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October 2019

LIDS Seminar – George Pappas (University of Pennsylvania)

October 21, 2019 @ 4:00 pm - 5:00 pm

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. 

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Towards Robust Statistical Learning Theory

October 18, 2019 @ 11:00 am - 12:00 pm

Stanislav Minsker (University of Southern California)

E18-304

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 of the talk is to introduce the principles and robust algorithms based on these principles that can be applied in the general framework of statistical…

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Markov Chain Monte Carlo Methods and Some Attempts at Parallelizing Them

October 16, 2019 @ 4:00 pm - 5:00 pm

Pierre E. Jacob (Harvard University)

E18-304

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: MCMC methods yield approximations that converge to quantities of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal: it stands at odds with current trends in computing hardware. Namely, it would often be computationally preferable to run many short chains in parallel, but such an approach is flawed because of the so-called “burn-in” bias.  This talk will first describe that issue and some known…

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SES PhD Admissions Webinar

October 16, 2019 @ 9:00 am - 10:00 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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The Planted Matching Problem

October 11, 2019 @ 11:00 am - 12:00 pm

Cristopher Moore (Santa Fe Institute)

E18-304

Abstract: What happens when an optimization problem has a good solution built into it, but which is partly obscured by randomness? Here we revisit a classic polynomial-time problem, the minimum perfect matching problem on bipartite graphs. If the edges have random weights in , Mézard and Parisi — and then Aldous, rigorously — showed that the minimum matching has expected weight zeta(2) = pi^2/6. We consider a “planted” version where a particular matching has weights drawn from an exponential distribution…

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Probabilistic Programming and Artificial Intelligence

October 9, 2019 @ 4:00 pm - 5:00 pm

Vikash Mansinghka (MIT)

E18-304

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: Probabilistic programming is an emerging field at the intersection of programming languages, probability theory, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data that is too small and/or messy for standard approaches from machine learning and…

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Theoretical Foundations of Active Machine Learning

October 7, 2019 @ 4:00 pm - 5:00 pm

Rob Nowak (University of Wisconsin - Madison)

E18-304

Title: Theoretical Foundations of Active Machine Learning Abstract: The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text, but they must be trained with more images and text than a person can see in nearly a lifetime.  The computational complexity of training has been offset by recent technological advances, but the cost of training data is measured in…

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Behavior of the Gibbs Sampler in the Imbalanced Case/Bias Correction from Daily Min and Max Temperature Measurements

October 2, 2019 @ 4:00 pm - 5:00 pm

Natesh Pillai (Harvard University)

E18-304

IDS.190 Topics in Bayesian Modeling and Computation *Note:  The speaker this week will give two shorter talks within the usual session Title: Behavior of the Gibbs sampler in the imbalanced case Abstract:   Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty. However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and…

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Data-driven Coordination of Distributed Energy Resources

October 1, 2019 @ 4:00 pm - 5:00 pm

Alejandro Dominguez-Garcia (University of Illinois at Urbana-Champaign)

32-155

The integration of distributed energy resources (DERs), e.g., rooftop photovoltaics installations, electric energy storage devices, and flexible loads, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which the DERs are connected, but also creates new opportunities for the provision of grid services. In the first part of the talk, we discuss one such operational challenge—ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem, we propose a…

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September 2019

Selection and Endogenous Bias in Studies of Health Behaviors

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

Emily Oster (Brown University)

E18-304

Abstract: Studies of health behaviors using observational data are prone to bias from selection in behavior choices. How important are these biases? Are they dynamic - that is, are they influenced by the recommendations we make? Are there formal assumptions under which we can use information we have about selection on observed variables to learn about the possible bias from unobserved selection? About the Speaker: Emily Oster is a professor of economics. Prior to coming to Brown she was an…

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