54 citations · 69 across the 5 of their papers we have counts for
9 papers
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework
Hannah Sansford, Nicholas Richardson, Hermina Petric Maretic +1
Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies, also known as hallucinations, with respect to the provided knowledge, are becoming increasingly…
FGOT: Graph Distances based on Filters and Optimal Transport
Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia +1
Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and in…
Wasserstein-based Graph Alignment
Hermina Petric Maretic, Mireille El Gheche, Matthias Minder +2
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph…
Forward-Backward Splitting for Optimal Transport based Problems
Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou +2
Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given…
GOT: An Optimal Transport framework for Graph comparison
Hermina Petric Maretic, Mireille EL Gheche, Giovanni Chierchia +1
We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph sig…
Graph heat mixture model learning
Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard
Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most…
Supervised Linear Regression for Graph Learning from Graph Signals
Arun Venkitaraman, Hermina Petric Maretic, Saikat Chatterjee +1
We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model…
Graph Laplacian mixture model
Hermina Petric Maretic, Pascal Frossard
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships betw…
Graph learning under sparsity priors
Hermina Petric Maretic, Dorina Thanou, Pascal Frossard
Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori b…