Please join us for the one-day symposium Beyond Fairness: Big Data, Racial Justice & Housing on Wednesday, April 27, 2022 from 9:00-4:30pm at the MIT Media Lab (E14-674). Organized by the ICSR Housing vertical team, this one-day symposium explores the intersection of data, algorithms and AI in relation to housing insecurity, home ownership and evictions. Please see icsr-fairhousing.mit.edu for more information.
Abstract: Under the current Swiss law, taxpayers can deduct charitable donations from their individual’s taxable income subject to a 20%-ceiling. This deductible ceiling was increased at the communal and cantonal level from a previous 5%-ceiling in 2009. The goal of the reform was boosting charitable giving to non-profit entities. However, the effects of this reform, and more generally of the existing Swiss system of tax deductions for charitable giving has never been empirically studied. The aim of this work is…
Please join us on Monday, May 2 at 4:00pm for the IDSS Distinguished Speaker Seminar with Elias Bareinboim (Columbia University).
Abstract: This talk will cover recent work in our group developing and applying algorithms to simulate rare events in atmospheric science and other areas. I will review a rare event simulation scheme that biases model simulations toward the rare event of interest by preferentially duplicating simulations making progress toward the event and removing others. I will describe applications of this approach to rapid intensification of tropical cyclones and instability of Mercury's orbit with an emphasis on the elements of algorithm…
Abstract: Many existing literature on bandits and reinforcement learning assume a linear reward/value function, but what happens if the reward is non-linear? Two curious phenomena arise for non-linear bandits: first, in addition to the "learning phase" with a standard \Theta(\sqrt(T)) regret, there is an "initialization phase" with a fixed cost determined by the reward function; second, achieving the smallest cost of the initialization phase requires new learning algorithms other than traditional ones such as UCB. For a special family of…
ABSTRACT Recent advances in data collection have made sports an attractive testing ground for new analyses and algorithms, and a fascinating controlled microcosm in which to explore social interactions. In this talk I will describe two studies in this arena: one related to public health and the pandemic and one related to decision-making in basketball. In the first, I will discuss what can be learned from the natural experiments that were (fortuitously) run in America football stadiums. During the 2020…
Abstract: Sortition is a storied paradigm of democracy built on the idea of choosing representatives through lotteries instead of elections. In recent years this idea has found renewed popularity in the form of citizens’ assemblies, which bring together randomly selected people from all walks of life to discuss key questions and deliver policy recommendations. A principled approach to sortition, however, must resolve the tension between two competing requirements: that the demographic composition of citizens’ assemblies reflect the general population and…
Abstract: In this work, we develop a comparison procedure for the Modified log-Sobolev Inequality (MLSI) constants of two reversible Markov chains on a finite state space. As an application, we provide a sharp estimate of the MLSI constant of the switch chain on the set of simple bipartite regular graphs of size n with a fixed degree d. Our estimate implies that the total variation mixing time of the switch chain is of order O(n log(n)). The result is optimal up to a multiple…
Abstract: We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for the repeated evaluations of an expensive forward model, which is often given as a black box or is impractical to differentiate. In this talk I will propose a new derivative-free algorithm Unscented Kalman Inversion, which utilizes the ideas from Kalman filter, to efficiently solve these inverse problems. First, I will explain some basics about Variational Inference under general metric tensors. In particular, under the…
Abstract: Motivated by applications to single-particle cryo-electron microscopy, we study a problem of group orbit estimation where samples of an unknown signal are observed under uniform random rotations from a rotational group. In high-noise settings, we show that geometric properties of the log-likelihood function are closely related to algebraic properties of the invariant algebra of the group action. Eigenvalues of the Fisher information matrix are stratified according to a sequence of transcendence degrees in this invariant algebra, and critical points…
