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September 2019

Some New Insights On Transfer Learning

September 20, 2019 @ 11:00 am - 12:00 pm

Samory Kpotufe (Columbia University)

E18-304

Abstract: The problem of transfer and domain adaptation is ubiquitous in machine learning and concerns situations where predictive technologies, trained on a given source dataset, have to be transferred to a new target domain that is somewhat related. For example, transferring voice recognition trained on American English accents to apply to Scottish accents, with minimal retraining. A first challenge is to understand how to properly model the ‘distance’ between source and target domains, viewed as probability distributions over a feature…

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Probabilistic Modeling meets Deep Learning using TensorFlow Probability

September 18, 2019 @ 4:00 pm - 5:00 pm

Brian Patton (Google AI)

E18-304

IDS.190 - Topics in Bayesian Modeling and Computation Speaker: Brian Patton (Google AI) Abstract: TensorFlow Probability provides a toolkit to enable researchers and practitioners to integrate uncertainty with gradient-based deep learning on modern accelerators. In this talk we'll walk through some practical problems addressed using TFP; discuss the high-level interfaces, goals, and principles of the library; and touch on some recent innovations in describing probabilistic graphical models. Time-permitting, we may touch on a couple areas of research interest for the…

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Dynamic Monitoring and Decision Systems (DyMonDS) Framework for Data-Enabled Integration in Complex Electric Energy Systems

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

Marija Ilic (MIT)

32-155

In this talk, we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach, distributed control and optimization problems are formulated so that: (1) the low-level decision-makers optimize cost of local interactions while accounting for their heterogeneous technologies, as well as for their social and risk preferences; and, (2) the higher layer aggregators and coordinators optimize…

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Automated Data Summarization for Scalability in Bayesian Inference

September 11, 2019 @ 4:00 pm - 5:00 pm

Tamara Broderick (MIT)

E18-304

IDS.190 - Topics in Bayesian Modeling and Computation Abstract: Many algorithms take prohibitively long to run on modern, large datasets. But even in complex data sets, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a "coreset") that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time, but…

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GANs, Optimal Transport, and Implicit Density Estimation

September 6, 2019 @ 11:00 am - 12:00 pm

Tengyuan Liang (University of Chicago)

E18-304

Abstract: We first study the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs), which subsumes Wasserstein, Sobolev, and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the implicit density estimation under the adversarial framework. On the parametric end, we establish a theory for general…

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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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