Mathematical & Statistical Methods
GOT: An Optimal Transport framework for Graph comparison
This allows us to derive an explicit expression of the Wasserstein distance between graph signal distributions in terms of the graph Laplacian matrices.
On Differentially Private Graph Sparsification and Applications
In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an input graph, returns a sparse graph which approximates the spectrum of the input graph while ensuring differential privacy.
Cost Effective Active Search
Shali Jiang, Roman Garnett, Benjamin Moseley
We propose simple and fast approximations for its expectation, which serves as an essential role in our proposed policy.
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
Neural Information Processing Systems http://nips.cc/
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng
We give the first efficient algorithms proven to succeed in the correlated Erdös-Rényi model (Pedarsani and Grossglauser, 2011).
Faster Linear Algebra for Distance Matrices
Such complexities are prohibitive for scaling to large datasets.
Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model
Community detection in random graphs or hypergraphs is an interesting fundamental problem in statistics, machine learning and computer vision.
Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model
Community detection in random graphs or hypergraphs is an interesting fundamental problem in statistics, machine learning and computer vision.
