Fault Detection in Power Transmission Systems: A Smart Grid Approach Computing&AI Connect, v. 2
Doi: 10.69709/caic.2024.103229
Singh, V., Khanzadeh, M., Davis, V., Rush, H., Rossi, E., Shrader, J. and Lio’, P., 2025 (Published online). Bayesian Binary Search Algorithms, v. 18
Doi: 10.3390/a18080452
Purificato, A., Cassarà, G., Siciliano, F., Liò, P. and Silvestri, F., 2025 (Published online). Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems ACM Transactions on Recommender Systems,
Doi: 10.1145/3742898
Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study. ACM Comput. Surv., v. 57
Yan, C., Lu, X., Lio, P. and Hui, P., 2025. EARVP: Efficient Aggregation for Federated Learning With Robustness, Verifiability, and Privacy IEEE Transactions on Information Forensics and Security, v. 20
Doi: 10.1109/TIFS.2025.3576008
Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2025. Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks AI Open, v. 6
Doi: http://doi.org/10.1016/j.aiopen.2025.02.001
Wang, R., Tian, Y., Liò, P. and Bianconi, G., 2025. Dirac-equation signal processing: Physics boosts topological machine learning Pnas Nexus, v. 4
Doi: 10.1093/pnasnexus/pgaf139
Prinzi, F., Barbiero, P., Greco, C., Amorese, T., Cordasco, G., Liò, P., Vitabile, S. and Esposito, A., 2025. Using AI explainable models and handwriting/drawing tasks for psychological well-being Information Systems, v. 127
Doi: 10.1016/j.is.2024.102465
Orka, NA., Awal, MA., Liò, P., Pogrebna, G., Ross, AG. and Moni, MA., 2025. Quantum deep learning in neuroinformatics: a systematic review Artificial Intelligence Review, v. 58
Doi: http://doi.org/10.1007/s10462-025-11136-7
Wang, P., Lu, X. and Lio, P., 2025. Research on Privacy Protection Technology of “2+2” Verifiable Federated Learning IEEE Internet of Things Journal, v. 12
Doi: 10.1109/JIOT.2025.3554155
Yan, C., Lu, X., Lio, P., Hui, P. and He, D., 2025. Self-Simulation and Meta-Model Aggregation-Based Heterogeneous-Graph-Coupled Federated Learning IEEE Internet of Things Journal, v. 12
Doi: 10.1109/JIOT.2024.3462724
Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lio, P., Lepri, B. and Passerini, A., 2025. Explaining the Explainers in Graph Neural Networks: a Comparative Study ACM Computing Surveys, v. 57
Doi: 10.1145/3696444
Zahoor, S., Liò, P., Dias, G. and Hasanuzzaman, M., 2025. Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data. CoRR, v. abs/2501.15973
Bi, X., Tang, S., Xiao, B., Li, W., Gao, X. and Liò, P., 2025. A Systematic Review of Heart Sound Detection Algorithms: Experimental Results and Insights IEEE Transactions on Instrumentation and Measurement, v. 74
Doi: http://doi.org/10.1109/TIM.2025.3547082
Hasan, MDM., Abdar, M., Khosravi, A., Aickelin, U., Lio, P., Hossain, I., Rahman, A. and Nahavandi, S., 2025. Survey on Leveraging Uncertainty Estimation Toward Trustworthy Deep Neural Networks: The Case of Reject Option and Post-Training Processing ACM Computing Surveys, v. 57
Doi: 10.1145/3727633
Li, K., Zheng, J., Ni, W., Huang, H., Lio, P., Dressler, F. and Akan, OB., 2025. Biasing Federated Learning with a New Adversarial Graph Attention Network IEEE Transactions on Mobile Computing, v. 24
Doi: http://doi.org/10.1109/TMC.2024.3499371
Raisa, RA., Rodela, AS., Abu Yousuf, M., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2025. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study (vol 12, pg 122959, 2024) IEEE ACCESS, v. 13
Doi: 10.1109/ACCESS.2025.3551391
Bali, A., Wolter, S., Pelzel, D., Weyer, U., Azevedo, T., Lio, P., Kouka, M., Geißler, K., Bitter, T., Ernst, G., Xylander, A., Ziller, N., Mühlig, A., von Eggeling, F., Guntinas-Lichius, O. and Pertzborn, D., 2025. Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach Cancers, v. 17
Doi: 10.3390/cancers17101617
Nobel, SMN., Swapno, SMMR., Islam, MB., Azad, AKM., Alyami, SA., Alamin, M., Liò, P. and Moni, MA., 2025. A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes Advanced Intelligent Systems, v. 7
Doi: 10.1002/aisy.202400566
Crowley, R., Parkin, K., Rocheteau, E., Massou, E., Friedmann, Y., John, A., Sippy, R., Liò, P. and Moore, A., 2025. Machine learning for prediction of childhood mental health problems in social care. BJPsych Open, v. 11
Doi: http://doi.org/10.1192/bjo.2025.32
Telyatnikov, L., Bucarelli, MS., Bernardez, G., Zaghen, O., Scardapane, S. and Liò, P., 2025. Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design Transactions on Machine Learning Research, v. 2025-February
Aucello, R., Pernice, S., Tortarolo, D., Calogero, RA., Herrera-Rincon, C., Ronchi, G., Geuna, S., Cordero, F., Lió, P. and Beccuti, M., 2025. UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale. Bioinformatics, v. 41
Doi: http://doi.org/10.1093/bioinformatics/btaf103
El, B., Choudhury, D., Liò, P. and Joshi, CK., 2025. Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs. CoRR, v. abs/2502.12352
Minelli, A., Meloni, A., Bortolomasi, M., Pisanu, C., Zampieri, E., Congiu, D., Lana, B., Manchia, M., Meattini, M., Paribello, P., Baune, BT., Serretti, A., Dierssen, M., Maron, E., Potier, MC., Gennarelli, M., van Westrhenen, R., Squassina, A., Stacey, D., Mehta, D., Janzing, JGE., Ebert, B., Fabbri, C., Lio’, P. and Rybakowski, F., 2025. Telomere length and mitochondrial DNA copy number in association with trauma-focused psychotherapy efficacy Neuroscience Applied, v. 4
