no code implementations • 18 Apr 2024 • Luca Ganassali
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.
no code implementations • 20 Oct 2023 • Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
This, in turn, allows us to characterize the sample complexity of any constraint-based causal discovery algorithm that uses VM-CI for CI tests.
no code implementations • 26 May 2023 • Sina Akbari, Luca Ganassali
We study the problem of causal structure learning from data using optimal transport (OT).
no code implementations • 27 Sep 2022 • Luca Ganassali, Laurent Massoulié, Guilhem Semerjian
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.
1 code implementation • 18 Mar 2022 • Eric Daoud, Luca Ganassali, Antoine Baker, Marc Lelarge
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.
1 code implementation • 15 Jul 2021 • Luca Ganassali, Laurent Massoulié, Marc Lelarge
We then conjecture that graph alignment is not feasible in polynomial time when the associated tree detection problem is impossible.
no code implementations • 4 Feb 2021 • Luca Ganassali, Laurent Massoulié, Marc Lelarge
Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges.
no code implementations • 30 Oct 2020 • Luca Ganassali
We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment.