Maximizing a Tree Series in the Representation Space. - Guillaume Rabusseau; François Denis (ICGI, 2014)

Learning Negative Mixture Models by Tensor Decompositions. - Guillaume Rabusseau; François Denis (CoRR, 2014)

Recognizable Series on Hypergraphs. - Raphaël Bailly; François Denis; Guillaume Rabusseau (LATA, 2015)

Weighted Tree Automata Approximation by Singular Value Truncation. - Guillaume Rabusseau; Borja Balle; Shay B. Cohen (CoRR, 2015)

Low-Rank Approximation of Weighted Tree Automata. - Guillaume Rabusseau; Borja Balle; Shay B. Cohen (AISTATS, 2016)

Low-Rank Regression with Tensor Responses. - Guillaume Rabusseau; Hachem Kadri (NIPS, 2016)

Higher-Order Low-Rank Regression. - Guillaume Rabusseau; Hachem Kadri (CoRR, 2016)

Multitask Spectral Learning of Weighted Automata. - Guillaume Rabusseau; Borja Balle; Joelle Pineau (NIPS, 2017)

Hierarchical Methods of Moments. - Matteo Ruffini; Guillaume Rabusseau; Borja Balle (NIPS, 2017)

Neural Network Based Nonlinear Weighted Finite Automata. - Tianyu Li; Guillaume Rabusseau; Doina Precup (CoRR, 2017)

Tensor Regression Networks with various Low-Rank Tensor Approximations. - Xingwei Cao; Guillaume Rabusseau; Joelle Pineau (CoRR, 2017)

Nonlinear Weighted Finite Automata. - Tianyu Li; Guillaume Rabusseau; Doina Precup (AISTATS, 2018)

Minimization of Graph Weighted Models over Circular Strings. - Guillaume Rabusseau (FoSSaCS, 2018)

Learning Graph Weighted Models on Pictures. - Philip Amortila; Guillaume Rabusseau (ICGI, 2018)

Sequential Coordination of Deep Models for Learning Visual Arithmetic. - Eric Crawford; Guillaume Rabusseau; Joelle Pineau (CoRR, 2018)

Clustering-Oriented Representation Learning with Attractive-Repulsive Loss. - Kian Kenyon-Dean; Andre Cianflone; Lucas Page-Caccia; Guillaume Rabusseau; Jackie Chi Kit Cheung; Doina Precup (CoRR, 2018)

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. - Guillaume Rabusseau; Tianyu Li; Doina Precup (AISTATS, 2019)

Neural Architecture Search for Class-incremental Learning. - Shenyang Huang; Vincent François-Lavet; Guillaume Rabusseau (CoRR, 2019)

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability. - Vincent François-Lavet; Guillaume Rabusseau; Joelle Pineau; Damien Ernst; Raphael Fonteneau (J. Artif. Intell. Res., 2019)

Recognizable series on graphs and hypergraphs. - Raphaël Bailly; Guillaume Rabusseau; François Denis (J. Comput. Syst. Sci., 2019)

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. - Tianyu Li; Bogdan Mazoure; Doina Precup; Guillaume Rabusseau (AISTATS, 2020)

Tensorized Random Projections. - Beheshteh T. Rakhshan; Guillaume Rabusseau (AISTATS, 2020)

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract). - Vincent François-Lavet; Guillaume Rabusseau; Joelle Pineau; Damien Ernst; Raphael Fonteneau (IJCAI, 2020)

Laplacian Change Point Detection for Dynamic Graphs. - Shenyang Huang; Yasmeen Hitti; Guillaume Rabusseau; Reihaneh Rabbany (KDD, 2020)

Provably efficient reconstruction of policy networks. - Bogdan Mazoure; Thang Doan; Tianyu Li; Vladimir Makarenkov; Joelle Pineau; Doina Precup; Guillaume Rabusseau (CoRR, 2020)

