Previous Conferences & Workshops

This talk will form part of a series of three talks focusing on Broué’s Abelian Defect Group Conjecture, which concerns the modular representation theory of finite groups. We will pay particular attention here to the ‘geometric’ form of the...

Abstract: Recent advances in biotechnology and genomics have generated dizzying amounts of large, noisy, and sparse datasets that require concomitant development of machine learning methods. The analyses of single-cell RNA-seq data have driven the...

Abstract: Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled...

Abstract: Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this...

Abstract: In many applications, it is of interest to cluster subjects based on very high-dimensional data, often in the presence of missing data. Although discrete mixture models are routinely used, we demonstrate pitfalls in high-dimensional...

Abstract: In today’s data-driven society, human beings still make most critical decisions and yet they are increasingly utilizing recommendations produced by statistical and machine learning methods. Given the prevalence of this approach in many...

Abstract: An abundant literature addresses missing data in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing values appear in...

Abstract: There is a very extensive literature of statistical methods for the analysis of data with missing values. I'll provide a historical overview, including ad-hoc approaches, statistical models and conditions for ignoring the missingness...

The workshop was organized by Laura Balzano (IAS/University of Michigan), Bianca Dumitrascu (IAS/ SAMSI), and Boaz Nadler (IAS/Weizmann Institute of Science) and will take place virtually.

Missing data is ubiquitous in statistical...