no code implementations • 8 Dec 2022 • Naveen Durvasula
Our results show that for a broad family of markets, the error bound may not grow faster than $n^2\log(n)$ while maintaining a deterministic guarantee on the behavior of stable matching mechanisms in the limit.
no code implementations • 6 Dec 2022 • Naveen Durvasula
Universal Approximation Theorems establish the density of various classes of neural network function approximators in $C(K, \mathbb{R}^m)$, where $K \subset \mathbb{R}^n$ is compact.
no code implementations • 2 Dec 2021 • Naveen Durvasula, Franklyn Wang, Scott Duke Kominers
In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space, and the ideal recommendations for the source and target tasks (which are non-sensitive) are given by unknown linear transformations of the user information.