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Found 4 papers, 0 papers with code
A Differentially Private Clustering Algorithm for Well-Clustered Graphs
We study differentially private (DP) algorithms for recovering clusters in well-clustered graphs, which are graphs whose vertex set can be partitioned into a small number of sets, each inducing a subgraph of high inner conductance and small outer conductance.
HUGE: Huge Unsupervised Graph Embeddings with TPUs
A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges.
Constant matters: Fine-grained Complexity of Differentially Private Continual Observation
Finally, we note that our result can be used to get a fine-grained error bound for non-interactive local learning {and the first lower bounds on the additive error for $(\epsilon,\delta)$-differentially-private counting under continual observation.}
A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice
We study property testing of $k$-NN graphs in theory and evaluate it empirically: given a point set $P \subset \mathbb{R}^\delta$ and a directed graph $G=(P, E)$, is $G$ a $k$-NN graph, i. e., every point $p \in P$ has outgoing edges to its $k$ nearest neighbors, or is it $\epsilon$-far from being a $k$-NN graph?
