AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
2 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper studies the estimation of a marginal regression function from independent units with repeated binary, count, or continuous responses using ReLU deep neural networks, and derives an oracle inequality for unequal cluster sizes and a rate for compositionally smooth functions.
Simulations show that joint estimation is useful when regression surfaces change abruptly across spatial boundaries, including settings with nonlinear effects, unequal region sizes, preferential sampling, and spatially correlated errors.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.