A software platform for developing multi-player pervasive games using small programmable object technologies. - Orestis Akribopoulos; Dimitrios Bousis; Dionysios Efstathiou; Haris Koutsouridis; Marios Logaras; Andreas Loukas; Alexandros Nafas; Georgios Oikonomou; Irini Thireou; Nikos Vasilakis; Panagiotis C. Kokkinos; Georgios Mylonas; et al. (MASS, 2008)

On mining sensor network software repositories. - Andreas Loukas; Matthias Woehrle; Koen Langendoen (SESENA@ICSE, 2011)

On distributed computation of information potentials. - Andreas Loukas; Matthias Woehrle; Philipp M. Glatz; Koen Langendoen (FOMC, 2012)

Think globally, act locally: on the reshaping of information landscapes. - Andreas Loukas; Marco Zuniga; Matthias Woehrle; Marco Cattani; Koen Langendoen (IPSN, 2013)

Fairness for All, Rate Allocation for Mobile Wireless Networks. - Andreas Loukas; Matthias Woehrle; Marco Zuniga; Koen Langendoen (MASS, 2013)

How to identify global trends from local decisions? Event region detection on mobile networks. - Andreas Loukas; Marco Zuniga; Ioannis Protonotarios; Jie Gao 001 (INFOCOM, 2014)

Lightweight neighborhood cardinality estimation in dynamic wireless networks. - Marco Cattani; Marco Zuniga; Andreas Loukas; Koen Langendoen (IPSN, 2014)

Sybil-Resistant Meta Strategies for the Forwarder's Dilemma. - Yunus Durmus; Andreas Loukas; Ertan Onur; Koen Langendoen (SASO, 2014)

Stochastic graph filtering on time-varying graphs. - Elvin Isufi; Andrea Simonetto; Andreas Loukas; Geert Leus (CAMSAP, 2015)

Graph scale-space theory for distributed peak and pit identification. - Andreas Loukas; Marco Cattani; Marco Zuniga; Jie Gao 001 (IPSN, 2015)

Distributed Autoregressive Moving Average Graph Filters. - Andreas Loukas; Andrea Simonetto; Geert Leus (IEEE Signal Process. Lett., 2015)

Distributed Graph Filters. - Andreas Loukas (2015)

Separable autoregressive moving average graph-temporal filters. - Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus (EUSIPCO, 2016)

Frequency analysis of time-varying graph signals. - Andreas Loukas; Damien Foucard (GlobalSIP, 2016)

Staffetta: Smart Duty-Cycling for Opportunistic Data Collection. - Marco Cattani; Andreas Loukas; Marco Zimmerling; Marco Zuniga; Koen Langendoen (SenSys, 2016)

Online Balanced Repartitioning. - Chen Avin; Andreas Loukas; Maciej Pacut; Stefan Schmid 001 (DISC, 2016)

Distributed Time-Varying Graph Filtering. - Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus (CoRR, 2016)

Frequency Analysis of Temporal Graph Signals. - Andreas Loukas; Damien Foucard (CoRR, 2016)

Predicting the evolution of stationary graph signals. - Andreas Loukas; Nathanaël Perraudin (CoRR, 2016)

Predicting the evolution of stationary graph signals. - Andreas Loukas; Elvin Isufi; Nathanaël Perraudin (ACSSC, 2017)

Autoregressive moving average graph filters a stable distributed implementation. - Elvin Isufi; Andreas Loukas; Geert Leus (ICASSP, 2017)

Learning time varying graphs. - Vassilis Kalofolias; Andreas Loukas; Dorina Thanou; Pascal Frossard (ICASSP, 2017)

Towards stationary time-vertex signal processing. - Nathanaël Perraudin; Andreas Loukas; Francesco Grassi; Pierre Vandergheynst (ICASSP, 2017)

Spinner: Scalable Graph Partitioning in the Cloud. - Claudio Martella; Dionysios Logothetis; Andreas Loukas; Georgos Siganos (ICDE, 2017)

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

Towards Communication-Aware Robust Topologies. - Chen Avin; Alexandr Hercules; Andreas Loukas; Stefan Schmid 001 (CoRR, 2017)

A Time-Vertex Signal Processing Framework. - Francesco Grassi; Andreas Loukas; Nathanaël Perraudin; Benjamin Ricaud (CoRR, 2017)

Autoregressive Moving Average Graph Filtering. - Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus (IEEE Trans. Signal Process., 2017)

Filtering Random Graph Processes Over Random Time-Varying Graphs. - Elvin Isufi; Andreas Loukas; Andrea Simonetto; Geert Leus (IEEE Trans. Signal Process., 2017)

Spectrally Approximating Large Graphs with Smaller Graphs. - Andreas Loukas; Pierre Vandergheynst (ICML, 2018)

Fast Approximate Spectral Clustering for Dynamic Networks. - Lionel Martin; Andreas Loukas; Pierre Vandergheynst (ICML, 2018)

Graph reduction by local variation. - Andreas Loukas (CoRR, 2018)

rDAN: Toward robust demand-aware network designs. - Chen Avin; Alexandr Hercules; Andreas Loukas; Stefan Schmid 001 (Inf. Process. Lett., 2018)

