Search Results for author:
Found 8 papers, 2 papers with code
The graph alignment problem: fundamental limits and efficient algorithms
This thesis studies the graph alignment problem, the noisy version of the graph isomorphism problem, which aims to find a matching between the nodes of two graphs which preserves most of the edges.
On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
This, in turn, allows us to characterize the sample complexity of any constraint-based causal discovery algorithm that uses VM-CI for CI tests.
Learning Causal Graphs via Monotone Triangular Transport Maps
We study the problem of causal structure learning from data using optimal transport (OT).
Statistical limits of correlation detection in trees
In this paper we address the problem of testing whether two observed trees $(t, t')$ are sampled either independently or from a joint distribution under which they are correlated.
SiMCa: Sinkhorn Matrix Factorization with Capacity Constraints
In these applications, there is somewhat of an asymmetry between users and items: items are viewed as static points, their embeddings, capacities and locations constraining the allocation.
Correlation detection in trees for planted graph alignment
We then conjecture that graph alignment is not feasible in polynomial time when the associated tree detection problem is impossible.
Impossibility of Partial Recovery in the Graph Alignment Problem
Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges.
Sharp threshold for alignment of graph databases with Gaussian weights
We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment.
