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ORCIDAndreas Loukas
Person information
- affiliation: Swiss Federal Institute of Technology in Lausanne, Switzerland
- affiliation (former): Technische Universität Berlin
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2020 – today
- 2023
[c39]Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas:
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. ICML 2023: 1200-1217- [c38]Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani:
Towards Understanding and Improving GFlowNet Training. ICML 2023: 30956-30975 - [c37]Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas:
AbDiffuser: full-atom generation of in-vitro functioning antibodies. NeurIPS 2023 - [i37]Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani:
Towards Understanding and Improving GFlowNet Training. CoRR abs/2305.07170 (2023) - [i36]Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas:
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. CoRR abs/2306.03175 (2023) - [i35]Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hötzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi:
Protein Discovery with Discrete Walk-Jump Sampling. CoRR abs/2306.12360 (2023) - [i34]Andreas Loukas, Pan Kessel:
Batched Predictors Generalize within Distribution. CoRR abs/2307.09379 (2023) - [i33]Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas:
AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies. CoRR abs/2308.05027 (2023) - 2022
- [j10]Andreas Scheck
, Stéphane Rosset, Michaël Defferrard, Andreas Loukas, Jaume Bonet, Pierre Vandergheynst, Bruno E. Correia:
RosettaSurf - A surface-centric computational design approach. PLoS Comput. Biol. 18(3) (2022) - [c36]Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer:
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. ICML 2022: 15159-15179 - [c35]Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka:
On the generalization of learning algorithms that do not converge. NeurIPS 2022 - [c34]Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka:
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. NeurIPS 2022 - [i32]Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer:
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. CoRR abs/2204.01613 (2022) - [i31]Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka:
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. CoRR abs/2208.04055 (2022) - [i30]Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka:
On the generalization of learning algorithms that do not converge. CoRR abs/2208.07951 (2022) - [i29]Natasa Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijevic:
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. CoRR abs/2210.10838 (2022) - [i28]Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia, Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Andreas Loukas:
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. CoRR abs/2210.12192 (2022) - 2021
- [c33]Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas:
Attention is not all you need: pure attention loses rank doubly exponentially with depth. ICML 2021: 2793-2803 - [c32]Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. NeurIPS 2021: 494-508 - [c31]Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas:
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning. NeurIPS 2021: 12587-12599 - [c30]Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein:
Partition and Code: learning how to compress graphs. NeurIPS 2021: 18603-18619 - [i27]Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas:
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth. CoRR abs/2103.03404 (2021) - [i26]Andreas Loukas, Marinos Poiitis, Stefanie Jegelka:
What training reveals about neural network complexity. CoRR abs/2106.04186 (2021) - [i25]Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein:
Partition and Code: learning how to compress graphs. CoRR abs/2107.01952 (2021) - [i24]Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas:
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning. CoRR abs/2110.14266 (2021) - 2020
- [j9]Chen Avin, Marcin Bienkowski, Andreas Loukas, Maciej Pacut, Stefan Schmid:
Dynamic Balanced Graph Partitioning. SIAM J. Discret. Math. 34(3): 1791-1812 (2020) - [c29]Yu Jin, Andreas Loukas, Joseph F. JáJá:
