Consider a group of workers who answered questions, which have a correct yet unknown answers. The workers are heterogenous, they could be ordinary people, trained volunteers, a panel of experts, different computer algorithms, or a mix of all the above. Our approach is based on empirical Bayes methods and the aim is to construct an algorithm that aggregates all workers’ answers to a single output that is close to the unknown truth. (MSc student: Tsviel Ben-Shabat, co-advisor: Reshef Meir)
We study a regression model in the context of high-dimensional and semi-supervised settings, making minimal assumptions on the distribution of the data. The goal is to estimate the fraction of variance explained by the best linear model without assuming linearity. (PhD student: Ilan Livne, co-advisor: Yair Goldberg).