A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs. - Francesco Grassi; Andreas Loukas; Nathanaël Perraudin; Benjamin Ricaud (IEEE Trans. Signal Process., 2018)

Extrapolating Paths with Graph Neural Networks. - Jean-Baptiste Cordonnier; Andreas Loukas (IJCAI, 2019)

Approximating Spectral Clustering via Sampling: a Review. - Nicolas Tremblay; Andreas Loukas (CoRR, 2019)

Some limitations of norm based generalization bounds in deep neural networks. - Konstantinos Pitas; Andreas Loukas; Mike Davies 001; Pierre Vandergheynst (CoRR, 2019)

Discriminative structural graph classification. - Younjoo Seo; Andreas Loukas; Nathanaël Perraudin (CoRR, 2019)

Stationary time-vertex signal processing. - Andreas Loukas; Nathanaël Perraudin (EURASIP J. Adv. Signal Process., 2019)

Graph Reduction with Spectral and Cut Guarantees. - Andreas Loukas (J. Mach. Learn. Res., 2019)

Forecasting Time Series With VARMA Recursions on Graphs. - Elvin Isufi; Andreas Loukas; Nathanaël Perraudin; Geert Leus (IEEE Trans. Signal Process., 2019)

Graph Coarsening with Preserved Spectral Properties. - Yu Jin 008; Andreas Loukas; Joseph F. JáJá (AISTATS, 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 (ICLR, 2020)

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. - Nikolaos Karalias; Andreas Loukas (NeurIPS, 2020)

How hard is to distinguish graphs with graph neural networks? - Andreas Loukas (NeurIPS, 2020)

Building powerful and equivariant graph neural networks with structural message-passing. - Clément Vignac; Andreas Loukas; Pascal Frossard (NeurIPS, 2020)

How hard is graph isomorphism for graph neural networks? - Andreas Loukas (CoRR, 2020)

Building powerful and equivariant graph neural networks with message-passing. - Clément Vignac; Andreas Loukas; Pascal Frossard (CoRR, 2020)

Multi-Head Attention: Collaborate Instead of Concatenate. - Jean-Baptiste Cordonnier; Andreas Loukas; Martin Jaggi (CoRR, 2020)

Dynamic Balanced Graph Partitioning. - Chen Avin; Marcin Bienkowski; Andreas Loukas; Maciej Pacut; Stefan Schmid 001 (SIAM J. Discret. Math., 2020)

Attention is not all you need: pure attention loses rank doubly exponentially with depth. - Yihe Dong; Jean-Baptiste Cordonnier; Andreas Loukas (ICML, 2021)

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

Partition and Code: learning how to compress graphs. - Giorgos Bouritsas; Andreas Loukas; Nikolaos Karalias; Michael M. Bronstein (NeurIPS, 2021)

What training reveals about neural network complexity. - Andreas Loukas; Marinos Poiitis; Stefanie Jegelka (NeurIPS, 2021)

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

On the generalization of learning algorithms that do not converge. - Nisha Chandramoorthy; Andreas Loukas; Khashayar Gatmiry; Stefanie Jegelka (NeurIPS, 2022)

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. - Nikolaos Karalias; Joshua Robinson 001; Andreas Loukas; Stefanie Jegelka (NeurIPS, 2022)

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. - Natasa Tagasovska; Nathan C. Frey; Andreas Loukas; Isidro Hötzel; Julien Lafrance-Vanasse; Ryan Lewis Kelly; Yan Wu 027; Arvind Rajpal; Richard Bonneau; Kyunghyun Cho; Stephen Ra; Vladimir Gligorijevic (CoRR, 2022)

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. - Max W. Shen; Ehsan Hajiramezanali; Gabriele Scalia; Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Andreas Loukas (CoRR, 2022)

RosettaSurf - A surface-centric computational design approach. - Andreas Scheck; Stéphane Rosset; Michaël Defferrard; Andreas Loukas; Jaume Bonet; Pierre Vandergheynst; Bruno E. Correia (PLoS Comput. Biol., 2022)

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

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

AbDiffuser: full-atom generation of in-vitro functioning antibodies. - Karolis Martinkus; Jan Ludwiczak; Wei-Ching Liang; Julien Lafrance-Vanasse; Isidro Hötzel; Arvind Rajpal; Yan Wu 027; Kyunghyun Cho; Richard Bonneau; Vladimir Gligorijevic; Andreas Loukas (NeurIPS, 2023)

Batched Predictors Generalize within Distribution. - Andreas Loukas; Pan Kessel (CoRR, 2023)

Protein Discovery with Discrete Walk-Jump Sampling. - Nathan C. Frey; Daniel Berenberg; Karina Zadorozhny; Joseph Kleinhenz; Julien Lafrance-Vanasse; Isidro Hötzel; Yan Wu 027; Stephen Ra; Richard Bonneau; Kyunghyun Cho; Andreas Loukas; Vladimir Gligorijevic; et al. (ICLR, 2024)

Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. - Natasa Tagasovska; Vladimir Gligorijevic; Kyunghyun Cho; Andreas Loukas (NeurIPS, 2024)

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing. - Andreas Loukas; Karolis Martinkus; Ed Wagstaff; Kyunghyun Cho (CoRR, 2024)