AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A simple framework for constructing differentially private confidence regions by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resample, avoiding the computational and privacy costs of privatizing many intermediate resample statistics.
The main goal is to develop a procedure based on Oja's algorithm that is optimal without prior knowledge of the eigengap, which is otherwise needed to tune the learning rates effectively.
We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resample. The cost of privacy of our procedure is only logarithmic in the number of resamples $...
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.
We study principal component analysis (PCA) under memory constraints, a setting that is increasingly important in large-scale data analysis. Our focus is on Oja's algorithm, which is a one-pass, memory-efficient algorithm requiring only $O(p)$ storage in the rank-one case and $O(pk)$ storage for $k$- PCA. The main goal...
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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