Doi: http://doi.org/10.1016/j.nsa.2024.104095
Lomoio, U., Veltri, P., Guzzi, PH. and Liò, P., 2025. Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution. Artif Intell Med, v. 160
Doi: http://doi.org/10.1016/j.artmed.2024.103058
Sun, B. and Liò, P., 2025. EU-Nets: Enhanced, Explainable and Parsimonious U-Nets. CoRR, v. abs/2502.18122
Ali, R., Caso, F., Irwin, C. and Liò, P., 2025. Entropy-Lens: The Information Signature of Transformer Computations. CoRR, v. abs/2502.16570
Fiore, P., Terlizzi, A., Bardozzo, F., Liò, P. and Tagliaferri, R., 2025. Advancing label-free cell classification with connectome-inspired explainable models and a novel LIVECell-CLS dataset Computers in Biology and Medicine, v. 192
Doi: http://doi.org/10.1016/j.compbiomed.2025.110274
Sun, B. and Liò, P., 2025. Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers. CoRR, v. abs/2501.01311
Bergna, R., Calvo-Ordoñez, S., Opolka, FL., Liò, P. and Hernandez-Lobato, JM., 2025. UNCERTAINTY MODELING IN GRAPH NEURAL NETWORKS VIA STOCHASTIC DIFFERENTIAL EQUATIONS 13th International Conference on Learning Representations Iclr 2025,
Carli, F., Di Chiaro, P., Morelli, M., Arora, C., Bisceglia, L., De Oliveira Rosa, N., Cortesi, A., Franceschi, S., Lessi, F., Di Stefano, AL., Santonocito, OS., Pasqualetti, F., Aretini, P., Miglionico, P., Diaferia, GR., Giannotti, F., Liò, P., Duran-Frigola, M., Mazzanti, CM., Natoli, G. and Raimondi, F., 2025. Learning and actioning general principles of cancer cell drug sensitivity. Nat Commun, v. 16
Doi: http://doi.org/10.1038/s41467-025-56827-5
Lee, CK., Jeha, P., Frellsen, J., Lio, P., Albergo, MS. and Vargas, F., 2025. Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. CoRR, v. abs/2502.06079
Buterez, D., Janet, JP., Oglic, D. and Liò, P., 2025. An end-to-end attention-based approach for learning on graphs Nature Communications, v. 16
Doi: 10.1038/s41467-025-60252-z
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024 (Published online). Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting Nature Communications, v. 15
Doi: 10.1038/s41467-024-45566-8
Thaventhiran, J., 2024 (Accepted for publication). Intratumoral antigen signaling traps CD8+ T cells to confine exhaustion to the tumor site Science Immunology,
Doi: 10.1126/sciimmunol.ade2094
Rathod, S., Lio, P. and Zhang, X., 2024. Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes. CoRR, v. abs/2410.14426
Liu, L., Cheng, Y., Deng, Z., Wang, S., Chen, D., Hu, X., Liò, P., Schönlieb, CB. and Aviles-Rivero, A., 2024. TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios Mm 2024 Proceedings of the 32nd ACM International Conference on Multimedia,
Doi: 10.1145/3664647.3681153
Gantz, M., Mathis, SV., Nintzel, FEH., Lio, P. and Hollfelder, F., 2024. On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering. Faraday Discuss, v. 252
Doi: 10.1039/d4fd00065j
Zuo, K., Jiang, Y., Mo, F. and Lio, P., 2024. KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis. CoRR, v. abs/2412.16833
Mamalakis, M., Mamalakis, A., Agartz, I., Mørch-Johnsen, LE., Murray, GK., Suckling, J. and Lio, P., 2024. Solving the enigma: Deriving optimal explanations of deep networks. CoRR, v. abs/2405.10008
Ali, R., Kulyte, P., Sáez de Ocáriz Borde, H. and Liò, P., 2024. Metric Learning for Clifford Group Equivariant Neural Networks Proceedings of Machine Learning Research, v. 251
Kujawa, Z., Poole, J., Georgiev, D., Numeroso, D. and Liò, P., 2024. Neural Algorithmic Reasoning with Multiple Correct Solutions. CoRR, v. abs/2409.06953
Bardozzo, F., Terlizzi, A., Simoncini, C., Lió, P. and Tagliaferri, R., 2024. Elegans-AI: How the connectome of a living organism could model artificial neural networks Neurocomputing, v. 584
Doi: 10.1016/j.neucom.2024.127598
Du, Y., Jamasb, AR., Guo, J., Fu, T., Harris, C., Wang, Y., Duan, C., Liò, P., Schwaller, P. and Blundell, TL., 2024. Machine learning-aided generative molecular design Nature Machine Intelligence, v. 6
Doi: 10.1038/s42256-024-00843-5
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2024. GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Transactions on Machine Learning Research, v. 2024
Yang, L., Liò, P., Shen, X., Zhang, Y. and Peng, C., 2024. Adaptive multi-scale Graph Neural Architecture Search framework Neurocomputing, v. 599
Doi: 10.1016/j.neucom.2024.128094
Caralt, FH., Gil, GB., Duta, I., Liò, P. and Cot, EA., 2024. Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks Proceedings of Machine Learning Research, v. 251
Braithwaite, L., Duta, I. and Liò, P., 2024. Heterogeneous Sheaf Neural Networks. CoRR, v. abs/2409.08036
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Lió, P., 2024. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nat Commun, v. 15
Doi: 10.1038/s41467-024-45566-8
Li, M., Micheli, A., Wang, YG., Pan, S., Lio, P., Gnecco, GS. and Sanguineti, M., 2024. Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications IEEE Transactions on Neural Networks and Learning Systems, v. 35
Doi: 10.1109/TNNLS.2024.3371592
Wang, Z., Ma, J., Gao, Q., Bain, C., Imoto, S., Liò, P., Cai, H., Chen, H. and Song, J., 2024. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal, v. 97
Doi: 10.1016/j.media.2024.103252
Kulyte, P., Vargas, F., Mathis, SV., Wang, YG., Hernández-Lobato, JM. and Liò, P., 2024. Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR, v. abs/2406.05832
