Previous Conferences & Workshops

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...

In a joint work with Laurent Côté we show the following result. Any Lagrangian plane in the cotangent bundle of an open Riemann surface which coincides with a cotangent fibre outside of some compact subset, is compactly supported Hamiltonian...

During the 2020-2021 academic year, the School had a special program on Geometric and Modular Representation Theory. Geordie Williamson of the University of Sydney was the Distinguished Visiting Professor.

Confirmed senior participants for Term I...

Many challenging problems in modern applications amount to finding relevant results from an enormous output space of potential candidates, for example, finding the best matching product from a large catalog or suggesting related search phrases on a...

Classical algorithms typically provide "one size fits all" performance, and do not leverage properties or patterns in their inputs. A recent line of work aims to address this issue by developing algorithms that use machine learning predictions to...

Suppose you are monitoring discrete events in real time. Can you predict what events will happen in the future, and when? Can you fill in past events that you may have missed? A probability model that supports such reasoning is the neural Hawkes...

A brief review will be provided first on how deep learning has disrupted speech recognition and language processing industries since 2009. Then connections will be drawn between the techniques (deep learning or otherwise) for modeling speech and...