Pubblicazioni

Extracting the Multiscale Causal Backbone of Brain Dynamics

2024 - Proceedings of Machine Learning Research
D'Acunto, Gabriele; Bonchi, Francesco; De Francisci Morales, Gianmarco; Petri, Giovanni

Consistent Submodular Maximization

2024 - Proceedings of Machine Learning Research
Dutting, P.; Fusco, F.; Lattanzi, S.; Norouzi-Fard, A.; Zadimoghaddam, M.

Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models

2024 - Proceedings of Machine Learning Research
Zhang, H.; Edelman, B. L.; Francati, D.; Venturi, D.; Ateniese, G.; Barak, B.

Position: Topological Deep Learning is the New Frontier for Relational Learning

2024 - Proceedings of Machine Learning Research
Papamarkou, T.; Birdal, T.; Bronstein, M.; Carlsson, G.; Curry, J.; Gao, Y.; Hajij, M.; Kwitt, R.; Lio, P.; Di Lorenzo, P.; Maroulas, V.; Miolane, N.; Nasrin, F.; Ramamurthy, K. N.; Rieck, B.; Scardapane, S.; Schaub, M. T.; Velickovic, P.; Wang, B.; Wang, Y.; Wei, G. -W.; Zamzmi, G.

How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

2024 - Proceedings of Machine Learning Research
Huang, K.; Wang, Y. G.; Li, M.; Lio, P.

Fairness in Streaming Submodular Maximization over a Matroid Constraint

2023 - Proceedings of Machine Learning Research
El Halabi, M.; Fusco, F.; Norouzi-Fard, A.; Tardos, J.; Tarnawski, J.

Repeated Bilateral Trade Against a Smoothed Adversary

2023 - Proceedings of Machine Learning Research
Cesa-Bianchi, N.; Cesari, T.; Colomboni, R.; Fusco, F.; Leonardi, S.

ICML 2023 topological deep learning challenge. Design and results

2023 - Proceedings of Machine Learning Research
Papillon, Mathilde; Hajij, Mustafa; Frantzen, Florian; Hoppe, Josef; Jenne, Helen; Mathe, Johan; Myers, Audun; Papamarkou, Theodore; Schaub, Michael T.; Zamzmi, Ghada; Birdal, Tolga; Dey, Tamal; Doster, Timothy; Emerson, Tegan H.; Gopalakrishnan, Gurusankar; Govil, D.; Grande, Vincent P.; Guzm'an-S'aenz, Aldo; Kvinge, Henry; Livesay, Neal; Meisner, Jan; Mukherjee, Soham; Samaga, Shreyas N.; Natesan Ramamurthy, Karthikeyan; Reddy Karri, Maneel; Rosen, Paul; Sanborn, Sophia; Scholkemper, Michael; Walters, Robin; Agerberg, Jens; Bokman, Georg; Barikbin, Sadrodin; Battiloro, Claudio; Bazhenov, Gleb; Bern('a)rdez, Guillermo; Brent, Aiden; Escalera, Sergio; Fiorellino, Simone; Gavrilev, Dmitrii; Hassanin, Mohammed; Hausner, Paul; Hoff Gardaa, Odin; Khamis, Abdelwahed; Lecha, M; Magai, German; Malygina, Tatiana; Melnyk, Pavlo; Ballester, Rub('e)n; Varma Nadimpalli, Kalyan; Nikitin, Alexander; Rabinowitz, Abraham; Salatiello, Alessandro; Scardapane, Simone; Scofano, Luca; Singh, Suraj; Sjolund, Jens; Snopov, Paul; Spinelli, Indro; Telyatnikov, Lev; Testa, Lucia; Yang, Maosheng; Yue, Yixiao; Zaghen, Olga; Zia, Ali; Miolane, Nina

Find a witness or shatter: the landscape of computable PAC learning

2023 - Proceedings of Machine Learning Research
DELLE ROSE, Valentino; Kozachinskiy, Alexander; Rojas, Cristobal; Steifer, Tomasz

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

2023 - Proceedings of Machine Learning Research
Di Giovanni, F.; Giusti, L.; Barbero, F.; Luise, G.; Lio, P.; Bronstein, M.

Neural Algorithmic Reasoning for Combinatorial Optimisation

2023 - Proceedings of Machine Learning Research
Georgiev, D.; Numeroso, D.; Bacciu, D.; Lio, P.

Will More Expressive Graph Neural Networks do Better on Generative Tasks?

2023 - Proceedings of Machine Learning Research
Zou, X.; Zhao, X.; Lio, P.; Zhao, Y.

Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach

2023 - Proceedings of Machine Learning Research
Keskin, O.; Lupidi, A.; Giannini, F.; Fioravanti, S.; Magister, L. C.; Barbiero, P.; Lio, P.

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

2023 - Proceedings of Machine Learning Research
Norcliffe, A.; Cebere, B.; Imrie, F.; Lio, P.; van der Schaar, M.

On the Expressive Power of Geometric Graph Neural Networks

2023 - Proceedings of Machine Learning Research
Joshi, C. K.; Bodnar, C.; Mathis, S. V.; Cohen, T.; Lio, P.

Deletion Robust Submodular Maximization over Matroids

2022 - Proceedings of Machine Learning Research
Dutting, P.; Fusco, F.; Lattanzi, S.; Norouzi-Fard, A.; Zadimoghaddam, M.

Approximate Equivariance SO(3) Needlet Convolution

2022 - Proceedings of Machine Learning Research
Yi, K.; Chen, J.; Wang, Y. G.; Zhou, B.; Lio, P.; Fan, Y.; Hamann, J.

Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes

2022 - Proceedings of Machine Learning Research
Opolka, F. L.; Lio, P.

Sheaf Neural Networks with Connection Laplacians

2022 - Proceedings of Machine Learning Research
Barbero, F.; Bodnar, C.; de Ocariz Borde, H. S.; Bronstein, M.; Velickovic, P.; Lio, P.

Learning Graph Search Heuristics

2022 - Proceedings of Machine Learning Research
Pandy, M.; Qiu, W.; Corso, G.; Velickovic, P.; Ying, R.; Leskovec, J.; Lio, P.

Continuous Neural Algorithmic Planners

2022 - Proceedings of Machine Learning Research
He, Y.; Velickovic, P.; Lio, P.; Deac, A.

Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets

2022 - Proceedings of Machine Learning Research
Opolka, F. L.; Zhi, Y. -C.; Lio, P.; Dong, X.

Well-conditioned Spectral Transforms for Dynamic Graph Representation

2022 - Proceedings of Machine Learning Research
Zhou, B.; Liu, X.; Liu, Y.; Huang, Y.; Lio, P.; Wang, Y. G.

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

2021 - Proceedings of Machine Learning Research
Bodnar, C.; Frasca, F.; Wang, Y. G.; Otter, N.; Montufar, G.; Lio, P.; Bronstein, M. M.

How Framelets Enhance Graph Neural Networks

2021 - Proceedings of Machine Learning Research
Zheng, X.; Zhou, B.; Gao, J.; Wang, Y. G.; Lio, P.; Li, M.; Montufar, G.

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