March 2021
Testing the I.I.D. assumption online
Vladimir Vovk, Royal Holloway, University of London
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…
Find out more »April 2021
Sampler for the Wasserstein barycenter
Thibaut Le Gouic, MIT
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…
Find out more »How Can Governments Facilitate the Integration of Newcomers? Building an Evidence and Innovation Agenda for Migration Research
Jens Hainmueller (Stanford University)
Please join us on Tuesday, April 6, 2021 at 3:00pm for the Distinguished Speaker Seminar with Jens Hainmueller (Stanford University).
Find out more »Functions space view of linear multi-channel convolution networks with bounded weight norm
Suriya Gunasekar, Microsoft Research
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…
Find out more »AI for Healthcare Equity Conference
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.
Find out more »Sample Size Considerations in Precision Medicine
Eric Laber, Duke University
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…
Find out more »Prioritizing genes from genome-wide association studies
Hilary Finucane, Broad Institute
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,…
Find out more »Paths from Research to Impact – Workshop
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.
Find out more »May 2021
IDSS Distinguished Speaker Seminar with Eliana La Ferrara (Bocconi University)
Eliana La Ferrara (Bocconi University)
Please join us on Tuesday, May 4, 2021 at 3:00pm for the Distinguished Speaker Seminar with Eliana La Ferrara (Bocconi University).
Find out more »Likelihood-Free Frequentist Inference
Ann Lee, Carnegie Mellon University
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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