• Speaker: Daniel Spielman, Yale
• Title: Balancing covariates in randomized experiments using the Gram–Schmidt walk
• Date: 05/21/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of the One World MINDS series: https://sites.google.com/view/minds-seminar/home) In randomized experiments, such as a medical trials, we randomly assign the treatment, such as a drug or a placebo, that each experimental subject receives. Randomization can help us accurately estimate the difference in treatment effects with high probability. We also know that we want the two groups to be similar: ideally the two groups would be similar in every statistic we can measure beforehand. Recent advances in algorithmic discrepancy theory allow us to divide subjects into groups with similar statistics. By exploiting the recent Gram-Schmidt Walk algorithm of Bansal, Dadush, Garg, and Lovett, we can obtain random assignments of low discrepancy. These allow us to obtain more accurate estimates of treatment effects when the information we measure about the subjects is predictive, while also bounding the worst-case behavior when it is not. We will explain the experimental design problem we address, the Gram-Schmidt walk algorithm, and the major ideas behind our analyses. This is joint work with Chris Harshaw, Fredrik Sävje, and Peng Zhang. Paper: https://arxiv.org/abs/1911.03071 Code: https://github.com/crharshaw/GSWDesign.jl

• Speaker: Ben Adcock, Simon Fraser University
• Title: The troublesome kernel: instabilities in deep learning for inverse problems; zoom link @ https://sites.google.com/view/minds-seminar/home
• Date: 06/04/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of One World MINDS seminar: https://sites.google.com/view/minds-seminar/home) \[\] Due to their stunning success in traditional machine learning applications such as classification, techniques based on deep learning have recently begun to be actively investigated for problems in computational science and engineering. One of the key areas at the forefront of this trend is inverse problems, and specifically, inverse problems in imaging. The last few years have witnessed the emergence of many neural network-based algorithms for important imaging modalities such as MRI and X-ray CT. These claim to achieve competitive, and sometimes even superior, performance to current state-of-the-art techniques. However, there is a problem. Techniques based on deep learning are typically unstable. For example, small perturbations in the data can lead to a myriad of artefacts in the recovered images. Such artifacts can be hard to dismiss as obviously unphysical, meaning that this phenomenon has potentially serious consequences for the safe deployment of deep learning in practice. In this talk, I will first showcase the instability phenomenon empirically in a range of examples. I will then focus on its mathematical underpinnings, the consequences of these insights when it comes to potential remedies, and the future possibilities for computing genuinely stable neural networks for inverse problems in imaging. This is joint work with Vegard Antun, Nina M. Gottschling, Anders C. Hansen, Clarice Poon, and Francesco Renna Papers: https://www.pnas.org/content/early/2020/05/08/1907377117 https://arxiv.org/abs/2001.01258

• Speaker: Gitta Kutyniok, TU Berlin
• Title: TBA
• Date: 06/18/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of One World MINDS seminar: https://sites.google.com/view/minds-seminar/home)

• Speaker: Stephane Mallat, Ecole Normal Superieure
• Title: TBA
• Date: 07/02/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of One World MINDS seminar: https://sites.google.com/view/minds-seminar/home)

• Speaker: Helmut Bolcskei, ETH Zurich
• Title: TBA
• Date: 07/16/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of One World MINDS seminar: https://sites.google.com/view/minds-seminar/home)

• Speaker: Mary Wooters, Stanford University
• Title: TBA
• Date: 07/30/2020
• Time: 2:30 PM - 3:30 PM
• Place: 

(Part of One World MINDS seminar: https://sites.google.com/view/minds-seminar/home)