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Metalearning with Very Few Samples Per Task
In multitask learning, we are given a fixed set of related learning tasks and need to output one accurate model per task, whereas in metalearning we are given tasks that are drawn i. i. d.
Multitask Learning via Shared Features: Algorithms and Hardness
We investigate the computational efficiency of multitask learning of Boolean functions over the $d$-dimensional hypercube, that are related by means of a feature representation of size $k \ll d$ shared across all tasks.
Fair and Optimal Cohort Selection for Linear Utilities
The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms.
Fair and Useful Cohort Selection
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness.