Su, S., Duta, I., Magister, LC. and Liò, P., 2024. Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts. CoRR, v. abs/2410.07764
Mumenin, N., Yousuf, MA., Nashiry, MA., Azad, AKM., Alyami, SA., Lio’, P. and Moni, MA., 2024. ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology Iet Computer Vision, v. 18
Doi: 10.1049/cvi2.12271
Somathilaka, S., Ratwatte, A., Balasubramaniam, S., Vuran, MC., Srisa-an, W. and Liò, P., 2024. Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity. CoRR, v. abs/2403.08549
Moss, J., England, J. and Lió, P., 2024. Deep Kernel Learning of Nonlinear Latent Force Models Transactions on Machine Learning Research, v. 2024
Huang, K., Wang, YG., Li, M. and Liò, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing Proceedings of Machine Learning Research, v. 235
Sabari, A., Hasan, I., Alyami, SA., Liò, P., Ali, MS., Moni, MA. and Azad, AKM., 2024. LandSin: A differential ML and google API-enabled web server for real-time land insights and beyond[Formula presented] Software Impacts, v. 22
Doi: http://doi.org/10.1016/j.simpa.2024.100718
Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning Advances in Neural Information Processing Systems, v. 37
Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297
Raisa, RA., Rodela, AS., Yousuf, MA., Azad, A., Alyami, SA., Lio, P., Islam, MZ., Pogrebna, G. and Moni, MA., 2024. Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study IEEE Access, v. 12
Doi: 10.1109/ACCESS.2024.3426928
Lope, EGD., Deshpande, S., Torné, RV., Liò, P., Glaab, E. and Bordas, SPA., 2024. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson’s Disease. CoRR, v. abs/2406.14442
Schneuing, A., Harris, C., Du, Y., Didi, K., Jamasb, A., Igashov, I., Du, W., Gomes, C., Blundell, TL., Lio, P., Welling, M., Bronstein, M. and Correia, B., 2024. Structure-based drug design with equivariant diffusion models. Nat Comput Sci, v. 4
Doi: http://doi.org/10.1038/s43588-024-00737-x
Defilippo, A., Veltri, P., Lió, P. and Guzzi, PH., 2024. Leveraging graph neural networks for supporting automatic triage of patients. Sci Rep, v. 14
Doi: 10.1038/s41598-024-63376-2
Rowbottom, J., Maierhofer, G., Deveney, T., Schratz, K., Liò, P., Schönlieb, C-B. and Budd, CJ., 2024. G-Adaptive mesh refinement – leveraging graph neural networks and differentiable finite element solvers. CoRR, v. abs/2407.04516
Buterez, D., Janet, JP., Oglic, D. and Lio, P., 2024. Masked Attention is All You Need for Graphs. CoRR, v. abs/2402.10793
Zaki, JK., Tomasik, J., McCune, JA., Bahn, S., Liò, P. and Scherman, OA., 2024. Explainable Deep Learning Framework for SERS Bio-quantification. CoRR, v. abs/2411.08082
Zhao, X., Li, Z., Shen, M., Stan, G-B., Liò, P. and Zhao, Y., 2024. Enhancing Real-World Complex Network Representations with Hyperedge Augmentation. CoRR, v. abs/2402.13033
Jamasb, AR., Morehead, A., Joshi, CK., Zhang, Z., Didi, K., Mathis, S., Harris, C., Tang, J., Cheng, J., Liò, P. and Blundell, TL., 2024. Evaluating Representation Learning on the Protein Structure Universe. ArXiv,
Zhu, M., Bazaga, A. and Liò, P., 2024. FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models. CoRR, v. abs/2406.04501
Ceccarelli, F., Liò, P. and Holden, SB., 2024. AnnoGCD: a generalized category discovery framework for automatic cell type annotation. NAR Genom Bioinform, v. 6
Doi: http://doi.org/10.1093/nargab/lqae166
Bazaga, A., Liò, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs Emnlp 2024 2024 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2024,
Doi: 10.18653/v1/2024.findings-emnlp.537
Georgiev, D., Wilson, JJ., Buffelli, D. and Liò, P., 2024. Deep Equilibrium Algorithmic Reasoning. CoRR, v. abs/2410.15059
Lu, X., Zhao, J., Zhu, S. and Lio, P., 2024. SNDGCN: Robust Android malware detection based on subgraph network and denoising GCN network Expert Systems with Applications, v. 250
Doi: 10.1016/j.eswa.2024.123922
Zhou, B., Zheng, L., Wu, B., Yi, K., Zhong, B., Tan, Y., Liu, Q., Liò, P. and Hong, L., 2024. A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity. Cell Discov, v. 10
Doi: 10.1038/s41421-024-00728-2
Bi, X., Yang, Z., Liu, B., Cun, X., Pun, C-M., Lio, P. and Xiao, B., 2024. ZeroPur: Succinct Training-Free Adversarial Purification. CoRR, v. abs/2406.03143
Yi, K., Fan, Y., Hamann, J., Liò, P. and Wang, YG., 2024. ABCMB: deep delensing assisted likelihood-free inference from CMB polarization maps Machine Learning Science and Technology, v. 5
Doi: 10.1088/2632-2153/ad9af9
Barucci, A., Ciacci, G., Liò, P., Azevedo, T., Di Cencio, A., Merella, M., Bianucci, G., Bosio, G., Casati, S. and Collareta, A., 2024. An explainable Convolutional Neural Network approach to fossil shark tooth identification Bollettino Della Societa Paleontologica Italiana, v. 63
Doi: 10.4435/BSPI.2024.15
Oliva, V., Martone, A., Fanelli, G., Domschke, K., Minelli, A., Gennarelli, M., Martini, P., Bortolomasi, M., Maron, E., Squassina, A., Pisanu, C., Kasper, S., Zohar, J., Souery, D., Montgomery, S., Albani, D., Forloni, G., Ferentinos, P., Rujescu, D., Mendlewicz, J., De Ronchi, D., Baune, BT., Potier, MC., van Westrhenen, R., Rybakowski, F., Mehta, D., Dierssen, M., Janzing, JGE., Liò, P., Serretti, A. and Fabbri, C., 2024. Polygenic scores of subcortical brain volumes as possible modulators of treatment response in depression Neuroscience Applied, v. 3
