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Found 3 papers, 0 papers with code
Utility-Based Communication Requirements for Stable Matching in Large Markets
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.
Extending Universal Approximation Guarantees: A Theoretical Justification for the Continuity of Real-World Learning Tasks
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.
Recommending with Recommendations
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.
