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Towards Robust Statistical Learning Theory

E18-304 , United States

Abstract: Real-world data typically do not fit statistical models or satisfy assumptions underlying the theory exactly, hence reducing the number and strictness of these assumptions helps to lessen the gap between the “mathematical” world and the “real” world. The concept of robustness, in particular, robustness to outliers, plays the central role in understanding this gap. The goal…

LIDS Seminar – George Pappas (University of Pennsylvania)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 

Esther Williams in the Harold Holt Memorial Swimming Pool: Some Thoughts on Complexity

E18-304 , United States

IDS.190 – Topics in Bayesian Modeling and Computation Speaker: Daniel Simpson (University of Toronto) Abstract: Abstract: As data becomes more complex and computational modelling becomes more powerful, we rapidly find ourselves beyond the scope of traditional statistical theory. As we venture beyond the traditional thunderdome, we need to think about how to cope with this…

Accurate Simulation-Based Parametric Inference in High Dimensional Settings

E18-304 , United States

Abstract: Accurate estimation and inference in finite sample is important for decision making in many experimental and social fields, especially when the available data are complex, like when they include mixed types of measurements, they are dependent in several ways, there are missing data, outliers, etc. Indeed, the more complex the data (hence the models),…

The Age of Information in Networks: Moments, Distributions, and Sampling

32-155

We examine a source providing status updates to monitors through a network with state defined by a continuous-time finite Markov chain. Using an age of information (AoI) metric, we characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, we derive first-order linear differential equations for…

Communicating uncertainty about facts, numbers and science

32-D643

The claim of a ‘post-truth’ society, in which emotional responses trump balanced consideration of evidence, presents a strong challenge to those who value quantitative and scientific evidence: how can we communicate risks and unavoidable scientific uncertainty in a transparent and trustworthy way? Communication of quantifiable risks has been well-studied, leading to recommendations for using an…

Using Bagged Posteriors for Robust Inference

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.…

FinTech in China and the extension of new organizational firm boundary

E18-304 , United States

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…

LIDS@80: A Celebration

Tang Building (E51) , United States

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.

SES PhD Admissions Webinar (updated start time)

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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