Doi: 10.1016/j.nsa.2024.103937
Cao, P. and Lio, P., 2024. GenRec: Generative Personalized Sequential Recommendation. CoRR, v. abs/2407.21191
Ceccarelli, F., Liò, P. and Holden, SB., 2024. AnnoGCD: a generalized category discovery framework for automatic cell type annotation NAR Genomics and Bioinformatics, v. 6
Doi: 10.1093/nargab/lqae166
Bazaga, A., Liò, P. and Micklem, G., 2024. UNSUPERVISED PRETRAINING FOR FACT VERIFICATION BY LANGUAGE MODEL DISTILLATION 12th International Conference on Learning Representations Iclr 2024,
Luca, VMD., Longa, A., Passerini, A. and Liò, P., 2024. xAI-Drop: Don’t Use What You Cannot Explain. CoRR, v. abs/2407.20067
Siebenmorgen, T., Menezes, F., Benassou, S., Merdivan, E., Didi, K., Mourão, ASD., Kitel, R., Liò, P., Kesselheim, S., Piraud, M., Theis, FJ., Sattler, M. and Popowicz, GM., 2024. MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. Nat Comput Sci, v. 4
Doi: 10.1038/s43588-024-00627-2
Duta, I. and Liò, P., 2024. SPHINX: Structural Prediction using Hypergraph Inference Network. CoRR, v. abs/2410.03208
Shen, X., Lio, P., Yang, L., Yuan, R., Zhang, Y. and Peng, C., 2024. Graph Rewiring and Preprocessing for Graph Neural Networks Based on Effective Resistance IEEE Transactions on Knowledge and Data Engineering, v. 36
Doi: 10.1109/TKDE.2024.3397692
Lawrence, E., El-Shazly, A., Seal, S., Joshi, CK., Liò, P., Singh, S., Bender, A., Sormanni, P. and Greenig, M., 2024. Understanding Biology in the Age of Artificial Intelligence. CoRR, v. abs/2403.04106
Yang, Z., Zhang, G., Wu, J., Yang, J., Sheng, QZ., Xue, S., Zhou, C., Aggarwal, C., Peng, H., Hu, W., Hancock, E. and Liò, P., 2024. State of the Art and Potentialities of Graph-level Learning ACM Computing Surveys, v. 57
Doi: 10.1145/3695863
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023 (Published online). Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the AAAI Conference on Artificial Intelligence, v. 37
Doi: 10.1609/aaai.v37i8.26090
Breger, A., Selby, I., Roberts, M., Babar, J., Gkrania-Klotsas, E., Preller, J., Escudero Sanchez, L., Rudd, J., Aston, J., Weir-McCall, J., Sala, E. and Schoenlieb, C., 2023 (Accepted for publication). A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data Scientific data,
Doi: 10.1038/s41597-023-02340-7
Mei, X., Yang, Y., Li, M., Huang, C., Zhang, K. and Lió, P., 2023. A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution. CoRR, v. abs/2306.01500
Lu, X., Liu, C., Zhu, S., Mao, Y., Lio, P. and Hui, P., 2023. RLPTO: A Reinforcement Learning-Based Performance-Time Optimized Task and Resource Scheduling Mechanism for Distributed Machine Learning IEEE Transactions on Parallel and Distributed Systems, v. 34
Doi: 10.1109/TPDS.2023.3317388
Petrović, A., Nikolić, M., Bugarić, U., Delibašić, B. and Lio, P., 2023. Controlling highway toll stations using deep learning, queuing theory, and differential evolution Engineering Applications of Artificial Intelligence, v. 119
Doi: 10.1016/j.engappai.2022.105683
Dimitri, GM., Spasov, SE., Duggento, A., Passamonti, L., Liò, P. and Toschi, N., 2023. Multimodal and multicontrast image fusion via deep generative models. CoRR, v. abs/2303.15963
Brant, I., Norcliffe, A. and Liò, P., 2023. Fourier Neural Differential Equations for learning Quantum Field Theories. CoRR, v. abs/2311.17250
Jiang, Y., Ding, Q., Wang, YG., Liò, P. and Zhang, X., 2023. VISION GRAPH U-NET: GEOMETRIC LEARNING ENHANCED ENCODER FOR MEDICAL IMAGE SEGMENTATION AND RESTORATION Inverse Problems and Imaging, v. 2023
Doi: 10.3934/ipi.2023049
Campbell, A., Zippo, AG., Passamonti, L., Toschi, N. and Liò, P., 2023. DBGSL: Dynamic Brain Graph Structure Learning Proceedings of Machine Learning Research, v. 227
Liu, L., Cheng, Y., Chen, D., He, J., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. Traffic Video Object Detection using Motion Prior. CoRR, v. abs/2311.10092
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lio, P., Maggini, M. and Melacci, S., 2023. Logic Explained Networks NEURAL-SYMBOLIC LEARNING AND REASONING 2023, NESY 2023,
Xuanyuan, H., Barbiero, P., Georgiev, D., Magister, LC. and Liò, P., 2023. Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023, v. 37
Doi: 10.1609/aaai.v37i9.26267
Jürß, J., Magister, LC., Barbiero, P., Liò, P. and Simidjievski, N., 2023. Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts. CoRR, v. abs/2311.15112
COVID-19 Host Genetics Initiative, , 2023. A second update on mapping the human genetic architecture of COVID-19. Nature, v. 621
Doi: 10.1038/s41586-023-06355-3
Sathyanarayanan, A., Mueller, TT., Ali Moni, M., Schueler, K., ECNP TWG Network members, , Baune, BT., Lio, P., Mehta, D., Baune, BT., Dierssen, M., Ebert, B., Fabbri, C., Fusar-Poli, P., Gennarelli, M., Harmer, C., Howes, OD., Janzing, JGE., Lio, P., Maron, E., Mehta, D., Minelli, A., Nonell, L., Pisanu, C., Potier, M-C., Rybakowski, F., Serretti, A., Squassina, A., Stacey, D., van Westrhenen, R. and Xicota, L., 2023. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol, v. 69
Doi: 10.1016/j.euroneuro.2023.01.001
Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Graph Signal Compression. CoRR, v. abs/2308.11068
Yeh, KF., Flood, PDL., Redman, W. and Liò, P., 2023. Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing. CoRR, v. abs/2305.09060