Graph Coarsening with Preserved Spectral Properties. AISTATS 2020: 4452-4462 - [c28]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
On the Relationship between Self-Attention and Convolutional Layers. ICLR 2020 - [c27]Andreas Loukas:
What graph neural networks cannot learn: depth vs width. ICLR 2020 - [c26]Nikolaos Karalias, Andreas Loukas:
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS 2020 - [c25]Andreas Loukas:
How hard is to distinguish graphs with graph neural networks? NeurIPS 2020 - [c24]Clément Vignac, Andreas Loukas, Pascal Frossard:
Building powerful and equivariant graph neural networks with structural message-passing. NeurIPS 2020 - [i23]Andreas Loukas:
How hard is graph isomorphism for graph neural networks? CoRR abs/2005.06649 (2020) - [i22]Nikolaos Karalias, Andreas Loukas:
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. CoRR abs/2006.10643 (2020) - [i21]Clément Vignac, Andreas Loukas, Pascal Frossard:
Building powerful and equivariant graph neural networks with message-passing. CoRR abs/2006.15107 (2020) - [i20]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
Multi-Head Attention: Collaborate Instead of Concatenate. CoRR abs/2006.16362 (2020)
2010 – 2019
- 2019
- [j8]Andreas Loukas, Nathanaël Perraudin:
Stationary time-vertex signal processing. EURASIP J. Adv. Signal Process. 2019: 36 (2019) - [j7]Andreas Loukas:
Graph Reduction with Spectral and Cut Guarantees. J. Mach. Learn. Res. 20: 116:1-116:42 (2019) - [j6]Elvin Isufi, Andreas Loukas, Nathanaël Perraudin, Geert Leus:
Forecasting Time Series With VARMA Recursions on Graphs. IEEE Trans. Signal Process. 67(18): 4870-4885 (2019) - [c23]Jean-Baptiste Cordonnier, Andreas Loukas:
Extrapolating Paths with Graph Neural Networks. IJCAI 2019: 2187-2194 - [i19]Nicolas Tremblay, Andreas Loukas:
Approximating Spectral Clustering via Sampling: a Review. CoRR abs/1901.10204 (2019) - [i18]Jean-Baptiste Cordonnier, Andreas Loukas:
Extrapolating paths with graph neural networks. CoRR abs/1903.07518 (2019) - [i17]Konstantinos Pitas, Andreas Loukas, Mike Davies, Pierre Vandergheynst:
Some limitations of norm based generalization bounds in deep neural networks. CoRR abs/1905.09677 (2019) - [i16]Younjoo Seo, Andreas Loukas, Nathanaël Perraudin:
Discriminative structural graph classification. CoRR abs/1905.13422 (2019) - [i15]Andreas Loukas:
What graph neural networks cannot learn: depth vs width. CoRR abs/1907.03199 (2019) - [i14]Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi:
On the Relationship between Self-Attention and Convolutional Layers. CoRR abs/1911.03584 (2019) - 2018
- [j5]Chen Avin, Alexandr Hercules, Andreas Loukas, Stefan Schmid:
rDAN: Toward robust demand-aware network designs. Inf. Process. Lett. 133: 5-9 (2018) - [j4]Francesco Grassi, Andreas Loukas, Nathanaël Perraudin, Benjamin Ricaud:
A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs. IEEE Trans. Signal Process. 66(3): 817-829 (2018) - [c22]Andreas Loukas, Pierre Vandergheynst:
Spectrally Approximating Large Graphs with Smaller Graphs. ICML 2018: 3243-3252 - [c21]Lionel Martin, Andreas Loukas, Pierre Vandergheynst:
Fast Approximate Spectral Clustering for Dynamic Networks. ICML 2018: 3420-3429 - [i13]Andreas Loukas, Pierre Vandergheynst:
Spectrally approximating large graphs with smaller graphs. CoRR abs/1802.07510 (2018) - [i12]Andreas Loukas:
Graph reduction by local variation. CoRR abs/1808.10650 (2018) - [i11]Elvin Isufi, Andreas Loukas, Nathanaël Perraudin, Geert Leus:
Forecasting Time Series with VARMA Recursions on Graphs. CoRR abs/1810.08581 (2018) - 2017
- [j3]Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus:
Autoregressive Moving Average Graph Filtering. IEEE Trans. Signal Process. 65(2): 274-288 (2017) - [j2]Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus:
Filtering Random Graph Processes Over Random Time-Varying Graphs. IEEE Trans. Signal Process. 65(16): 4406-4421 (2017) - [c20]Andreas Loukas, Elvin Isufi, Nathanaël Perraudin:
Predicting the evolution of stationary graph signals. ACSSC 2017: 60-64 - [c19]Vassilis Kalofolias, Andreas Loukas, Dorina Thanou, Pascal Frossard:
Learning time varying graphs. ICASSP 2017: 2826-2830 - [c18]Nathanaël Perraudin, Andreas Loukas, Francesco Grassi, Pierre Vandergheynst:
Towards stationary time-vertex signal processing. ICASSP 2017: 3914-3918 - [c17]Elvin Isufi, Andreas Loukas, Geert Leus:
Autoregressive moving average graph filters a stable distributed implementation. ICASSP 2017: 4119-4123 - [c16]Claudio Martella, Dionysios Logothetis, Andreas Loukas, Georgos Siganos:
Spinner: Scalable Graph Partitioning in the Cloud. ICDE 2017: 1083-1094 - [c15]Andreas Loukas:
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? ICML 2017: 2228-2237 - [i10]Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus:
Filtering Random Graph Processes Over Random Time-Varying Graphs. CoRR abs/1705.00442 (2017) - [i9]Francesco Grassi, Andreas Loukas, Nathanaël Perraudin, Benjamin Ricaud:
A Time-Vertex Signal Processing Framework. CoRR abs/1705.02307 (2017) - [i8]Chen Avin, Alexandr Hercules, Andreas Loukas, Stefan Schmid:
Towards Communication-Aware Robust Topologies. CoRR abs/1705.07163 (2017) - 2016
- [c14]Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus:
Separable autoregressive moving average graph-temporal filters. EUSIPCO 2016: 200-204 - [c13]Andreas Loukas, Damien Foucard:
Frequency analysis of time-varying graph signals. GlobalSIP 2016: 346-350 - [c12]Marco Cattani, Andreas Loukas, Marco Zimmerling, Marco Zuniga, Koen Langendoen:
Staffetta: Smart Duty-Cycling for Opportunistic Data Collection. SenSys 2016: 56-69 - [c11]Chen Avin, Andreas Loukas, Maciej Pacut, Stefan Schmid:
Online Balanced Repartitioning. DISC 2016: 243-256 - [i7]Andreas Loukas, Damien Foucard:
Frequency Analysis of Temporal Graph Signals. CoRR abs/1602.04434 (2016) - [i6]Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus:
Distributed Time-Varying Graph Filtering. CoRR abs/1602.04436 (2016) - [i5]Nathanaël Perraudin, Andreas Loukas, Francesco Grassi, Pierre Vandergheynst:
Towards stationary time-vertex signal processing. CoRR abs/1606.06962 (2016) - [i4]Andreas Loukas, Nathanaël Perraudin:
Predicting the evolution of stationary graph signals. CoRR abs/1607.03313 (2016) - [i3]Andreas Loukas, Nathanaël Perraudin:
Stationary time-vertex signal processing. CoRR abs/1611.00255 (2016) - 2015
- [b1]Andreas Loukas:
Distributed Graph Filters. Delft University of Technology, Netherlands, 2015 - [j1]Andreas Loukas, Andrea Simonetto, Geert Leus:
Distributed Autoregressive Moving Average Graph Filters. IEEE Signal Process. Lett. 22(11): 1931-1935 (2015) - [c10]Elvin Isufi, Andrea Simonetto, Andreas Loukas, Geert Leus:
Stochastic graph filtering on time-varying graphs. CAMSAP 2015: 89-92 - [c9]Andreas Loukas, Marco Cattani, Marco Zuniga, Jie Gao:
Graph scale-space theory for distributed peak and pit identification. IPSN 2015: 118-129 - [i2]Andreas Loukas, Andrea Simonetto, Geert Leus:
Distributed Autoregressive Moving Average Graph Filters. CoRR abs/1508.05808 (2015) - [i1]Chen Avin, Andreas Loukas, Maciej Pacut, Stefan Schmid:
Online Balanced Repartitioning. CoRR abs/1511.02074 (2015) - 2014
- [c8]Andreas Loukas, Marco Zuniga, Ioannis Protonotarios, Jie Gao:
How to identify global trends from local decisions? Event region detection on mobile networks. INFOCOM 2014: 1177-1185 - [c7]Marco Cattani, Marco Zuniga, Andreas Loukas, Koen Langendoen:
Lightweight neighborhood cardinality estimation in dynamic wireless networks. IPSN 2014: 179-189 - [c6]Yunus Durmus, Andreas Loukas, Ertan Onur, Koen Langendoen:
Sybil-Resistant Meta Strategies for the Forwarder's Dilemma. SASO 2014: 90-99 - 2013
- [c5]Andreas Loukas, Marco Zuniga, Matthias Woehrle, Marco Cattani, Koen Langendoen:
Think globally, act locally: on the reshaping of information landscapes. IPSN 2013: 265-276 - [c4]Andreas Loukas, Matthias Woehrle, Marco Zuniga, Koen Langendoen:
Fairness for All, Rate Allocation for Mobile Wireless Networks. MASS 2013: 154-162 - 2012
- [c3]Andreas Loukas, Matthias Woehrle, Philipp M. Glatz, Koen Langendoen:
On distributed computation of information potentials. FOMC 2012: 5 - 2011
- [c2]Andreas Loukas, Matthias Woehrle, Koen Langendoen:
On mining sensor network software repositories. SESENA@ICSE 2011: 25-30
2000 – 2009
- 2008
- [c1]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, Ioannis Chatzigiannakis:
A software platform for developing multi-player pervasive games using small programmable object technologies. MASS 2008: 544-546
Coauthor Index
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