Tensor Networks for Language Modeling. - Jacob Miller 003; Guillaume Rabusseau; John Terilla (CoRR, 2020)

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. - Stefano Alletto; Shenyang Huang; Vincent François-Lavet; Yohei Nakata; Guillaume Rabusseau (CoRR, 2020)

Adaptive Tensor Learning with Tensor Networks. - Meraj Hashemizadeh; Michelle Liu; Jacob Miller 003; Guillaume Rabusseau (CoRR, 2020)

Understanding Capacity Saturation in Incremental Learning. - Shenyang Huang; Vincent François-Lavet; Guillaume Rabusseau (Canadian AI, 2021)

Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. - Sandesh Adhikary; Siddarth Srinivasan; Jacob Miller 003; Guillaume Rabusseau; Byron Boots (AISTATS, 2021)

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. - Thang Doan; Mehdi Abbana Bennani; Bogdan Mazoure; Guillaume Rabusseau; Pierre Alquier (AISTATS, 2021)

Tensor Networks for Probabilistic Sequence Modeling. - Jacob Miller 003; Guillaume Rabusseau; John Terilla (AISTATS, 2021)

Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. - Borja Balle; Clara Lacroce; Prakash Panangaden; Doina Precup; Guillaume Rabusseau (ICALP, 2021)

Extracting Weighted Automata for Approximate Minimization in Language Modelling. - Clara Lacroce; Prakash Panangaden; Guillaume Rabusseau (ICGI, 2021)

Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models. - Behnoush Khavari; Guillaume Rabusseau (NeurIPS, 2021)

Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application. - Greta Laage; Emma Frejinger; William L. Hamilton; Andrea Lodi 001; Guillaume Rabusseau (CoRR, 2021)

Lower and Upper Bounds on the VC-Dimension of Tensor Network Models. - Behnoush Khavari; Guillaume Rabusseau (CoRR, 2021)

Rademacher Random Projections with Tensor Networks. - Beheshteh T. Rakhshan; Guillaume Rabusseau (CoRR, 2021)

High-Order Pooling for Graph Neural Networks with Tensor Decomposition. - Chenqing Hua; Guillaume Rabusseau; Jian Tang (NeurIPS, 2022)

Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case. - Clara Lacroce; Prakash Panangaden; Guillaume Rabusseau (CoRR, 2022)

Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata. - Tianyu Li; Bogdan Mazoure; Guillaume Rabusseau (CoRR, 2022)

Spectral Regularization: an Inductive Bias for Sequence Modeling. - Kaiwen Hou; Guillaume Rabusseau (CoRR, 2022)

Approximate minimization of weighted tree automata. - Borja Balle; Guillaume Rabusseau (Inf. Comput., 2022)

Low-Rank Representation of Reinforcement Learning Policies. - Bogdan Mazoure; Thang Doan; Tianyu Li; Vladimir Makarenkov; Joelle Pineau; Doina Precup; Guillaume Rabusseau (J. Artif. Intell. Res., 2022)

Temporal Graph Benchmark for Machine Learning on Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Jacob Danovitch; Matthias Fey; Weihua Hu; Emanuele Rossi; Jure Leskovec; Michael M. Bronstein; Guillaume Rabusseau; Reihaneh Rabbany (NeurIPS, 2023)

Fast and Attributed Change Detection on Dynamic Graphs with Density of States. - Shenyang Huang; Jacob Danovitch; Guillaume Rabusseau; Reihaneh Rabbany (PAKDD (1), 2023)

Explaining Graph Neural Networks Using Interpretable Local Surrogates. - Farzaneh Heidari; Perouz Taslakian; Guillaume Rabusseau (TAG-ML, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - 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; et al. (CoRR, 2023)

Generative Learning of Continuous Data by Tensor Networks. - Alex Meiburg; Jing Chen; Jacob Miller 003; Raphaëlle Tihon; Guillaume Rabusseau; Alejandro Perdomo-Ortiz (CoRR, 2023)