Azevedo, T., Bethlehem, RAI., Whiteside, DJ., Swaddiwudhipong, N., Rowe, JB., Lió, P., Rittman, T. and Alzheimer’s Disease Neuroimaging Initiative, , 2023. Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank. Commun Med (Lond), v. 3
Doi: 10.1038/s43856-023-00313-w
Bardozzo, F., Terlizzi, A., Liò, P. and Tagliaferri, R., 2023. ElegansNet: a brief scientific report and initial experiments. CoRR, v. abs/2304.13538
Waqas, M., Aziz, S., Liò, P., Khan, Y., Ali, A., Iqbal, A., Khan, F. and Almajhdi, FN., 2023. Immunoinformatics design of multivalent epitope vaccine against monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets. Front Immunol, v. 14
Doi: 10.3389/fimmu.2023.1091941
Zhao, X., Stärk, H., Beaini, D., Liò, P. and Zhao, Y., 2023. Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. CoRR, v. abs/2301.11517
Bazaga, A., Liò, P. and Micklem, G., 2023. Language Model Knowledge Distillation for Efficient Question Answering in Spanish. CoRR, v. abs/2312.04193
Yang, J. and Liò, P., 2023. Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network. CoRR, v. abs/2312.00661
Shadbahr, T., Roberts, M., Stanczuk, J., Gilbey, J., Teare, P., Dittmer, S., Thorpe, M., Torné, RV., Sala, E., Lió, P., Patel, M., Preller, J., AIX-COVNET Collaboration, , Rudd, JHF., Mirtti, T., Rannikko, AS., Aston, JAD., Tang, J. and Schönlieb, C-B., 2023. The impact of imputation quality on machine learning classifiers for datasets with missing values. Commun Med (Lond), v. 3
Doi: 10.1038/s43856-023-00356-z
Zhu, M., Kobalczyk, K., Petrovic, A., Nikolic, M., Schaar, MVD., Delibasic, B. and Liò, P., 2023. Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces. CoRR, v. abs/2311.10051
Bisercic, A., Nikolic, M., Schaar, MVD., Delibasic, B., Liò, P. and Petrovic, A., 2023. Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models. CoRR, v. abs/2306.05052
Chen, J., Wang, Y., Bodnar, C., Ying, R., Lio, P. and Wang, Y., 2023. Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation. CoRR, v. abs/2311.05767
Giannini, F., Fioravanti, S., Keskin, O., Lupidi, AM., Magister, LC., Lió, P. and Barbiero, P., 2023. Interpretable Graph Networks Formulate Universal Algebra Conjectures Advances in Neural Information Processing Systems, v. 36
Viñas, R., Joshi, CK., Georgiev, D., Lin, P., Dumitrascu, B., Gamazon, ER. and Liò, P., 2023. Hypergraph factorization for multi-tissue gene expression imputation. Nat Mach Intell, v. 5
Doi: 10.1038/s42256-023-00684-8
Villaforesta, AFD., Magister, LC., Barbiero, P. and Liò, P., 2023. Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians. CoRR, v. abs/2312.02225
Bazaga, A., Liò, P. and Micklem, G., 2023. SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation. CoRR, v. abs/2310.18376
Dong, T., Jamnik, M. and Liò, P., 2025 (Published online). Neural Reasoning for Sure Through Constructing Explainable Models Proceedings of the AAAI Conference on Artificial Intelligence, v. 39
Doi: 10.1609/aaai.v39i11.33262
Sun, Z., Harit, A., Yu, J., Wang, J. and Liò, P., 2025. Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks Www Companion 2025 Companion Proceedings of the ACM Web Conference 2025,
Doi: 10.1145/3701716.3715577
Dong, T., Jamnik, M. and Liò, P., 2025. Neural Reasoning for Sure Through Constructing Explainable Models Proceedings of the Aaai Conference on Artificial Intelligence, v. 39
Doi: 10.1609/aaai.v39i11.33262
Li, M., Fang, Y., Wang, Y., Feng, H., Gu, Y., Bai, L. and Lio, P., 2025. Deep Hypergraph Neural Networks with Tight Framelets Proceedings of the Aaai Conference on Artificial Intelligence, v. 39
Doi: 10.1609/aaai.v39i17.34023
Cretu, M., Harris, C., Igashov, I., Schneuing, A., Segler, M., Correia, B., Roy, J., Bengio, E. and Liò, P., 2025. SYNFLOWNET: DESIGN OF DIVERSE AND NOVEL MOLECULES WITH SYNTHESIS CONSTRAINTS 13th International Conference on Learning Representations Iclr 2025,
Li, M., Gu, Y., Wang, Y., Fang, Y., Bai, L., Zhuang, X. and Lio, P., 2025. When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline Proceedings of the Aaai Conference on Artificial Intelligence, v. 39
Doi: 10.1609/aaai.v39i17.34022
Joshi, CK., Jamasb, AR., Viñas, R., Harris, C., Mathis, SV., Morehead, A., Anand, R. and Liò, P., 2025. GRNADE: GEOMETRIC DEEP LEARNING FOR 3D RNA INVERSE DESIGN 13th International Conference on Learning Representations Iclr 2025,
Mamalakis, M., De Vareilles, H., Murray, GK., Lio, P. and Suckling, J., 2025. The Explanation Necessity for Healthcare AI 2025 IEEE Symposium on Trustworthy Explainable and Responsible Computational Intelligence Citrex Companion 2025,
Doi: 10.1109/CITRExCompanion65208.2025.10981502
Bardozzo, F., Fiore, P., Liò, P. and Tagliaferri, R., 2025. FP-Elegans M1: Feature Pyramid Reservoir Connectome Transformers and Multi-backbone Feature Extractors for MEDMNIST2D-V2 Lecture Notes in Computer Science, v. 15276 LNBI
Doi: http://doi.org/10.1007/978-3-031-89704-7_9
Aucello, R., Licheri, N., Rosso, E., Ferrero, G., Gepiro Contaldo, S., Pernice, S., Lió, P., Cordero, F. and Beccuti, M., 2025. Synergy Between Mechanistic Modelling and Ensemble Feature Selection Approaches to Explore Multiscale Biological Heterogeneity Lecture Notes in Computer Science, v. 14513 LNBI
Doi: http://doi.org/10.1007/978-3-031-90714-2_9
