Pietro LiΓ³
67 papers · 2017–2026 · 9 conferences · across top CS/AI conferences
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(25)
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(53)
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Conferences
NIPS (17)
ICML (16)
ICLR (13)
AAAI (11)
AISTATS (4)
EMNLP (2)
MIDL (2)
CVPR (1)
UAI (1)
Top co-authors
Keywords
graph neural network
(18)
hypergraph neural network
(6)
neural network
(5)
graph classification
(4)
representation learning
(4)
weisfeiler-lehman test
(4)
graph convolution
(4)
message passing
(4)
generative model
(3)
feature selection
(3)
gaussian process
(3)
framelet transform
(3)
spectral graph wavelet
(2)
protein structure
(2)
hyperbolic space
(2)
variational inference
(2)
simplicial complex
(2)
graph signal processing
(2)
graph embedding
(2)
geometric deep learning
(2)
Papers
High-Pass Matters: Theoretical Insights and Sheaflet-Based Design for Hypergraph Neural Networks
AAAI 2026
Permutation Equivariant Framelet-based Hypergraph Neural Networks
AAAI 2026
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
ICML 2025
Stochastic Encodings for Active Feature Acquisition
ICML 2025
EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
ICML 2025
NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones
ICML 2025
SPHINX: Structural Prediction using Hypergraph Inference Network
ICML 2025
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
ICLR 2025
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
ICLR 2025
gRNAde: Geometric Deep Learning for 3D RNA inverse design
ICLR 2025
Deep Hypergraph Neural Networks with Tight Framelets
AAAI 2025
Neural Reasoning for Sure Through Constructing Explainable Models
AAAI 2025
When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline
AAAI 2025
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
ICML 2025
Unsupervised Pretraining for Fact Verification by Language Model Distillation
ICLR 2024
DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform
NIPS 2024
Deep Equilibrium Algorithmic Reasoning
NIPS 2024
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs
EMNLP 2024
Dynamics-Informed Protein Design with Structure Conditioning
ICLR 2024
Evaluating Representation Learning on the Protein Structure Universe
ICLR 2024
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
ICML 2024
Position: Topological Deep Learning is the New Frontier for Relational Learning
ICML 2024
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
AAAI 2023
Latent Graph Inference using Product Manifolds
ICLR 2023
Global Explainability of GNNs via Logic Combination of Learned Concepts
ICLR 2023
DBGDGM: Dynamic Brain Graph Deep Generative Model
MIDL 2023
Graph classification Gaussian processes via spectral features
UAI 2023
Interpretable Graph Networks Formulate Universal Algebra Conjectures
NIPS 2023
Sheaf Hypergraph Networks
NIPS 2023
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
AISTATS 2023
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
CVPR 2023
Graph Denoising Diffusion for Inverse Protein Folding
NIPS 2023
DBGSL: Dynamic Brain Graph Structure Learning
MIDL 2023
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis
AAAI 2023
On the Expressive Power of Geometric Graph Neural Networks
ICML 2023
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
ICML 2023
Interpretable Neural-Symbolic Concept Reasoning
ICML 2023
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
NIPS 2022
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
AISTATS 2022
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes
AISTATS 2022
Extending Logic Explained Networks to Text Classification
EMNLP 2022
Do We Need Anisotropic Graph Neural Networks?
ICLR 2022
Composite Feature Selection Using Deep Ensembles
NIPS 2022
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
NIPS 2022
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
NIPS 2022
Attentional Meta-learners for Few-shot Polythetic Classification
ICML 2022
3D Infomax improves GNNs for Molecular Property Prediction
ICML 2022
Graph Neural Networks with Adaptive Readouts
NIPS 2022
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
NIPS 2022
Entropy-Based Logic Explanations of Neural Networks
AAAI 2022
Algorithmic Concept-Based Explainable Reasoning
AAAI 2022
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
ICML 2021
How Framelets Enhance Graph Neural Networks
ICML 2021
Neural ODE Processes
ICLR 2021
Neural Distance Embeddings for Biological Sequences
NIPS 2021
Weisfeiler and Lehman Go Cellular: CW Networks
NIPS 2021
Directional Graph Networks
ICML 2021
Proximal Distilled Evolutionary Reinforcement Learning
AAAI 2020
On Second Order Behaviour in Augmented Neural ODEs
NIPS 2020
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
ICLR 2020
Principal Neighbourhood Aggregation for Graph Nets
NIPS 2020
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
NIPS 2020
Path Integral Based Convolution and Pooling for Graph Neural Networks
NIPS 2020
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
AAAI 2019
Deep Graph Infomax
ICLR 2019
Graph Attention Networks
ICLR 2018
Bayesian Hybrid Matrix Factorisation for Data Integration
AISTATS 2017