Lió, Pietro

140 publications

TMLR 2026 TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality Carlos Vonessen, Charles Harris, Miruna Cretu, Pietro Lio

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

ECML-PKDD 2025 Balanced and Token-Efficient Summarization of User Reviews via Stratified Sampling and Large Language Models Fabrizio Marozzo, Loris Belcastro, Cristian Cosentino, Pietro Liò

ICLRW 2025 Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo Lee Cheuk Kit, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas

AAAI 2025 Deep Hypergraph Neural Networks with Tight Framelets Ming Li, Yujie Fang, Yi Wang, Han Feng, Yongchun Gu, Lu Bai, Pietro Liò

ICML 2025 EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction Ming Li, Yukang Cheng, Lu Bai, Feilong Cao, Ke Lv, Jiye Liang, Pietro Lio

ICML 2025 G-Adaptivity: Optimised Graph-Based Mesh Relocation for Finite Element Methods James Rowbottom, Georg Maierhofer, Teo Deveney, Eike Hermann Müller, Alberto Paganini, Katharina Schratz, Pietro Lio, Carola-Bibiane Schönlieb, Chris Budd

ICML 2025 Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification Flavio Petruzzellis, Cristina Cornelio, Pietro Lio

NeurIPS 2025 How Particle System Theory Enhances Hypergraph Message Passing Yixuan Ma, Kai Yi, Pietro Lio, Shi Jin, Yu Guang Wang

ICLRW 2025 HyperGenie: A Method for Predicting Enzymatic Gene Essentiality Using Hypergraph Neural Networks and Genome-Scale Metabolic Models Panayiotis Ioannou, Iulia Duta, Suraj Verma, Pietro Cicuta, Pietro Lio, Claudio Angione

TMLR 2025 Hypergraph Neural Networks Through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez, Olga Zaghen, Simone Scardapane, Pietro Lio

ICML 2025 NMA-Tune: Generating Highly Designable and Dynamics Aware Protein Backbones Urszula Julia Komorowska, Francisco Vargas, Alessandro Rondina, Pietro Lio, Mateja Jamnik

AAAI 2025 Neural Reasoning for Sure Through Constructing Explainable Models Tiansi Dong, Mateja Jamnik, Pietro Liò

ICLRW 2025 Predicting Time-Varying Metabolic Dynamics Using Structured Neural Ode Processes Santanu Rathod, Pietro Lio, Xiao Zhang

TMLR 2025 RNA-FrameFlow: Flow Matching for De Novo 3D RNA Backbone Design Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian Rokkum Jamasb, Charles Harris, Simon V Mathis, Kieran Didi, Rex Ying, Bryan Hooi, Pietro Lio

ICML 2025 SPHINX: Structural Prediction Using Hypergraph Inference Network Iulia Duta, Pietro Lio

ICML 2025 Stochastic Encodings for Active Feature Acquisition Alexander Luke Ian Norcliffe, Changhee Lee, Fergus Imrie, Mihaela Van Der Schaar, Pietro Lio

ICLR 2025 SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints Miruna Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin Segler, Bruno Correia, Julien Roy, Emmanuel Bengio, Pietro Lio

ICLRW 2025 Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs Batu El, Deepro Choudhury, Pietro Lio, Chaitanya K. Joshi

TMLR 2025 UMP-Net: Uncertainty-Aware Mixture of Prompts Network for Efficient Instruction Tuning Fatemeh Daneshfar, Abdulhady Abas, Moloud Abdar, Pietro Lio

ICLR 2025 Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka, Pietro Lio, José Miguel Hernández-Lobato

AAAI 2025 When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline Ming Li, Yongchun Gu, Yi Wang, Yujie Fang, Lu Bai, Xiaosheng Zhuang, Pietro Liò