Erriquez, E., Crisafi, F., Ragni, A., Monti, F., Di Noia, G., Rahman, M., Azevedo, T., Vali, M., Vanna, R., Pertzborn, D., Pelzel, D., Weyer, U., Xylander, A., Muehlig, A., Guntinas-Lichius, O., Liò, P., Cerullo, G. and Negro, M., 2025. Multiplex stimulated Raman histology: a route towards the application to the clinical routine Advanced Chemical Microscopy for Life Science and Translational Medicine 2025,
Doi: 10.1117/12.3043003
Giusti, L., Reu, T., Ceccarelli, F., Bodnar, C. and Liò, P., 2024. Topological Message Passing for Higher – Order and Long – Range Interactions Proceedings of the International Joint Conference on Neural Networks,
Doi: 10.1109/IJCNN60899.2024.10650343
Zaghen, O., Longa, A., Azzolin, S., Telyatnikov, L., Passerini, A. and Liò, P., 2024. Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians Proceedings of Machine Learning Research, v. 251
Ceccarelli, F., Prinzi, F., Liò, P., Vitabile, S. and Holden, SB., 2024. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI Proceedings of the International Joint Conference on Neural Networks,
Doi: 10.1109/IJCNN60899.2024.10650117
Bazaga, A., Lio, P. and Micklem, G., 2024. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs. EMNLP (Findings),
Huang, K., Cao, W., Ta, H., Xiao, X. and Liò, P., 2024. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach Www 2024 Proceedings of the ACM Web Conference,
Doi: 10.1145/3589334.3645705
Bazaga, A., Lio, P. and Micklem, G., 2024. Language Model Knowledge Distillation for Efficient Question Answering in Spanish. Tiny Papers @ ICLR,
Huang, K., Wang, YG., Li, M. and Lio, P., 2024. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing. ICML,
Ceccarelli, F., Prinzi, F., Liò, P., Vitabile, S. and Holden, SB., 2024. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI. IJCNN,
Denker, A., Vargas, F., Padhy, S., Didi, K., Mathis, S., Dutordoir, V., Barbano, R., Mathieu, E., Komorowska, UJ. and Lio, P., 2024. DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transform Advances in Neural Information Processing Systems, v. 37
Ceccarelli, F., Giusti, L., Holden, S. and Lio, P., 2024. Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMs
Doi: 10.5220/0012453600003654
Iuliano, A., Lio, P., Manfredi, G. and Romaniello, F., 2024. Denoising Probabilistic Diffusion Models for Synthetic Healthcare Image Generation 2024 IEEE International Workshop on Metrology for Living Environment Metrolivenv 2024 Proceedings,
Doi: 10.1109/MetroLivEnv60384.2024.10615511
Talone, B., Crisafi, F., Ragni, A., Di Noia, G., Rahman, M., Azevedo, T., Yang, J., Petrov, I., Vali, M., Vanna, R., Pertzborn, D., Hoffmann, F., Guntinas-Lichius, O., Liò, P., Ferrari, AC., Cerullo, G. and Negro, M., 2024. Broadband coherent Raman platform for stimulated Raman histology Advanced Chemical Microscopy for Life Science and Translational Medicine 2024,
Doi: http://doi.org/10.1117/12.3002725
Zhao, X., Li, Z., Shen, M., Stan, GB., Liò, P. and Zhao, Y., 2024. Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation Frontiers in Artificial Intelligence and Applications, v. 392
Doi: http://doi.org/10.3233/FAIA240652
Komorowska, UJ., Mathis, S., Didi, K., Vargas, F., Lio, P. and Jamnik, M., 2024. Dynamics-Informed Protein Design with Structure Conditioning
Shen, Y., Chen, Z., Mamalakis, M., Liu, Y., Li, T., Su, Y., He, J., Liò, P. and Wang, YG., 2024. TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering Proceedings 2024 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2024,
Doi: 10.1109/BIBM62325.2024.10822695
Bazaga, A., Lio, P. and Micklem, G., 2024. Unsupervised Pretraining for Fact Verification by Language Model Distillation. ICLR,
Papamarkou, T., Birdal, T., Bronstein, M., Carlsson, G., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Liò, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Ramamurthy, KN., Rieck, B., Scardapane, S., Schaub, MT., Veličković, P., Wang, B., Wang, Y., Wei, G-W. and Zamzmi, G., 2024. Position: Topological Deep Learning is the New Frontier for Relational Learning. Proc Mach Learn Res, v. 235
Kovac, V., Bekkers, EJ., Liò, P. and Eijkelboom, F., 2024. E(n) Equivariant Message Passing Cellular Networks Proceedings of Machine Learning Research, v. 251
Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2023. Graph Classification Gaussian Processes via Spectral Features Proceedings of Machine Learning Research, v. 216
Barbiero, P., Ciravegna, G., Giannini, F., Zarlenga, ME., Magister, LC., Tonda, A., Lió, P., Precioso, F., Jamnik, M. and Marra, G., 2023. Interpretable Neural-Symbolic Concept Reasoning Proceedings of Machine Learning Research, v. 202
Keskin, O., Lupidi, A., Giannini, F., Fioravanti, S., Magister, LC., Barbiero, P. and Liò, P., 2023. Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach Proceedings of Machine Learning Research, v. 221
Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P. and Liò, P., 2023. Topological Network Traffic Compression Gnnet 2023 Proceedings of the 2nd Graph Neural Networking Workshop 2023,
Doi: 10.1145/3630049.3630172
Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and Schaar, MVD., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS,
Bi, X., Tang, S., Yang, Z., Deng, X., Xiao, B. and Lio, P., 2023. MMCTNet: Multi-Modal Cony-Transformer Network for Predicting Good and Poor Outcomes in Cardiac Arrest Patients Computing in Cardiology,
Doi: 10.22489/CinC.2023.099
