 5:00 pm

Cheuk Ting Li (UC Berkeley)

E18-304

Abstract: In information theory, coding theorems are usually proved in the asymptotic regime where the blocklength tends to infinity. While there are techniques for finite blocklength analysis, they are often more complex than their asymptotic counterparts. In this talk, we study the use of Poisson processes in proving coding theorems, which not only gives sharp one-shot and finite blocklength results, but also gives significantly shorter proofs than conventional asymptotic techniques in some settings. Instead of using fixed-size random codebooks, we…

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Causal Inference in the Age of Big Data

November 4, 2019 @ 4:00 pm - 5:00 pm

Jasjeet Sekhon (UC Berkeley)

E18-304

The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social, behavioral, and medical treatments. With the availability of fine-grained data, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges, Machine Learning (ML) is now being…

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SES PhD Admissions Webinar (updated start time)

November 4, 2019 @ 10:00 am - 11:00 am

Ali Jadbabaie (MIT)

Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty who introduces the program and answers your questions. Please register in advance.

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LIDS@80: A Celebration

November 1, 2019 - November 2, 2019

Tang Building (E51)

We are pleased to announce that registration is now open for the LIDS 80th-anniversary celebration. This free event will take place on November 1-2, 2019 at MIT. Advance registration is required. Registration closes on October 3, 2019.

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October 2019

Using Bagged Posteriors for Robust Inference

October 30, 2019 @ 4:00 pm

Jonathan Huggins (Boston University)

37-212

IDS.190 – Topics in Bayesian Modeling and Computation **PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** Speaker:   Jonathan Huggins (Boston University) Abstract: Standard Bayesian inference is known to be sensitive to misspecification between the model and the data-generating mechanism, leading to unreliable uncertainty quantification and poor predictive performance. However, finding generally applicable and computationally feasible methods for robust Bayesian inference under misspecification has proven to be a difficult challenge. An intriguing approach is…

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FinTech in China and the extension of new organizational firm boundary

October 30, 2019 @ 4:00 pm - 5:00 pm

Zixia Sheng (New Hope Financial Services)

E18-304

Speaker: Zixia Sheng, CEO, New Hope Financial Services Abstract: Recent new technologies (Fintech and 5G) have had a profound impact on extending the boundaries of firms into more complicated financial ecology system. Nowadays in China, a typical traditional loan underwriting procedure within a bank has been fulfilled by different external parties (e.g. online portals, marketing agencies, data vendors, risk modelers, trusts, funds, invest bankers, debt collectors). How new technology could improve information sharing, reduce transaction costs/contractual costs and therefore change…

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