AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A flow matching based algorithm is formulated which learns a transport map from the source to target trajectory distribution, while preserving the Markov structure, which is consistent in the population limit and derive finite-sample error bounds under mixing time assumptions.
A new analysis of the ubiquitous Oja's algorithm for the most general, gap-free variant of this problem, where no eigengap assumptions are made on the underlying mean matrix, complemented by a nearly-matching lower bound.
Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^d:=\{\mathbf{x}\in\{\pm 1\}^d:|\{i:\mathbf{x}_i=1\}|=k\}$, in high-dimensional regimes where $k\ll d$ (i.e., where $\mathcal{X}_k^d$ is \emp...
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