Lu, X., Zhang, X. and Lio, P., 2023. GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network ACM Web Conference 2023 Companion of the World Wide Web Conference Www 2023,
Doi: 10.1145/3543873.3587329
Norcliffe, A., Cebere, B., Imrie, F., Liò, P. and van der Schaar, M., 2023. SurvivalGAN: Generating Time-to-Event Data for Survival Analysis Proceedings of Machine Learning Research, v. 206
Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. and Lió, P., 2023. A Federated Learning Benchmark for Drug-Target Interaction ACM Web Conference 2023 Companion of the World Wide Web Conference Www 2023,
Doi: 10.1145/3543873.3587687
Sun, Z., Harit, A., Cristea, AI., Wang, J. and Lio, P., 2023. A Rewiring Contrastive Patch PerformerMixer Framework for Graph Representation Learning Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023,
Doi: 10.1109/BigData59044.2023.10386951
Duta, I., Cassarà, G., Silvestri, F. and Lió, P., 2023. Sheaf Hypergraph Networks. NeurIPS,
Jang, A., Patel, S., Patel, S., Shah, S. and Lio, P., 2023. Predicting mortality in systemic sclerosis patients using machine learning approaches JOURNAL OF INVESTIGATIVE DERMATOLOGY, v. 143
Zou, X., Zhao, X., Lio, P. and Zhao, Y., 2023. Will More Expressive Graph Neural Networks Do Better on Generative Tasks? LoG, v. 231
Liu, L., Prost, J., Zhu, L., Papadakis, N., Liò, P., Schönlieb, C-B. and Avilés-Rivero, AI., 2023. SCOTCH and SODA: A Transformer Video Shadow Detection Framework. CVPR,
Georgiev, D., Numeroso, D., Bacciu, D. and Lio, P., 2023. Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG, v. 231
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Liò, P. and Bronstein, M., 2023. On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology Proceedings of Machine Learning Research, v. 202
Borde, HSDO., Kazi, A., Barbero, F. and Liò, P., 2023. Latent Graph Inference using Product Manifolds. ICLR,
Azzolin, S., Longa, A., Barbiero, P., Liò, P. and Passerini, A., 2023. Global Explainability of GNNs via Logic Combination of Learned Concepts. ICLR,
Joshi, CK., Bodnar, C., Mathis, SV., Cohen, T. and Liò, P., 2023. On the Expressive Power of Geometric Graph Neural Networks Proceedings of Machine Learning Research, v. 202
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023. Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023, v. 37
Doi: 10.1609/aaai.v37i8.26090
Sun, Z., Cristea, AI., Lio, P. and Yu, J., 2023. Adaptive Distance Message Passing From the Multi-Relational Edge View. Tiny Papers @ ICLR,
Liu, L., Prost, J., Zhu, L., Papadakis, N., Liò, P., Schönlieb, CB. and Aviles-Rivero, AI., 2023. SCOTCH and SODA: A Transformer Video Shadow Detection Framework Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2023-June
Doi: 10.1109/CVPR52729.2023.01007
Tilly, T., Auckland, K., Nibhani, R., Martin, J., Nihr, N., Morrell, NW., Lio’, P. and Graf, S., 2022. Deep learning of regulatory regions discovers enhancer variants implicated in PAH EUROPEAN RESPIRATORY JOURNAL, v. 60
Doi: 10.1183/13993003.congress-2022.2543
Yi, K., Chen, J., Wang, YG., Zhou, B., Liò, P., Fan, Y. and Hamann, J., 2022. APPROXIMATE EQUIVARIANCE SO(3) NEEDLET CONVOLUTION Proceedings of Machine Learning Research, v. 196
Liu, L., Huang, Z., Liò, P., Schönlieb, CB. and Aviles-Rivero, AI., 2022. You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, v. 13386 LNCS
Doi: 10.1007/978-3-031-11203-4_21
Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations Ceur Workshop Proceedings, v. 3318
Day, B., Viñas, R., Simidjievski, N. and Liò, P., 2022. Attentional Meta-learners for Few-shot Polythetic Classification Proceedings of Machine Learning Research, v. 162
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S. and Liò, P., 2022. 3D Infomax improves GNNs for Molecular Property Prediction Proceedings of Machine Learning Research, v. 162
Fan, J., Pei, J., Bi, X., Xiao, B. and Lio, P., 2022. Context Correlation Aware Network for Cardiac Segmentation Proceedings IEEE International Conference on Multimedia and Expo, v. 2022-July
Doi: 10.1109/ICME52920.2022.9859985
Imrie, F., Norcliffe, A., Lió, P. and Schaar, MVD., 2022. Composite Feature Selection Using Deep Ensembles. NeurIPS,
Lu, X. and Lio, P., 2022. Second International Workshop On Artificial Intelligence To Security – AITS 2022 Proceedings 52nd Annual IEEE IFIP International Conference on Dependable Systems and Networks Workshop Volume Dsn W 2022,
Doi: 10.1109/DSN-W54100.2022.00010
Buffelli, D., Lió, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. NeurIPS,
Manouchehrinia, A., Ebrahimi, A., Wiil, UK., Kiani, NA., Lio, P., Olsson, T. and Kockum, I., 2022. A susceptibility network analysis of disease pathways leading to multiple sclerosis MULTIPLE SCLEROSIS JOURNAL, v. 28
Zarlenga, ME., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., 2022. Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off Advances in Neural Information Processing Systems, v. 35
Qian, P., Yang, J., Lió, P., Hu, P. and Qi, H., 2022. Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, v. 13413 LNCS
Doi: 10.1007/978-3-031-12053-4_5
Opolka, FL. and Liò, P., 2022. Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes Proceedings of Machine Learning Research, v. 151
Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M. and Melacci, S., 2022. Entropy-Based Logic Explanations of Neural Networks Proceedings of the 36th Aaai Conference on Artificial Intelligence Aaai 2022, v. 36
