@preamble{{ \newcommand{\noop}[1]{} }}
@inproceedings{ROSAworkshop,
title = {ROSA: Random Orthogonal Subspace Adaptation},
author = {Gamal, Marawan and Rabusseau, Guillaume},
booktitle = {Workshop on Efficient Systems for Foundation Models@ ICML2023},
year = {2023}
}
@inproceedings{ILSworkshop,
title = {Explaining Graph Neural Networks Using Interpretable Local Surrogates},
author = {Heidari, Farzaneh and Taslakian, Perouz and Rabusseau, Guillaume},
booktitle = {Topological, Algebraic and Geometric Learning Workshops 2023},
pages = {146--155},
year = {2023},
organization = {PMLR}
}
@inproceedings{NeurIPS23,
title = {Temporal graph benchmark for machine learning on temporal graphs},
author = {Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael and Rabusseau, Guillaume and Rabbany, Reihaneh},
booktitle = {NeurIPS},
year = {2023},
arxiv = {https://arxiv.org/abs/2307.01026}
}
@inproceedings{PAKDD23,
title = {Fast and Attributed Change Detection on
Dynamic Graphs with Density of States},
author = {Huang, Shenyang and Danovitch, Jacob and Rabusseau, Guillaume and Rabbany, Reihaneh},
booktitle = {PAKDD},
year = {2023},
arxiv = {https://arxiv.org/abs/2205.11691}
}
@inproceedings{NeurIPS22,
title = {High-Order Pooling for Graph Neural Networks with Tensor Decomposition},
author = {Hua, Chenqing and Rabusseau, Guillaume and Tang, Jian},
booktitle = {NeurIPS},
year = {2022},
arxiv = {https://arxiv.org/abs/2205.11691}
}
@article{learnaut2022clara,
title = {Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case},
author = {Lacroce, Clara and Panangaden, Prakash and Rabusseau, Guillaume},
journal = {LearnAut workshop at ICALP 2022},
year = {2022}
}
@article{learnaut2022tianyu,
title = {Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata},
author = {Li, Tianyu and Mazoure, Bogdan and Rabusseau, Guillaume},
journal = {LearnAut workshop at ICALP 2022},
year = {2022}
}
@article{learnaut2022maude,
title = {Spectral Initialization of Recurrent Neural Networks: Proof of Concept },
author = {Lizaire, Maude and Verret, Simon and Rabusseau, Guillaume},
journal = {LearnAut workshop at ICALP 2022},
year = {2022}
}
@article{learnaut2022kaiwen,
title = {
Spectral Regularization:
an Inductive Bias for Sequence Modeling},
author = {Hou, Kaiwein and Rabusseau, Guillaume},
journal = {LearnAut workshop at ICALP 2022},
year = {2022}
}
@inproceedings{QTNML2021sid,
title = {Towards a Trace-Preserving Tensor Network Representation of Quantum Channels},
author = {Srinivasan, Siddarth and Adhikary, Sandesh and Miller, Jacob and Pokharel, Bibek and Rabusseau, Guillaume and Boots, Byron},
booktitle = {Second Workshop on Quantum Tensor Networks in Machine Learning In conjunction with NeurIPS},
year = {2021}
}
@inproceedings{QTNML2021beheshteh,
title = {Rademacher Random Projections with Tensor Networks},
author = {Rakhshan, Beheshteh T and Rabusseau, Guillaume},
booktitle = {Second Workshop on Quantum Tensor Networks in Machine Learning In conjunction with NeurIPS},
year = {2021}
}
@inproceedings{MetaLearning2021andy,
title = {Few Shot Image Generation via Implicit Autoencoding of Support Sets},
author = {Huang, Andy and Wang, Kuan-Chieh and Rabusseau, Guillaume and Makhzani, Alireza},
booktitle = {Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems},
year = {2021}
}
@article{JAIR2022,