ICLR 2025 gRNAde: Geometric Deep Learning for 3D RNA Inverse Design Chaitanya K. Joshi, Arian Rokkum Jamasb, Ramon Viñas Torné, Charles Harris, Simon V Mathis, Alex Morehead, Rishabh Anand, Pietro Lio

LoG 2025 xAI-Drop: Don’t Use What You Cannot Explain Vincenzo Marco De Luca, Antonio Longa, Pietro Lio, Andrea Passerini

ICLRW 2024 AI-Accelerated Biocatalyst Engineering by Rapid Microfluidic Sequence-Function Mapping Maximilian Gantz, Simon V Mathis, Friederike E. H. Nintzel, Paul J. Zurek, Tanja Knaus, Elie S. Patel, Daniel Boros, Friedrich-Maximilian Weberling, Elliot Medcalf, Jacob Moss, Michael Herger, Tomasz S. Kaminski, Francesco G. Mutti, Pietro Lio, Florian Hollfelder

NeurIPS 2024 DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-Transform Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

NeurIPS 2024 Deep Equilibrium Algorithmic Reasoning Dobrik Georgiev, Jj Wilson, Davide Buffelli, Pietro Liò

TMLR 2024 Deep Kernel Learning of Nonlinear Latent Force Models Jacob Moss, Jeremy England, Pietro Lio

ICLR 2024 Dynamics-Informed Protein Design with Structure Conditioning Urszula Julia Komorowska, Simon V Mathis, Kieran Didi, Francisco Vargas, Pietro Lio, Mateja Jamnik

ICMLW 2024 E(n) Equivariant Message Passing Cellular Networks Veljko Kovac, Erik J Bekkers, Pietro Lio, Floor Eijkelboom

ICMLW 2024 Ensemble Guidance: Towards Generative 3D SBDD in Bioactive Chemical Spaces Charles Harris, Arian Rokkum Jamasb, Pietro Lio, Tom Leon Blundell

ICLR 2024 Evaluating Representation Learning on the Protein Structure Universe Arian Rokkum Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom Leon Blundell

TMLR 2024 GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

ICML 2024 How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-Smoothing, and Over-Squashing Keke Huang, Yu Guang Wang, Ming Li, Pietro Lio

NeurIPSW 2024 Improving Antibody Design with Force-Guided Sampling in Diffusion Models Paulina Kulytė, Francisco Vargas, Simon V Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Lio

ICMLW 2024 Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks Ferran Hernandez Caralt, Guillermo Bernardez, Iulia Duta, Eduard Alarcon, Pietro Lio

ICMLW 2024 Metric Learning for Clifford Group Equivariant Neural Networks Riccardo Ali, Paulina Kulytė, Haitz Sáez de Ocáriz Borde, Pietro Lio

ICMLW 2024 Parallelising Differentiable Algorithms Removes the Scalar Bottleneck: A Case Study Euan Ong, Ferenc Huszár, Pietro Lio, Petar Veličković

ICML 2024 Position: Topological Deep Learning Is the New Frontier for Relational Learning Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guowei Wei, Ghada Zamzmi

NeurIPSW 2024 Prechastic Coding: An Alternative Approach to Neural Network Description Lengths Paris Dominic Louis Flood, Pietro Lio

ICMLW 2024 RNA-FrameFlow for De Novo 3D RNA Backbone Design Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian Rokkum Jamasb, Charles Harris, Simon V Mathis, Kieran Didi, Bryan Hooi, Pietro Lio

ICMLW 2024 Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians Olga Zaghen, Antonio Longa, Steve Azzolin, Lev Telyatnikov, Andrea Passerini, Pietro Lio

ICLRW 2024 SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways Miruna Cretu, Charles Harris, Julien Roy, Emmanuel Bengio, Pietro Lio

ICMLW 2024 TabMDA: Tabular Manifold Data Augmentation for Any Classifier Using Transformers with In-Context Subsetting Andrei Margeloiu, Adrián Bazaga, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

NeurIPSW 2024 Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka, Pietro Lio, José Miguel Hernández-Lobato

