Loukas, Andreas

27 publications

ICLRW 2025 Generalizing to Any Diverse Distribution: Uniformity & Rebalancing Andreas Loukas, Karolis Martinkus, Edward Wagstaff, Kyunghyun Cho

NeurIPS 2024 Implicitly Guided Design with PropEn: Match Your Data to Follow the Gradient Nataša Tagasovska, Vladimir Gligorijević, Kyunghyun Cho, Andreas Loukas

ICLR 2024 Protein Discovery with Discrete Walk-Jump Sampling Nathan C. Frey, Dan Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi

NeurIPS 2023 AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas

ICML 2023 Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas

ICLRW 2023 Learning Protein Family Manifolds with Smoothed Energy-Based Models Nathan C. Frey, Dan Berenberg, Joseph Kleinhenz, Isidro Hotzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi

ICML 2023 Towards Understanding and Improving GFlowNet Training Max W Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani

NeurIPSW 2022 A Pareto-Optimal Compositional Energy-Based Model for Sampling and Optimization of Protein Sequences Natasa Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hotzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijevic

ICLRW 2022 Deep Sharpening of Topological Features for De Novo Protein Design Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, Pierre Vandergheynst, Micheal Bronstein, Bruno Correia

NeurIPS 2022 Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions Nikolaos Karalias, Joshua W. Robinson, Andreas Loukas, Stefanie Jegelka

NeurIPS 2022 On the Generalization of Learning Algorithms That Do Not Converge Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka

ICML 2022 SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-Shot Graph Generators Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer

ICML 2021 Attention Is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas

NeurIPS 2021 Partition and Code: Learning How to Compress Graphs Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael Bronstein

NeurIPS 2021 SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas

NeurIPS 2021 What Training Reveals About Neural Network Complexity Andreas Loukas, Marinos Poiitis, Stefanie Jegelka

NeurIPS 2020 Building Powerful and Equivariant Graph Neural Networks with Structural Message-Passing Clément Vignac, Andreas Loukas, Pascal Frossard

NeurIPS 2020 Erdos Goes Neural: An Unsupervised Learning Framework for Combinatorial Optimization on Graphs Nikolaos Karalias, Andreas Loukas

AISTATS 2020 Graph Coarsening with Preserved Spectral Properties Yu Jin, Andreas Loukas, Joseph JaJa

NeurIPS 2020 How Hard Is to Distinguish Graphs with Graph Neural Networks? Andreas Loukas

ICLR 2020 On the Relationship Between Self-Attention and Convolutional Layers Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

ICLR 2020 What Graph Neural Networks Cannot Learn: Depth vs Width Andreas Loukas

IJCAI 2019 Extrapolating Paths with Graph Neural Networks Jean-Baptiste Cordonnier, Andreas Loukas

JMLR 2019 Graph Reduction with Spectral and Cut Guarantees Andreas Loukas

ICML 2018 Fast Approximate Spectral Clustering for Dynamic Networks Lionel Martin, Andreas Loukas, Pierre Vandergheynst

ICML 2018 Spectrally Approximating Large Graphs with Smaller Graphs Andreas Loukas, Pierre Vandergheynst

ICML 2017 How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? Andreas Loukas