Search Results for author: Pablo Barceló

Found 13 papers, 3 papers with code

Link Prediction with Relational Hypergraphs

no code implementations6 Feb 2024 Xingyue Huang, Miguel Romero Orth, Pablo Barceló, Michael M. Bronstein, İsmail İlkan Ceylan

Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications.

Graph Neural Network Inductive Link Prediction +1

A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs

no code implementations6 Oct 2023 Tamara Cucumides, Daniel Daza, Pablo Barceló, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero

We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs, encompassing both cyclic queries and tree-like queries with existentially quantified leaves.

Knowledge Graphs

On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters

no code implementations29 Sep 2023 Matthias Lanzinger, Pablo Barceló

A central focus of research in this field revolves around determining the least dimensionality $k$, for which $k$WL can discern graphs with different number of occurrences of a pattern graph $P$.

Subgraph Counting

Three iterations of $(d-1)$-WL test distinguish non isometric clouds of $d$-dimensional points

no code implementations22 Mar 2023 Valentino Delle Rose, Alexander Kozachinskiy, Cristóbal Rojas, Mircea Petrache, Pablo Barceló

Our main result states that the $(d-1)$-dimensional WL test is complete for point clouds in $d$-dimensional Euclidean space, for any $d\ge 2$, and that only three iterations of the test suffice.

Graph Neural Network

A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs

1 code implementation NeurIPS 2023 Xingyue Huang, Miguel Romero Orth, İsmail İlkan Ceylan, Pablo Barceló

Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction.

Knowledge Graphs Link Prediction +1

No Agreement Without Loss: Learning and Social Choice in Peer Review

no code implementations3 Nov 2022 Pablo Barceló, Mauricio Duarte, Cristóbal Rojas, Tomasz Steifer

It may be assumed that each reviewer has her own mapping from the set of features to a recommendation, and that different reviewers have different mappings in mind.

On Computing Probabilistic Explanations for Decision Trees

no code implementations30 Jun 2022 Marcelo Arenas, Pablo Barceló, Miguel Romero, Bernardo Subercaseaux

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.

Explainable Artificial Intelligence (XAI)

Foundations of Symbolic Languages for Model Interpretability

1 code implementation NeurIPS 2021 Marcelo Arenas, Daniel Baez, Pablo Barceló, Jorge Pérez, Bernardo Subercaseaux

Several queries and scores have recently been proposed to explain individual predictions over ML models.

Graph Neural Networks with Local Graph Parameters

1 code implementation NeurIPS 2021 Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov

We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account.

Graph Learning

On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results

no code implementations16 Apr 2021 Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi, Mikaël Monet

While in general computing Shapley values is an intractable problem, we prove a strong positive result stating that the $\mathsf{SHAP}$-score can be computed in polynomial time over deterministic and decomposable Boolean circuits.

Model Interpretability through the Lens of Computational Complexity

no code implementations NeurIPS 2020 Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux

We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P$\neq$NP), both linear and tree-based models are strictly more interpretable than neural networks.

The Logical Expressiveness of Graph Neural Networks

no code implementations ICLR 2020 Pablo Barceló, Egor V. Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, Juan Pablo Silva

We show that this class of GNNs is too weak to capture all FOC2 classifiers, and provide a syntactic characterization of the largest subclass of FOC2 classifiers that can be captured by AC-GNNs.


On the Turing Completeness of Modern Neural Network Architectures

no code implementations ICLR 2019 Jorge Pérez, Javier Marinković, Pablo Barceló

Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences.

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