ICLR 2024 Unsupervised Pretraining for Fact Verification by Language Model Distillation Adrián Bazaga, Pietro Lio, Gos Micklem

NeurIPSW 2023 A Framework for Conditional Diffusion Modelling with Applications in Motif Scaffolding for Protein Design Kieran Didi, Francisco Vargas, Simon Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

NeurIPSW 2023 AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Lio

NeurIPSW 2023 Beyond Erdos-Renyi: Generalization in Algorithmic Reasoning on Graphs Dobrik Georgiev, Pietro Lio, Jakub Bachurski, Junhua Chen, Tunan Shi

ICMLW 2023 Bridging Equational Properties and Patterns on Graphs: An AI-Based Approach Oguzhan Keskin, Alisia Maria Lupidi, Stefano Fioravanti, Lucie Charlotte Magister, Pietro Barbiero, Pietro Lio, Francesco Giannini

ICMLW 2023 DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio

NeurIPSW 2023 Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero, Pietro Lio, Nikola Simidjievski

ICLRW 2023 Flexible Small-Molecule Design and Optimization with Equivariant Diffusion Models Charles Harris, Kieran Didi, Arne Schneuing, Yuanqi Du, Arian Rokkum Jamasb, Michael M. Bronstein, Bruno Correia, Pietro Lio, Tom Leon Blundell

NeurIPSW 2023 From Charts to Atlas: Merging Latent Spaces into One Donato Crisostomi, Irene Cannistraci, Luca Moschella, Pietro Barbiero, Marco Ciccone, Pietro Lio, Emanuele Rodolà

ICLRW 2023 GCI: A (G)raph (C)oncept (I)nterpretation Framework Dmitry Kazhdan, Botty Dimanov, Lucie Charlotte Magister, Pietro Barbiero, Mateja Jamnik, Pietro Lio

NeurIPSW 2023 GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

AAAI 2023 Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis Han Xuanyuan, Pietro Barbiero, Dobrik Georgiev, Lucie Charlotte Magister, Pietro Liò

ICLR 2023 Global Explainability of GNNs via Logic Combination of Learned Concepts Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Lio, Andrea Passerini

UAI 2023 Graph Classification Gaussian Processes via Spectral Features Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong

NeurIPS 2023 Graph Denoising Diffusion for Inverse Protein Folding Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Lió, Yuguang Wang

TMLR 2023 Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini

ICMLW 2023 Group Invariant Global Pooling Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Lio

NeurIPSW 2023 Incorporating LLM Priors into Tabular Learners Max Zhu, Siniša Stanivuk, Andrija Petrovic, Mladen Nikolic, Pietro Lio

NeurIPS 2023 Interpretable Graph Networks Formulate Universal Algebra Conjectures Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin, Alisia Lupidi, Lucie Charlotte Magister, Pietro Lió, Pietro Barbiero

ICML 2023 Interpretable Neural-Symbolic Concept Reasoning Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra

ICMLW 2023 Interpretable Neural-Symbolic Concept Reasoning Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra

ICLR 2023 Latent Graph Inference Using Product Manifolds Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Lio

ICMLW 2023 Meta-Learning Deep Kernels for Latent Force Inference Jacob Moss, Felix Opolka, Jeremy England, Pietro Lio

NeurIPSW 2023 Modelling Biology in Novel Ways - An AI-First Course in Structural Bioinformatics Kieran Didi, Charles Harris, Pietro Lio, Rainer Beck

LoG 2023 Neural Algorithmic Reasoning for Combinatorial Optimisation Dobrik Georgiev Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio

ICMLW 2023 NeuroEvolve: A Dynamic Brain Graph Deep Generative Model Simeon Emilov Spasov, Alexander Campbell, Nicola Toschi, Pietro Lio

ICML 2023 On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein

ICML 2023 On the Expressive Power of Geometric Graph Neural Networks Chaitanya K. Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, Pietro Lio