Length independent PAC-Bayes bounds for Simple RNNs. - Volodimir Mitarchuk; Clara Lacroce; Rémi Eyraud; Rémi Emonet; Amaury Habrard; Guillaume Rabusseau (AISTATS, 2024)

Simulating weighted automata over sequences and trees with transformers. - Michael Rizvi-Martel; Maude Lizaire; Clara Lacroce; Guillaume Rabusseau (AISTATS, 2024)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - 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; et al. (ICLR, 2024)

A Tensor Decomposition Perspective on Second-order RNNs. - Maude Lizaire; Michael Rizvi-Martel; Marawan Gamal Abdel Hameed; Guillaume Rabusseau (ICML, 2024)

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Reihaneh Rabbany; Guillaume Rabusseau; Emanuele Rossi (LoG, 2024)

Efficient Leverage Score Sampling for Tensor Train Decomposition. - Vivek Bharadwaj; Beheshteh T. Rakhshan; Osman Asif Malik; Guillaume Rabusseau (NeurIPS, 2024)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. - Julia Gastinger; Shenyang Huang; Michael Galkin; Erfan Loghmani; Ali Parviz; Farimah Poursafaei; Jacob Danovitch; Emanuele Rossi; Ioannis Koutis; Heiner Stuckenschmidt; Reihaneh Rabbany; Guillaume Rabusseau (NeurIPS, 2024)

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs. - Razieh Shirzadkhani; Tran Gia Bao Ngo; Kiarash Shamsi; Shenyang Huang; Farimah Poursafaei; Poupak Azad; Reihaneh Rabbany; Baris Coskunuzer; Guillaume Rabusseau; Cuneyt Gurcan Akcora (CoRR, 2024)

ROSA: Random Subspace Adaptation for Efficient Fine-Tuning. - Marawan Gamal Abdel Hameed; Aristides Milios; Siva Reddy; Guillaume Rabusseau (CoRR, 2024)

GFlowNets for Hamiltonian decomposition in groups of compatible operators. - Isaac L. Huidobro-Meezs; Jun Dai; Guillaume Rabusseau; Rodrigo A. Vargas-Hernández (CoRR, 2024)

Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data. - Soroush Omranpour; Guillaume Rabusseau; Reihaneh Rabbany (CoRR, 2024)

Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data. - Soroush Omranpour; Guillaume Rabusseau; Reihaneh Rabbany (CoRR, 2024)

Optimal approximate minimization of one-letter weighted finite automata. - Clara Lacroce; Borja Balle; Prakash Panangaden; Guillaume Rabusseau (Math. Struct. Comput. Sci., 2024)

Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs. - Shenyang Huang; Samy Coulombe; Yasmeen Hitti; Reihaneh Rabbany; Guillaume Rabusseau (ACM Trans. Knowl. Discov. Data, 2024)

Connecting weighted automata, tensor networks and recurrent neural networks through spectral learning. - Tianyu Li; Doina Precup; Guillaume Rabusseau (Mach. Learn., 2024)

Are Large Language Models Good Temporal Graph Learners? - Shenyang Huang; Ali Parviz; Emma Kondrup; Zachary Yang; Zifeng Ding; Michael M. Bronstein; Reihaneh Rabbany; Guillaume Rabusseau (CoRR, 2025)

Grokking Beyond the Euclidean Norm of Model Parameters. - Pascal Tikeng Notsawo Jr.; Guillaume Dumas; Guillaume Rabusseau (CoRR, 2025)

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs. - Alireza Dizaji; Benedict Aaron Tjandra; Mehrab Hamidi; Shenyang Huang; Guillaume Rabusseau (CoRR, 2025)

Numerical PDE solvers outperform neural PDE solvers. - Patrick Chatain; Michael Rizvi-Martel; Guillaume Rabusseau; Adam Oberman (CoRR, 2025)