Andreas Loukas
23 papers · 2017–2024 · 6 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π§ Keyword Pioneer π Academic Marathon (7)
π
Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(10)
π§
Keyword Pioneer
πΊ
Lone Wolf
(4)
π§¬
Topic Evolution
π
Keyword Champion
(3)
ποΈ
Keyword Collector
(110)
β‘
Prolific Year
(6)
π
Century Club
(23)
π
Trend Setter
π₯
Unstoppable
(8)
β
The Questioner
(2)
Conferences
NIPS (10)
ICML (7)
ICLR (3)
AISTATS (1)
IJCAI (1)
JMLR (1)
Top co-authors
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