title = {Low-Rank Representation of Reinforcement Learning Policies},
author = {Mazoure, Bogdan and Doan, Thang and Li, Tianyu and Makarenkov, Vladimir and Pineau, Joelle and Precup, Doina and Rabusseau, Guillaume},
year = {2022},
journal = {Journal of Artificial Intelligence Research}
}
@article{ML2022,
title = {Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning},
author = {Li, Tianyu and Precup, Doina and Rabusseau, Guillaume},
journal = {Machine Learning},
arxiv = {https://arxiv.org/abs/2010.10029},
year = 2022
}
@inproceedings{NeurIPS21,
title = {Lower and upper bounds on the pseudo-dimension of tensor network models},
author = {Khavari, Behnoush and Rabusseau, Guillaume},
booktitle = {NeurIPS},
year = {2021},
arxiv = {https://arxiv.org/abs/2106.11827}
}
@inproceedings{ICGI21,
title = {Extracting Weighted Automata for Approximate Minimization in Language Modelling},
author = {Lacroce, Clara and Panangaden, Prakash and Rabusseau, Guillaume},
booktitle = {ICGI},
year = {2021},
arxiv = {https://arxiv.org/abs/2106.02965}
}
@inproceedings{CanAI21,
title = {Understanding Capacity Saturation in Incremental Learning},
author = {Huang, Shenyang and Fran{\c{c}}ois-Lavet, Vincent and Rabusseau, Guillaume},
booktitle = {34th Canadian Conference on Artificial Intelligence},
year = {2021}
}
@inproceedings{ICALP21,
title = {Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata},
author = {Balle, Borja and Lacroce, Clara and Panangaden, Prakash and Precup, Doina and Rabusseau, Guillaume},
booktitle = {ICALP},
year = {2021},
arxiv = {https://arxiv.org/abs/2102.06860}
}
@inproceedings{AISTATS21-a,
title = {Quantum Tensor Networks, Stochastic Processes, and Weighted Automata},
author = {Srinivasan, Siddarth and Adhikary, Sandesh and Miller, Jacob and Rabusseau, Guillaume and Boots, Byron},
booktitle = {AISTATS},
year = {2021},
arxiv = {https://arxiv.org/abs/2010.10653}
}
@inproceedings{AISTATS21-b,
title = {A Theoretical Analysis of Catastrophic Forgetting through the {NTK} Overlap Matrix},
author = {Doan, Thang and Bennani, Mehdi and Mazoure, Bogdan and Rabusseau, Guillaume and Alquier, Pierre},
booktitle = {AISTATS},
year = {2021},
arxiv = {https://arxiv.org/abs/2010.04003}
}
@inproceedings{AISTATS21-c,
title = {Tensor Networks for Probabilistic Sequence Modeling},
author = {Miller, Jacob and Rabusseau, Guillaume and Terilla, John},
journal = {arXiv e-prints},
booktitle = {AISTATS},
year = {2021},
arxiv = {https://arxiv.org/abs/2003.01039}
}
@article{BMC2020,
title = {Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin},
author = {Makarenkov, Vladimir and Mazoure, Bogdan and Rabusseau, Guillaume and Legendre, Pierre},
journal = {BMC Ecology and Evolution},
volume = {21},
number = {1},
pages = {1--18},
year = {2021},
publisher = {BioMed Central}
}
@inproceedings{huang2020laplacian,
title = {Laplacian Change Point Detection for Dynamic Graphs},
author = {Huang, Shenyang and Hitti, Yasmeen and Rabusseau, Guillaume and Rabbany, Reihaneh},
booktitle = {KDD},
year = {2020},
arxiv = {https://arxiv.org/abs/2007.01229}
}
@article{hashemizadeh2020adaptive,
title = {Adaptive Tensor Learning with Tensor Networks},
author = {Hashemizadeh, Meraj and Liu, Michelle and Miller, Jacob and Rabusseau, Guillaume},
journal = {arXiv preprint arXiv:2008.05437},
year = {2020},
arxiv = {https://arxiv.org/abs/2008.05437}
}
@article{miller2020tensor,
title = {Tensor Networks for Language Modeling},
author = {Miller, Jacob and Rabusseau, Guillaume and Terilla, John},
journal = {arXiv preprint arXiv:2003.01039},
year = {2020},
arxiv = {https://arxiv.org/abs/2003.01039}
}
@article{mazoure2020provably,
title = {Provably efficient reconstruction of policy networks},
author = {Mazoure, Bogdan and Doan, Thang and Li, Tianyu and Makarenkov, Vladimir and Pineau, Joelle and Precup, Doina and Rabusseau, Guillaume},
journal = {arXiv preprint arXiv:2002.02863},
year = {2020},
arxiv = {https://arxiv.org/abs/2002.02863}
}
@article{AISTATS2020,
title = {Tensorized Random Projections},
author = {Rakhshan, Beheshteh and Rabusseau, Guillaume},
journal = {AISTATS},
year = {2020},
arxiv = {https://arxiv.org/abs/2003.05101},
code = {https://github.com/brakhshan/Tensorized_Random_Projection}
}
@article{li2019efficient,
title = {Efficient Planning under Partial Observability with Unnormalized {Q} Functions and Spectral Learning},
author = {Li, Tianyu and Mazoure, Bogdan and Precup, Doina and Rabusseau, Guillaume},
journal = {AISTATS},
year = {2020},
arxiv = {https://arxiv.org/abs/1911.05010}
}
@article{huang2019neural,
title = {Neural Architecture Search for Class-incremental Learning},
author = {Huang, Shenyang and Fran{\c{c}}ois-Lavet, Vincent and Rabusseau, Guillaume},
journal = {arXiv preprint arXiv:1909.06686},
year = {2019}
}
@article{IandC2019,
title = {Approximate minimization of weighted tree automata},
author = {Balle, Borja and Rabusseau, Guillaume},
journal = {Information and Computation},
volume = {282},
pages = {104654},
year = {2022},
publisher = {Elsevier},
pdf = {./files/IandC2019.pdf}
}
@article{JAIR2019,
title = {On overfitting and asymptotic bias in batch reinforcement learning with partial observability},
author = {Fran{\c{c}}ois-Lavet, Vincent and Rabusseau, Guillaume and Pineau, Joelle and Ernst, Damien and Fonteneau, Raphael},
journal = {Journal of Artificial Intelligence Research},
volume = {65},
pages = {1--30},
year = {2019},
arxiv = {https://arxiv.org/abs/1709.07796}
}
@article{wuoptimizing,
title = {Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning},
author = {Wu, Di and Rabusseau, Guillaume and Fran{\c{c}}ois-lavet, Vincent and Precup, Doina and Boulet, Benoit},
year = {2018},
pdf = {http://ala2018.it.nuigalway.ie/papers/ALA_2018_paper_37.pdf},
journal = {Adaptive Learning Agents (ALA) workshop at the Federated AI Meeting}
}
@article{huang2018exploring,
title = {Exploring Continual Learning Using Incremental Architecture Search},
author = {Shenyang Huang and Vincent François-Lavet and Guillaume Rabusseau and Joelle Pineau},
journal = {NeurIPS 2018 Workshop on Continual Learning},
year = 2018,
pdf = {https://marcpickett.com/cl2018/CL-2018_paper_34.pdf}
}
@article{kenyon2019clustering,
title = {Clustering-Oriented Representation Learning with Attractive-Repulsive Loss},
author = {Kenyon-Dean, Kian and Cianflone, Andre and Page-Caccia, Lucas and Rabusseau, Guillaume and Cheung, Jackie Chi Kit and Precup, Doina},
arxiv = {https://arxiv.org/abs/1812.07627},
year = {2019},
journal = {AAAI 2019 Workshop on Network Interpretability for Deep Learning}
}
@article{cao2017,
title = {Tensor Regression Networks with various Low-Rank Tensor Approximations},
author = {Cao, Xingwei and Rabusseau, Guillaume},
journal = {arXiv preprint arXiv:1712.09520},
year = {2017},
arxiv = {https://arxiv.org/abs/1712.09520}
}
@inproceedings{AISTATS2019,
title = {Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning},
author = {Rabusseau, Guillaume and Li, Tianyu and Precup, Doina},
booktitle = {AISTATS},
year = {2019},
arxiv = {https://arxiv.org/abs/1807.01406},
pdf = {http://proceedings.mlr.press/v89/rabusseau19a.html},
slides = {./files/2019.stonybrook.pdf},
poster = {./files/poster_aistats_2019.pdf}
}
@article{crawford2018,
title = {Sequential Coordination of Deep Models for Learning Visual Arithmetic},
author = {Crawford, Eric and Rabusseau, Guillaume and Pineau, Joelle},
year = {2018},
pdf = {https://openreview.net/pdf?id=H1kMMmb0-}
}
@inproceedings{ICGI2018,
title = {Learning Graph Weighted Models on Pictures},
author = {Amortila, Philip and Rabusseau, Guillaume},
booktitle = {ICGI},
year = {2018},
arxiv = {https://arxiv.org/abs/1806.08297},
pdf = {http://proceedings.mlr.press/v93/amortila19a.html}
}
@article{amortila2018,
title = {Learning Graph Weighted Models on Pictures},
author = {Amortila, Philip and Rabusseau, Guillaume},
journal = {2nd workshop on Learning and Automata (LearnAut at FLoC 2018)},
year = {2018}
}
@incollection{AISTATS2018,
title = {Nonlinear Weighted Finite Automata},
author = {Li, Tianyu and Rabusseau, Guillaume and Precup, Doina},
booktitle = {AISTATS},
year = {2018},
pdf = {http://proceedings.mlr.press/v84/li18a}
}
@incollection{FOSSACS2018,
title = {Minimization of Graph Weighted Models over Circular Strings},
author = {Rabusseau, Guillaume},
booktitle = {FoSSaCS},
year = {2018},
pdf = {./all_papers/FoSSaCS18.pdf},
slides = {./files/slides_fossacs_2018.pdf}
}
@article{JCSS2018,
title = {Recognizable series on graphs and hypergraphs},
journal = {Journal of Computer and System Sciences},
volume = {104},
pages = {58 - 81},
year = {2019},
issn = {0022-0000},
author = {Raphaël Bailly and Guillaume Rabusseau and François Denis},
preprint = {./files/JCSS2017.pdf},
pdf = {http://www.sciencedirect.com/science/article/pii/S0022000017301563}
}
@incollection{NIPS17-a,
title = {Multitask Spectral Learning of Weighted Automata},
author = {Rabusseau, Guillaume and Balle, Borja and Pineau, Joelle},
booktitle = {NeurIPS},
year = {2017},
pdf = {http://papers.nips.cc/paper/6852-multitask-spectral-learning-of-weighted-automata},
slides = {./files/2018.magnet.pdf},
poster = {./files/poster_nips_2017.pdf}
}
@incollection{NIPS17-b,
title = {Hierarchical Methods of Moments},
author = {Ruffini, Matteo and Rabusseau, Guillaume and Balle, Borja},
booktitle = {NeurIPS},
year = {2017},
pdf = {http://papers.nips.cc/paper/6786-hierarchical-methods-of-moments}
}
@article{NIPS16-ws,
title = {Graph Learning as a Tensor Factorization Problem},
author = {Bailly, Rapha{\"e}l and Rabusseau, Guillaume},
journal = {NIPS workshop on Learning with Tensors},
year = {2016},
pdf = {https://hal.archives-ouvertes.fr/hal-01519851/}
}
@article{LICS17-ws1,
title = {Multitask Spectral Learning of Weighted Automata},
author = {Rabusseau, Guillaume and Pineau, Joelle},
journal = {LICS workshop on Learning and Automata},
year = {2017}
}
@article{LICS17-ws2,
title = {Neural Network Based Nonlinear Weighted Finite Automata},
author = {Li, Tianyu and Rabusseau, Guillaume and Precup, Doina},
journal = {LICS workshop on Learning and Automata},
year = {2017}
}
@phdthesis{rabusseau2016thesis,
title = {A Tensor Perspective on Weighted Automata, Low-Rank Regression
and Algebraic Mixtures},
author = {Guillaume Rabusseau},
year = {2016},
school = {Aix-Marseille Université},
pdf = {./files/phd_rabusseau_final.pdf}
}
@incollection{NIPS16,
title = {Low-Rank Regression with Tensor Responses},
author = {Rabusseau, Guillaume and Kadri, Hachem},
booktitle = {NeurIPS},
year = {2016},
pdf = {./files/nips2016.pdf},
poster = {./files/nips2016-poster.pdf},
code = {https://github.com/grwip/HOLRR}
}
@inproceedings{AISTATS16,
title = {{Low-Rank Approximation of Weighted Tree Automata}},
author = {Rabusseau, Guillaume and Balle, Borja and Cohen, Shay B.},
booktitle = {AISTATS},
year = {2016},
arxiv = {http://arxiv.org/abs/1511.01442},
pdf = {http://proceedings.mlr.press/v51/rabusseau16.html},
poster = {./files/AISTATS16-poster.pdf}
}
@article{CAP16-a,
author = {Guillaume Rabusseau},
title = {{Régression de faible rang pour réponses tensorielles}},
journal = {Conférence sur l'Apprentissage Automatique},
locdate = {Marseille, France, July 5-7},
year = {2016}
}
@article{CAP16-b,
author = {Guillaume Rabusseau and Borja Balle and Shay B. Cohen},
title = {{Minimisation approximée d'automates pondérés d'arbres}},
journal = {Conférence sur l'Apprentissage Automatique},
locdate = {Marseille, France, July 5-7},
year = {2016}
}
@inproceedings{LATA15,
author = {Rapha{\"{e}}l Bailly and
Fran{\c{c}}ois Denis and
Guillaume Rabusseau},
title = {{Recognizable Series on Hypergraphs}},
booktitle = {LATA},
year = {2015},
slides = {./files/LATA2015-slides.pdf},
arxiv = {http://arxiv.org/abs/1404.7533}
}
@article{GRETSI15,
author = {Guillaume Rabusseau and Hachem Kadri and
Fran{\c{c}}ois Denis},
title = {{Régression de faible rang non-paramétrique pour réponses tensorielles}},
journal = {GRETSI},
year = {2015},
no_poster = {./files/GRETSI15-poster.pdf}
}
@inproceedings{ICGI14,
author = {Guillaume Rabusseau and
Fran{\c{c}}ois Denis},
title = {{Maximizing a Tree Series in the Representation Space}},
booktitle = {ICGI},
year = {2014},
pdf = {http://jmlr.csail.mit.edu/proceedings/papers/v34/rabusseau14a.pdf},
slides = {./files/ICGI2014-slides.pdf}
}
@article{rabusseau2014learning,
author = {Guillaume Rabusseau and
Fran{\c{c}}ois Denis},
title = {{Learning Negative Mixture Models by Tensor Decompositions}},
journal = {CoRR},
volume = {abs/1403.4224},
year = {2014},
arxiv = {http://arxiv.org/abs/1403.4224}
}
@article{CAP14,
author = {Guillaume Rabusseau and
Fran{\c{c}}ois Denis},
title = {{Décompositions Tensorielles pour l'Apprentissage de Modèles de Mélanges Négatifs}},
journal = {Conférence sur l'Apprentissage Automatique},
locdate = {Saint-Etienne, France, July 4-7},
year = {2014},
note = {\textbf{Best paper award}},
slides = {./files/CAP2014-slides.pdf}
}
@article{ICMLworkshop14,
author = {Guillaume Rabusseau and
Fran{\c{c}}ois Denis},
title = {{Learning Negative Mixture Models by Tensor Decompositions}},
journal = {Workshop on Method of Moments and Spectral Learning (ICML 2014)},
locdate = {Beijing, China, June 25},
year = {2014},
poster = {./files/ICMLworkshop14-poster.pdf}
}
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