What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? In this talk, I formalize the optimal teaching problem. In particular, I will focus on learners who employ Bayesian models. The framework is expressed as an optimization problem …
We study decentralized markets which include middlemen, producers and consumers connected via a trading network. We develop a model for trade in such settings based on non-cooperative bargaining with search frictions. Our goal is to investigate how the structure of the trading network and the role of middlemen influence the …
A wide-ranging and freewheeling panel discussion of how science, especially the science of information and optimization, can best make itself known to the public.
Speaker 1: Chris Hinrichs Abstract: Multiple hypothesis testing is a significant problem in nearly all neuroimaging studies. In order to correct for this phenomena, we require a reliable estimate of the Family-Wise Error Rate (FWER). The well known Bonferroni correction method, while being simple to implement, is quite conservative, and …
We describe a sparse signal detection algorithm that is being developed in the context of an on-going research project in spectrum sensing for cognitive radio. The objective is to detect the support of a sparse signal vector, whose exact number of nonzero components is unknown, using a sequence of noisy …
Prof. Rob Nowak Mind Reading involves a subtle interplay of statistics and physical modeling, and often the latter is not considered as critically as it should be. For example, parapsychological experiments have demonstrated mind reading effects that are statistically significant, but physically implausible. But we don’t have to look at …
Jagdish Ramakrishnan In radiation therapy, the fractionation schedule, i.e. the total number of treatment days and the dose delivered per day, plays an important role in treatment outcome. In the first part of the talk, we analyze the effect of tumor repopulation on the optimal fractionation scheme. We find that …
Merve Bodur Stochastic programming is a way of dealing with uncertainty in the optimization problems. We consider two-stage stochastic programs with integer first stage and continuous second stage. It means that the decision maker must take some integer decisions before the uncertainty is revealed, then can observe the realizations and …
Sarah Rich Sometimes in life things are complicated, and we just wish they were simpler! (Am I right, ladies?) A standard approach of theoreticians is to just pretend that they *are* simpler and keep going! We’ll consider this approach in the context of models for large empirical networks, like social …
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select coefficients, we propose an adaptive $\ell_1$-penalization methodology, where …