most citedBoosting Relational Deep Learning with Pretrained Tabular Models

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.AI2026

SokoBench: Evaluating Long-Horizon Planning and Reasoning in Large Language Models

Sebastiano Monti, Carlo Nicolini, Gianni Pellegrini +2

Although the capabilities of large language models have been increasingly tested on complex reasoning tasks, their long-horizon planning abilities have not yet been extensively inv…

cs.LG2025

To Ask or Not to Ask: Learning to Require Human Feedback

Andrea Pugnana, Giovanni De Toni, Cesare Barbera +3

Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machi…

cs.IR2025

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control

Giovanni De Toni, Erasmo Purificato, Emilia Gómez +3

Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevan…

cs.LG2025

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction

Francesco Ferrini, Veronica Lachi, Antonio Longa +2

Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-centric message-passing schemes ove…

cs.LG2025

Bridging Theory and Practice in Link Representation with Graph Neural Networks

Veronica Lachi, Francesco Ferrini, Antonio Longa +3

Graph Neural Networks (GNNs) are widely used to compute representations of node pairs for downstream tasks such as link prediction. Yet, theoretical understanding of their expressi…

cs.DB20251 cited

Boosting Relational Deep Learning with Pretrained Tabular Models

Veronica Lachi, Antonio Longa, Beatrice Bevilacqua +3

Relational databases, organized into tables connected by primary-foreign key relationships, are a common format for organizing data. Making predictions on relational data often inv…