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March 2021

Testing the I.I.D. assumption online

Vladimir Vovk, Royal Holloway, University of London

March 26 @ 11:00 am - 12:00 pm

Abstract: Mainstream machine learning, despite its recent successes, has a serious drawback: while its state-of-the-art algorithms often produce excellent predictions, they do not provide measures of their accuracy and reliability that would be both practically useful and provably valid. Conformal prediction adapts rank tests, popular in nonparametric statistics, to testing the IID assumption (the observations being independent and identically distributed). This gives us practical measures, provably valid under the IID assumption, of the accuracy and reliability of predictions produced by…

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April 2021

Sampler for the Wasserstein barycenter

Thibaut Le Gouic, MIT

April 2 @ 11:00 am - 12:00 pm

Abstract: Wasserstein barycenters have become a central object in applied optimal transport as a tool to summarize complex objects that can be represented as distributions. Such objects include posterior distributions in Bayesian statistics, functions in functional data analysis and images in graphics. In a nutshell a Wasserstein barycenter is a probability distribution that provides a compelling summary of a finite set of input distributions. While the question of computing Wasserstein barycenters has received significant attention, this talk focuses on a…

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How Can Governments Facilitate the Integration of Newcomers? Building an Evidence and Innovation Agenda for Migration Research

Jens Hainmueller (Stanford University)

April 6 @ 3:00 pm - 4:00 pm
Jens Hainmueller

Please join us on Tuesday, April 6, 2021 at 3:00pm for the Distinguished Speaker Seminar with Jens Hainmueller (Stanford University).

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Functions space view of linear multi-channel convolution networks with bounded weight norm

Suriya Gunasekar, Microsoft Research

April 9 @ 11:00 am - 12:00 pm

Abstract: The magnitude of the weights of a neural network is a fundamental measure of complexity that plays a crucial role in the study of implicit and explicit regularization. For example, in recent work, gradient descent updates in overparameterized models asymptotically lead to solutions that implicitly minimize the ell_2 norm of the parameters of the model, resulting in an inductive bias that is highly architecture dependent. To investigate the properties of learned functions, it is natural to consider a function…

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AI for Healthcare Equity Conference

April 12

The potential of AI to bring equity in healthcare has spurred significant research efforts across academia, industry and government. Racial, gender and socio-economic disparities have traditionally afflicted healthcare systems in ways that are difficult to detect and quantify. New AI technologies, however, provide a platform for change.

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Sample Size Considerations in Precision Medicine

Eric Laber, Duke University

April 16 @ 11:00 am - 12:00 pm

Abstract:  Sequential Multiple Assignment Randomized Trials (SMARTs) are considered the gold standard for estimation and evaluation of treatment regimes. SMARTs are typically sized to ensure sufficient power for a simple comparison, e.g., the comparison of two fixed treatment sequences. Estimation of an optimal treatment regime is conducted as part of a secondary and hypothesis-generating analysis with formal evaluation of the estimated optimal regime deferred to a follow-up trial. However, running a follow-up trial to evaluate an estimated optimal treatment regime…

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Prioritizing genes from genome-wide association studies

Hilary Finucane, Broad Institute

April 23 @ 11:00 am - 12:00 pm

Abstract: Prioritizing likely causal genes from genome-wide association studies (GWAS) is a fundamental problem. There are many methods for GWAS gene prioritization, including methods that map candidate SNPs to their target genes and methods that leverage patterns of enrichment from across the genome. In this talk, I will introduce a new method for leveraging genome-wide patterns of enrichment to prioritize genes at GWAS loci, incorporating information about genes from many sources. I will then discuss the problem of benchmarking gene prioritization methods,…

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Paths from Research to Impact – Workshop

April 30

The Paths from Research to Impact workshop brings together a breadth of experts to highlight how data-driven research on COVID-19 has informed policy decisions and impacted the pandemic response on both local and global scales.

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May 2021

IDSS Distinguished Speaker Seminar with Eliana La Ferrara (Bocconi University)

Eliana La Ferrara (Bocconi University)

May 4 @ 3:00 pm - 4:00 pm

Please join us on Tuesday, May 4, 2021 at 3:00pm for the Distinguished Speaker Seminar with Eliana La Ferrara (Bocconi University).

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Likelihood-Free Frequentist Inference

Ann Lee, Carnegie Mellon University

May 14 @ 11:00 am - 12:00 pm

Abstract: Many areas of the physical, engineering and biological sciences make extensive use of computer simulators to model complex systems. Confidence sets and hypothesis testing are the hallmarks of statistical inference, but classical methods are poorly suited for scientific applications involving complex simulators without a tractable likelihood. Recently, many techniques have been introduced that learn a surrogate likelihood using forward-simulated data, but these methods do not guarantee frequentist confidence sets and tests with nominal coverage and Type I error control,…

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