Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Ganassali, Luca"'
The Procrustes-Wasserstein problem consists in matching two high-dimensional point clouds in an unsupervised setting, and has many applications in natural language processing and computer vision. We consider a planted model with two datasets $X,Y$ th
Externí odkaz:
http://arxiv.org/abs/2405.14532
Autor:
Ganassali, Luca
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. Focusing on the planted version where the graphs are
Externí odkaz:
http://arxiv.org/abs/2404.12418
Motivated by conditional independence testing, an essential step in constraint-based causal discovery algorithms, we study the nonparametric Von Mises estimator for the entropy of multivariate distributions built on a kernel density estimator. We est
Externí odkaz:
http://arxiv.org/abs/2310.13553
We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangular monotone parametric transport maps to design conditional independenc
Externí odkaz:
http://arxiv.org/abs/2305.18210
Publikováno v:
Annals of Applied Probability 2024, Vol. 34, No. 4, 3701-3734
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. This problem, which we refer to as correlation detection in trees, play
Externí odkaz:
http://arxiv.org/abs/2209.13723
For a very broad range of problems, recommendation algorithms have been increasingly used over the past decade. In most of these algorithms, the predictions are built upon user-item affinity scores which are obtained from high-dimensional embeddings
Externí odkaz:
http://arxiv.org/abs/2203.10107
Publikováno v:
Ann. Appl. Probab. 34 (3) 2799 - 2843, June 2024
Motivated by alignment of correlated sparse random graphs, we introduce a hypothesis testing problem of deciding whether or not two random trees are correlated. We obtain sufficient conditions under which this testing is impossible or feasible. We pr
Externí odkaz:
http://arxiv.org/abs/2107.07623
Publikováno v:
Proceedings of Thirty Fourth Conference on Learning Theory, PMLR 134:2080-2102, 2021
Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges. This can be viewed as an average-case and noisy version of the well-known graph isomorphism problem. For the correlated
Externí odkaz:
http://arxiv.org/abs/2102.02685
Autor:
Ganassali, Luca
We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment. We consider a model of two graphs where $\pi^*$ is a planted uniform permutation and all pairs of edge weights $(A_{i,j}, B_{\pi^*(i),\pi^*(j)})_{1 \
Externí odkaz:
http://arxiv.org/abs/2010.16295
Autor:
Ganassali, Luca, Massoulié, Laurent
Publikováno v:
Proceedings of Thirty Third Conference on Learning Theory, PMLR 125:1633-1665, 2020
In this paper we consider alignment of sparse graphs, for which we introduce the Neighborhood Tree Matching Algorithm (NTMA). For correlated Erd\H{o}s-R\'{e}nyi random graphs, we prove that the algorithm returns -- in polynomial time -- a positive fr
Externí odkaz:
http://arxiv.org/abs/2002.01258