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May 2019

Learning for Dynamics and Control (L4DC)

May 30, 2019 - May 31, 2019

32-123

Over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world. This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations of our discipline. From a machine…

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Conference on Synthetic Controls and Related Methods

May 20, 2019 - May 21, 2019

E18-304

Organizers are Alberto Abadie (MIT), Victor Chernozhukov (MIT), and Guido Imbens (Stanford University). The program is posted here. Participation by invitation only.

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Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

May 14, 2019 @ 4:00 pm - 5:00 pm

Mihaela van der Schaar (University of California, Los Angeles)

32-155

The overarching goal of my research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms, and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways, better utilize resources & reduce costs; and inform public health. To do this, Prof. van der Schaar is creating what she calls Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning, AI…

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Data Science and Big Data Analytics: Making Data-Driven Decisions

May 13, 2019

Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May 13, 2019.

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Counting and sampling at low temperatures

May 10, 2019 @ 8:00 am - 5:00 pm

Will Perkins (University of Illinois at Chicago)

E18-304

Abstract: We consider the problem of efficient sampling from the hard-core and Potts models from statistical physics. On certain families of graphs, phase transitions in the underlying physics model are linked to changes in the performance of some sampling algorithms, including Markov chains. We develop new sampling and counting algorithms that exploit the phase transition phenomenon and work efficiently on lattices (and bipartite expander graphs) at sufficiently low temperatures in the phase coexistence regime. Our algorithms are based on Pirogov-Sinai…

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Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model

May 8, 2019 @ 3:00 pm - 4:00 pm

Espen Flo Boedal (Norwegian University of Science and Technology)

32 – LIDS Lounge

Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making investment decisions as it significantly impacts the value of flexible resources. In this work we present a method for using duals from a rolling horizon operational model, with wind power uncertainty and market representations, to represent power system operation in an investment problem. The method…

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Design and Analysis of Two-Stage Randomized Experiments

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

Kosuke Imai (Harvard University)

E18-304

Abstract: In many social science experiments, subjects often interact with each other and as a result, one unit's treatment can influence the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. In this talk, we will discuss two-stage randomized experiments, which enable the identification of the average spillover effects as well as that of the average direct effect of one's own treatment. In particular,…

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Stochastics and Statistics Seminar Series

May 3, 2019 @ 11:00 am - 12:00 pm

Tracy Ke (Harvard University)

E18-304

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Generalization and Learning under Dobrushin’s Condition

May 1, 2019 @ 3:00 pm - 4:00 pm

Yuval Dagan (EECS)

32 – LIDS Lounge

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures, which appropriately extend Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data, our work is motivated by settings where…

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April 2019

On Coupling Methods for Nonlinear Filtering and Smoothing

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

Youssef Marzouk (MIT)

32-155

Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability distributions enable new solutions to this problem. We first consider filtering in high-dimensional models with nonlinear (potentially chaotic) dynamics and sparse observations in space and time. While the ensemble Kalman filter (EnKF) yields robust ensemble approximations of the filtering distribution in this setting, it is limited by linear forecast-to-analysis transformations. To generalize…

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