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@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 = {https://link.springer.com/chapter/10.1007/978-3-319-89366-2_28}, 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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