← Authors

Andreas Loukas

23 papers · 2017–2024 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (11) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (7) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐺 Lone Wolf (4) 🧬 Topic Evolution πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (110) ⚑ Prolific Year (6) πŸ’Ž Century Club (23) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (8) ❓ The Questioner (2)

Conferences

NIPS (10) ICML (7) ICLR (3) AISTATS (1) IJCAI (1) JMLR (1)

Top co-authors

Kyunghyun Cho (4) Jean-Baptiste Cordonnier (3) Stefanie Jegelka (3) Vladimir Gligorijevic (3) Nikolaos Karalias (3) Yan Wu (2) Pierre Vandergheynst (2) Isidro Hotzel (2) Richard Bonneau (2) Karolis Martinkus (2)

Keywords

graph neural network (4) graph coarsening (3) combinatorial optimization (2) graph laplacian (2) message passing (2) unsupervised learning (2) generalization bound (2) spectral approximation (2) spectral clustering (2) stochastic gradient descent (1) graph classification (1) graph clustering (1) gradient-based optimization (1) logical reasoning (1) representation learning (1) statistical learning (1) trajectory prediction (1) principal component analysis (1) sample complexity (1) question answering (1)

Papers

Protein Discovery with Discrete Walk-Jump Sampling ICLR 2024
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient NIPS 2024
Towards Understanding and Improving GFlowNet Training ICML 2023
AbDiffuser: full-atom generation of in-vitro functioning antibodies NIPS 2023
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning ICML 2023
On the generalization of learning algorithms that do not converge NIPS 2022
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators ICML 2022
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions NIPS 2022
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning NIPS 2021
Attention is not all you need: pure attention loses rank doubly exponentially with depth ICML 2021
What training reveals about neural network complexity NIPS 2021
Partition and Code: learning how to compress graphs NIPS 2021
How hard is to distinguish graphs with graph neural networks? NIPS 2020
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs NIPS 2020
Building powerful and equivariant graph neural networks with structural message-passing NIPS 2020
Graph Coarsening with Preserved Spectral Properties AISTATS 2020
On the Relationship between Self-Attention and Convolutional Layers ICLR 2020
What graph neural networks cannot learn: depth vs width ICLR 2020
Extrapolating Paths with Graph Neural Networks IJCAI 2019
Graph Reduction with Spectral and Cut Guarantees JMLR 2019
Fast Approximate Spectral Clustering for Dynamic Networks ICML 2018
Spectrally Approximating Large Graphs with Smaller Graphs ICML 2018
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? ICML 2017