Doi: 10.1609/aaai.v36i6.20551
Opolka, FL., Zhi, YC., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets Proceedings of Machine Learning Research, v. 151
Georgiev, D., Barbiero, P., Kazhdan, D., Veličković, P. and Liò, P., 2022. Algorithmic Concept-Based Explainable Reasoning Proceedings of the 36th Aaai Conference on Artificial Intelligence Aaai 2022, v. 36
Doi: 10.1609/aaai.v36i6.20623
Bodnar, C., Giovanni, FD., Chamberlain, BP., Lió, P. and Bronstein, MM., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS,
Opolka, FL., Zhi, Y-C., Liò, P. and Dong, X., 2022. Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. AISTATS, v. 151
Jain, R., Ciravegna, G., Barbiero, P., Giannini, F., Buffelli, D. and Lio, P., 2022. Extending Logic Explained Networks to Text Classification Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing Emnlp 2022,
Doi: 10.18653/v1/2022.emnlp-main.604
Pándy, M., Qiu, W., Corso, G., Veličković, P., Ying, R., Leskovec, J. and Liò, P., 2022. Learning Graph Search Heuristics Proceedings of Machine Learning Research, v. 198
He, Y., Veličković, P., Liò, P. and Deac, A., 2022. Continuous Neural Algorithmic Planners Proceedings of Machine Learning Research, v. 198
Aghakhanyan, G., Barucci, A., Colantonio, S., Colcelli, V., Pasquinelli, F., Gini, R., Lio, P., Mazzei, M., Erba, P., Miele, V. and Neri, E., 2022. NAVIGATOR: An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, v. 49
Tailor, SA., Opolka, FL., Liò, P. and Lane, ND., 2022. DO WE NEED ANISOTROPIC GRAPH NEURAL NETWORKS? Iclr 2022 10th International Conference on Learning Representations,
Lu, X., Zhao, J. and Lio, P., 2022. Robust android malware detection based on subgraph network and denoising GCN network Mobisys 2022 Proceedings of the 2022 20th Annual International Conference on Mobile Systems Applications and Services,
Doi: 10.1145/3498361.3538778
Buffelli, D., Liò, P. and Vandin, F., 2022. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks Advances in Neural Information Processing Systems, v. 35
Imrie, F., Norcliffe, A., Liò, P. and van der Schaar, M., 2022. Composite Feature Selection Using Deep Ensembles Advances in Neural Information Processing Systems, v. 35
Lu, X., Pang, R. and Lio, P., 2022. Poster: CFMAP: A Robust CPU Clock Fingerprint Model for Device Authentication Proceedings of the ACM Conference on Computer and Communications Security,
Doi: 10.1145/3548606.3563528
Bodnar, C., Di Giovanni, F., Chamberlain, BP., Liò, P. and Bronstein, M., 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Advances in Neural Information Processing Systems, v. 35
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts. NeurIPS,
Jamasb, AR., Viñas, R., Ma, EJ., Harris, C., Huang, K., Hall, D., Lió, P. and Blundell, TL., 2022. Graphein – a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks Advances in Neural Information Processing Systems, v. 35
Barbero, F., Bodnar, C., de Ocáriz Borde, HS., Bronstein, M., Veličković, P. and Liò, P., 2022. SH EA F NEU RA L NETWO RK S W ITH CO NN ECTIO N LAPLACIANS Proceedings of Machine Learning Research, v. 196
Campbell, A., Qendro, L., Liò, P. and Mascolo, C., 2022. ROBUST AND EFFICIENT UNCERTAINTY AWARE BIOSIGNAL CLASSIFICATION VIA EARLY EXIT ENSEMBLES ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, v. 2022-May
Doi: 10.1109/ICASSP43922.2022.9746330
Buterez, D., Janet, JP., Kiddle, SJ., Oglic, D. and Liò, P., 2022. Graph Neural Networks with Adaptive Readouts Advances in Neural Information Processing Systems, v. 35
Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, MA., Jamnik, M. and Liò, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations. CIKM Workshops, v. 3318
Scherer, P., Liò, P. and Jamnik, M., 2022. Distributed Representations of Graphs for Drug Pair Scoring Proceedings of Machine Learning Research, v. 198
Zhou, B., Liu, X., Liu, Y., Huang, Y., Liò, P. and Wang, YG., 2022. Well-conditioned Spectral Transforms for Dynamic Graph Representation Proceedings of Machine Learning Research, v. 198
Bodnar, C., Frasca, F., Otter, N., Wang, YG., Lio, P., Montufar, G. and Bronstein, M., 2021. Weisfeiler and Lehman Go Cellular: CW Networks ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021),
Bodnar, C., Frasca, F., Wang, YG., Otter, N., Montúfar, G., Liò, P. and Bronstein, MM., 2021. Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Proceedings of Machine Learning Research, v. 139
Corso, G., Ying, R., Pandy, M., Veličković, P., Leskovec, J. and Lio, P., 2021. Neural Distance Embeddings for Biological Sequences Advances in Neural Information Processing Systems, v. 22
Lu, X. and Lio, P., 2021. International Workshop on Application of Intelligent Technology in Security – AITS 2021 Proceedings 51st Annual IEEE IFIP International Conference on Dependable Systems and Networks Workshops Dsn W 2021,
Doi: 10.1109/DSN-W52860.2021.00008
Zheng, X., Zhou, B., Gao, J., Wang, YG., Liò, P., Li, M. and Montúfar, G., 2021. How Framelets Enhance Graph Neural Networks Proceedings of Machine Learning Research, v. 139
Qendro, L., Campbell, A., Liò, P. and Mascolo, C., 2021. Early Exit Ensembles for Uncertainty Quantification Proceedings of Machine Learning Research, v. 158
Norcliffe, A., Bodnar, C., Day, B., Moss, J. and Liò, P., 2021. Neural ODE Processes
Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches