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Guillaume Rabusseau
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2020 – today
- 2024
- [j6]Tianyu Li, Doina Precup, Guillaume Rabusseau:
Connecting weighted automata, tensor networks and recurrent neural networks through spectral learning. Mach. Learn. 113(5): 2619-2653 (2024) - [j5]Shenyang Huang, Samy Coulombe, Yasmeen Hitti, Reihaneh Rabbany, Guillaume Rabusseau:
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs. ACM Trans. Knowl. Discov. Data 18(3): 63:1-63:32 (2024) - [c29]Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau:
Simulating weighted automata over sequences and trees with transformers. AISTATS 2024: 2368-2376 - [c28]Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau:
Length independent PAC-Bayes bounds for Simple RNNs. AISTATS 2024: 3547-3555 - [c27]Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters:
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. ICLR 2024 - [c26]Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal Abdel Hameed, Guillaume Rabusseau:
A Tensor Decomposition Perspective on Second-order RNNs. ICML 2024 - [i44]Michael Rizvi, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau:
Simulating Weighted Automata over Sequences and Trees with Transformers. CoRR abs/2403.09728 (2024) - [i43]Vivek Bharadwaj, Beheshteh T. Rakhshan, Osman Asif Malik, Guillaume Rabusseau:
Efficient Leverage Score Sampling for Tensor Train Decomposition. CoRR abs/2406.02749 (2024) - [i42]Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal Abdel Hameed, Guillaume Rabusseau:
A Tensor Decomposition Perspective on Second-order RNNs. CoRR abs/2406.05045 (2024) - [i41]Julia Gastinger, Shenyang Huang, Mikhail Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau:
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. CoRR abs/2406.09639 (2024) - [i40]Razieh Shirzadkhani, Tran Gia Bao Ngo, Kiarash Shamsi, Shenyang Huang, Farimah Poursafaei, Poupak Azad, Reihaneh Rabbany, Baris Coskunuzer, Guillaume Rabusseau, Cuneyt Gurcan Akcora:
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs. CoRR abs/2406.10426 (2024) - [i39]Marawan Gamal Abdel Hameed, Aristides Milios, Siva Reddy, Guillaume Rabusseau:
ROSA: Random Subspace Adaptation for Efficient Fine-Tuning. CoRR abs/2407.07802 (2024) - [i38]Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, Guillaume Rabusseau, Emanuele Rossi:
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs. CoRR abs/2407.12269 (2024) - 2023
- [c25]Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany:
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. NeurIPS 2023 - [c24]Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau, Reihaneh Rabbany:
Fast and Attributed Change Detection on Dynamic Graphs with Density of States. PAKDD (1) 2023: 15-26 - [c23]Farzaneh Heidari, Perouz Taslakian, Guillaume Rabusseau:
Explaining Graph Neural Networks Using Interpretable Local Surrogates. TAG-ML 2023: 146-155 - [e1]François Coste, Faissal Ouardi, Guillaume Rabusseau:
International Conference on Grammatical Inference, ICGI 2023, 10-13 July 2023, Rabat, Morocco. Proceedings of Machine Learning Research 217, PMLR 2023 [contents] - [i37]Shenyang Huang, Samy Coulombe, Yasmeen Hitti, Reihaneh Rabbany, Guillaume Rabusseau:
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs. CoRR abs/2302.01204 (2023) - [i36]Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau, Reihaneh Rabbany:
Fast and Attributed Change Detection on Dynamic Graphs with Density of States. CoRR abs/2305.08750 (2023) - [i35]Clara Lacroce, Borja Balle, Prakash Panangaden, Guillaume Rabusseau:
Optimal Approximate Minimization of One-Letter Weighted Finite Automata. CoRR abs/2306.00135 (2023) - [i34]Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh Rabbany:
Temporal Graph Benchmark for Machine Learning on Temporal Graphs. CoRR abs/2307.01026 (2023) - [i33]Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters:
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. CoRR abs/2310.04292 (2023) - [i32]Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz:
Generative Learning of Continuous Data by Tensor Networks. CoRR abs/2310.20498 (2023) - 2022
- [j4]Borja Balle, Guillaume Rabusseau:
Approximate minimization of weighted tree automata. Inf. Comput. 282: 104654 (2022) - [j3]Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau:
Low-Rank Representation of Reinforcement Learning Policies. J. Artif. Intell. Res. 75: 597-636 (2022) - [c22]Chenqing Hua, Guillaume Rabusseau, Jian Tang:
High-Order Pooling for Graph Neural Networks with Tensor Decomposition. NeurIPS 2022 - [i31]Chenqing Hua, Guillaume Rabusseau, Jian Tang:
High-Order Pooling for Graph Neural Networks with Tensor Decomposition. CoRR abs/2205.11691 (2022) - [i30]Clara Lacroce, Prakash Panangaden, Guillaume Rabusseau:
Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case. CoRR abs/2206.00172 (2022) - [i29]Tianyu Li, Bogdan Mazoure, Guillaume Rabusseau:
Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata. CoRR abs/2206.03923 (2022) - [i28]Kaiwen Hou, Guillaume Rabusseau:
Spectral Regularization: an Inductive Bias for Sequence Modeling. CoRR abs/2211.02255 (2022) - 2021
- [c21]Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau:
Understanding Capacity Saturation in Incremental Learning. Canadian AI 2021 - [c20]Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier:
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. AISTATS 2021: 1072-1080 - [c19]Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots:
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. AISTATS 2021: 2080-2088 - [c18]Jacob Miller, Guillaume Rabusseau, John Terilla:
Tensor Networks for Probabilistic Sequence Modeling. AISTATS 2021: 3079-3087 - [c17]Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, Guillaume Rabusseau:
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. ICALP 2021: 118:1-118:20 - [c16]Clara Lacroce, Prakash Panangaden, Guillaume Rabusseau:
Extracting Weighted Automata for Approximate Minimization in Language Modelling. ICGI 2021: 92-112 - [c15]Behnoush Khavari, Guillaume Rabusseau:
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models. NeurIPS 2021: 10931-10943 - [i27]Greta Laage, Emma Frejinger, William L. Hamilton, Andrea Lodi, Guillaume Rabusseau:
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application. CoRR abs/2101.10249 (2021) - [i26]Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, Guillaume Rabusseau:
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. CoRR abs/2102.06860 (2021) - [i25]Clara Lacroce, Prakash Panangaden, Guillaume Rabusseau:
Extracting Weighted Automata for Approximate Minimization in Language Modelling. CoRR abs/2106.02965 (2021) - [i24]Behnoush Khavari, Guillaume Rabusseau:
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models. CoRR abs/2106.11827 (2021) - [i23]Beheshteh T. Rakhshan, Guillaume Rabusseau:
Rademacher Random Projections with Tensor Networks. CoRR abs/2110.13970 (2021) - 2020
- [c14]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. AISTATS 2020: 2852-2862 - [c13]Beheshteh T. Rakhshan, Guillaume Rabusseau:
Tensorized Random Projections. AISTATS 2020: 3306-3316 - [c12]Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau:
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract). IJCAI 2020: 5055-5059 - [c11]Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau, Reihaneh Rabbany:
Laplacian Change Point Detection for Dynamic Graphs. KDD 2020: 349-358 - [i22]Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau:
Provably efficient reconstruction of policy networks. CoRR abs/2002.02863 (2020) - [i21]Jacob Miller, Guillaume Rabusseau, John Terilla:
Tensor Networks for Language Modeling. CoRR abs/2003.01039 (2020) - [i20]Stefano Alletto, Shenyang Huang, Vincent François-Lavet, Yohei Nakata, Guillaume Rabusseau:
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. CoRR abs/2003.01181 (2020) - [i19]Beheshteh T. Rakhshan, Guillaume Rabusseau:
Tensorized Random Projections. CoRR abs/2003.05101 (2020) - [i18]Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau, Reihaneh Rabbany:
Laplacian Change Point Detection for Dynamic Graphs. CoRR abs/2007.01229 (2020) - [i17]Meraj Hashemizadeh, Michelle Liu, Jacob Miller, Guillaume Rabusseau:
Adaptive Tensor Learning with Tensor Networks. CoRR abs/2008.05437 (2020) - [i16]Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier:
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. CoRR abs/2010.04003 (2020) - [i15]Tianyu Li, Doina Precup, Guillaume Rabusseau:
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning. CoRR abs/2010.10029 (2020) - [i14]Siddarth Srinivasan, Sandesh Adhikary, Jacob Miller, Guillaume Rabusseau, Byron Boots:
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. CoRR abs/2010.10653 (2020)
2010 – 2019
- 2019
- [j2]Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau:
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability. J. Artif. Intell. Res. 65: 1-30 (2019) - [j1]Raphaël Bailly, Guillaume Rabusseau, François Denis:
Recognizable series on graphs and hypergraphs. J. Comput. Syst. Sci. 104: 58-81 (2019) - [c10]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. AISTATS 2019: 1630-1639 - [i13]Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau:
Neural Architecture Search for Class-incremental Learning. CoRR abs/1909.06686 (2019) - [i12]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. CoRR abs/1911.05010 (2019) - 2018
- [c9]Tianyu Li, Guillaume Rabusseau, Doina Precup:
Nonlinear Weighted Finite Automata. AISTATS 2018: 679-688 - [c8]Guillaume Rabusseau:
Minimization of Graph Weighted Models over Circular Strings. FoSSaCS 2018: 513-529 - [c7]Philip Amortila, Guillaume Rabusseau:
Learning Graph Weighted Models on Pictures. ICGI 2018: 104-117 - [i11]Philip Amortila, Guillaume Rabusseau:
Learning Graph Weighted Models on Pictures. CoRR abs/1806.08297 (2018) - [i10]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. CoRR abs/1807.01406 (2018) - [i9]Eric Crawford, Guillaume Rabusseau, Joelle Pineau:
Sequential Coordination of Deep Models for Learning Visual Arithmetic. CoRR abs/1809.04988 (2018) - [i8]Matteo Ruffini, Guillaume Rabusseau, Borja Balle:
Hierarchical Methods of Moments. CoRR abs/1810.07468 (2018) - [i7]Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia, Guillaume Rabusseau, Jackie Chi Kit Cheung, Doina Precup:
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss. CoRR abs/1812.07627 (2018) - 2017
- [c6]Matteo Ruffini, Guillaume Rabusseau, Borja Balle:
Hierarchical Methods of Moments. NIPS 2017: 1901-1911 - [c5]Guillaume Rabusseau, Borja Balle, Joelle Pineau:
Multitask Spectral Learning of Weighted Automata. NIPS 2017: 2588-2597 - [i6]Tianyu Li, Guillaume Rabusseau, Doina Precup:
Neural Network Based Nonlinear Weighted Finite Automata. CoRR abs/1709.04380 (2017) - [i5]Xingwei Cao, Guillaume Rabusseau, Joelle Pineau:
Tensor Regression Networks with various Low-Rank Tensor Approximations. CoRR abs/1712.09520 (2017) - 2016
- [c4]Guillaume Rabusseau, Borja Balle, Shay B. Cohen:
Low-Rank Approximation of Weighted Tree Automata. AISTATS 2016: 839-847 - [c3]Guillaume Rabusseau, Hachem Kadri:
Low-Rank Regression with Tensor Responses. NIPS 2016: 1867-1875 - [i4]Guillaume Rabusseau, Hachem Kadri:
Higher-Order Low-Rank Regression. CoRR abs/1602.06863 (2016) - 2015
- [c2]Raphaël Bailly, François Denis, Guillaume Rabusseau:
Recognizable Series on Hypergraphs. LATA 2015: 639-651 - [i3]Guillaume Rabusseau, Borja Balle, Shay B. Cohen:
Weighted Tree Automata Approximation by Singular Value Truncation. CoRR abs/1511.01442 (2015) - 2014
- [c1]Guillaume Rabusseau, François Denis:
Maximizing a Tree Series in the Representation Space. ICGI 2014: 124-138 - [i2]Guillaume Rabusseau, François Denis:
Learning Negative Mixture Models by Tensor Decompositions. CoRR abs/1403.4224 (2014) - [i1]Raphaël Bailly, François Denis, Guillaume Rabusseau:
Recognizable Series on Hypergraphs. CoRR abs/1404.7533 (2014)
Coauthor Index
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last updated on 2024-09-04 01:22 CEST by the dblp team
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