NeurIPSW 2023 PoseCheck: Generative Models for 3D Structure-Based Drug Design Produce Unrealistic Poses Charles Harris, Kieran Didi, Arian Jamasb, Chaitanya Joshi, Simon Mathis, Pietro Lio, Tom Blundell

CVPR 2023 SCOTCH and SODA: A Transformer Video Shadow Detection Framework Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero

NeurIPSW 2023 SHARCS: Shared Concept Space for\\Explainable Multimodal Learning Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister, Pietro Lio, Nikola Simidjievski

NeurIPS 2023 Sheaf Hypergraph Networks Iulia Duta, Giulia Cassarà, Fabrizio Silvestri, Pietro Lió

NeurIPSW 2023 Sheaf-Based Positional Encodings for Graph Neural Networks Yu He, Cristian Bodnar, Pietro Lio

AISTATS 2023 SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lió, Mihaela Schaar

ICLRW 2023 Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Lio

AAAI 2023 Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Andrei Margeloiu, Nikola Simidjievski, Pietro Liò, Mateja Jamnik

LoG 2023 Will More Expressive Graph Neural Networks Do Better on Generative Tasks? Xiandong Zou, Xiangyu Zhao, Pietro Lio, Yiren Zhao

AISTATS 2022 Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets Felix Opolka, Yin-Cong Zhi, Pietro Lió, Xiaowen Dong

AISTATS 2022 Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Felix Opolka, Pietro Lió

ICML 2022 3D Infomax Improves GNNs for Molecular Property Prediction Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió

AAAI 2022 Algorithmic Concept-Based Explainable Reasoning Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Velickovic, Pietro Lió

ICML 2022 Attentional Meta-Learners for Few-Shot Polythetic Classification Ben J Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lió

NeurIPSW 2022 Benchmarking Graph Neural Network-Based Imputation Methods on Single-Cell Transcriptomics Data Han-Bo Li, Ramon Viñas Torné, Pietro Lio

NeurIPS 2022 Composite Feature Selection Using Deep Ensembles Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar

NeurIPS 2022 Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lió, Mateja Jamnik

LoG 2022 Continuous Neural Algorithmic Planners Yu He, Petar Veličković, Pietro Lio, Andreea Deac

ICLRW 2022 Decoding Surface Fingerprints for Protein-Ligand Interactions Ilia Igashov, Arian Rokkum Jamasb, Ahmed Sadek, Freyr Sverrisson, Arne Schneuing, Tom Blundell, Pietro Lio, Michael M. Bronstein, Bruno Correia

LoG 2022 Distributed Representations of Graphs for Drug Pair Scoring Paul Scherer, Pietro Lio, Mateja Jamnik

ICLR 2022 Do We Need Anisotropic Graph Neural Networks? Shyam A. Tailor, Felix Opolka, Pietro Lio, Nicholas Donald Lane

NeurIPSW 2022 Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients Emma Charlotte Rocheteau, Ioana Bica, Pietro Lio, Ari Ercole

AAAI 2022 Entropy-Based Logic Explanations of Neural Networks Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, Stefano Melacci

NeurIPS 2022 Graph Neural Networks with Adaptive Readouts David Buterez, Jon Paul Janet, Steven J Kiddle, Dino Oglic, Pietro Liò

NeurIPS 2022 Graphein - A Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks Arian Jamasb, Ramon Viñas Torné, Eric Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lió, Tom Blundell

ICMLW 2022 Graphein - A Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks Arian Rokkum Jamasb, Ramon Viñas Torné, Eric J Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lio, Tom Leon Blundell

NeurIPSW 2022 Improving Classification and Data Imputation for Single-Cell Transcriptomics with Graph Neural Networks Han-Bo Li, Ramon Viñas Torné, Pietro Lio

LoG 2022 Learning Graph Search Heuristics Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Zhitao Ying, Jure Leskovec, Pietro Lio

