Past Events
Events List Navigation
Conference on Synthetic Controls and Related Methods
E18-304
Organizers are Alberto Abadie (MIT), Victor Chernozhukov (MIT), and Guido Imbens (Stanford University). The program is posted here. Participation by invitation only.
Find out more »Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
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…
Find out more »Data Science and Big Data Analytics: Making Data-Driven Decisions
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.
Find out more »Counting and sampling at low temperatures
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…
Find out more »Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model
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…
Find out more »Design and Analysis of Two-Stage Randomized Experiments
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,…
Find out more »Stochastics and Statistics Seminar Series
Tracy Ke (Harvard University)
E18-304
Generalization and Learning under Dobrushin’s Condition
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…
Find out more »On Coupling Methods for Nonlinear Filtering and Smoothing
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…
Find out more »Robust Estimation: Optimal Rates, Computation and Adaptation
Chao Gao (University of Chicago)
E18-304
Abstract: Chao Gao will discuss the problem of statistical estimation with contaminated data. In the first part of the talk, I will discuss depth-based approaches that achieve minimax rates in various problems. In general, the minimax rate of a given problem with contamination consists of two terms: the statistical complexity without contamination, and the contamination effect in the form of modulus of continuity. In the second part of the talk, I will discuss computational challenges of these depth-based estimators. An…
Find out more »