ICLRW 2022 Message Passing Neural Processes Cătălina Cangea, Ben Day, Arian Rokkum Jamasb, Pietro Lio

ICLRW 2022 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lio, Michael M. Bronstein

NeurIPS 2022 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lió, Michael Bronstein

NeurIPSW 2022 On the Expressive Power of Geometric Graph Neural Networks Chaitanya K. Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, Pietro Liò

NeurIPSW 2022 Sheaf Attention Networks Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Pietro Lio

ICLRW 2022 Simplicial Attention Networks Christopher Wei Jin Goh, Cristian Bodnar, Pietro Lio

NeurIPS 2022 SizeShiftReg: A Regularization Method for Improving Size-Generalization in Graph Neural Networks Davide Buffelli, Pietro Lió, Fabio Vandin

LoG 2022 Well-Conditioned Spectral Transforms for Dynamic Graph Representation Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Lio, Yu Guang Wang

ICMLW 2021 $\alpha$-VAEs : Optimising Variational Inference by Learning Data-Dependent Divergence Skew Jacob Deasy, Tom Andrew McIver, Nikola Simidjievski, Pietro Lio

NeurIPSW 2021 3D Infomax Improves GNNs for Molecular Property Prediction Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lio

ICML 2021 Directional Graph Networks Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, Pietro Lió

ICLRW 2021 Directional Graph Networks Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò

MLJ 2021 How Artificial Intelligence and Machine Learning Can Help Healthcare Systems Respond to COVID-19 Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole

ICML 2021 How Framelets Enhance Graph Neural Networks Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montufar

ICLRW 2021 Meta-Learning Using Privileged Information for Dynamics Ben Day, Alexander Luke Ian Norcliffe, Jacob Moss, Pietro Liò

NeurIPS 2021 Neural Distance Embeddings for Biological Sequences Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Veličković, Jure Leskovec, Pietro Liò

ICLR 2021 Neural ODE Processes Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

NeurIPSW 2021 Neural ODE Processes: A Short Summary Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio

NeurIPSW 2021 On Second Order Behaviour in Augmented Neural ODEs: A Short Summary Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio

NeurIPS 2021 Weisfeiler and Lehman Go Cellular: CW Networks Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F. Montufar, Michael Bronstein

ICML 2021 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, Michael Bronstein

ICLRW 2021 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montufar, Pietro Liò, Michael M. Bronstein

ICLR 2020 Abstract Diagrammatic Reasoning with Multiplex Graph Networks Duo Wang, Mateja Jamnik, Pietro Lio

NeurIPS 2020 Constraining Variational Inference with Geometric Jensen-Shannon Divergence Jacob Deasy, Nikola Simidjievski, Pietro Lió

NeurIPSW 2020 Deep Graph Mapper: Seeing Graphs Through the Neural Lens Cristian Bodnar, Cătălina Cangea, Pietro Liò

NeurIPSW 2020 MEME: Generating RNN Model Explanations via Model Extraction Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò

NeurIPS 2020 On Second Order Behaviour in Augmented Neural ODEs Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió

NeurIPS 2020 Path Integral Based Convolution and Pooling for Graph Neural Networks Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò

NeurIPS 2020 Principal Neighbourhood Aggregation for Graph Nets Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković

AAAI 2020 Proximal Distilled Evolutionary Reinforcement Learning Cristian Bodnar, Ben Day, Pietro Lió

ICLR 2019 Deep Graph Infomax Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm

AAAI 2019 Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Liò, Nigel Collier

ICLR 2018 Graph Attention Networks Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio

AISTATS 2017 Bayesian Hybrid Matrix Factorisation for Data Integration Thomas Brouwer, Pietro Liò

ECML-PKDD 2017 Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation Thomas Brouwer, Jes Frellsen, Pietro Liò

ECML-PKDD 2016 Warped Matrix Factorisation for Multi-View Data Integration Naruemon Pratanwanich, Pietro Liò, Oliver Stegle