ML Anthology

Rabusseau, Guillaume

31 publications

ICML 2025 Grokking Beyond the Euclidean Norm of Model Parameters Tikeng Notsawo Pascal Junior, Guillaume Dumas, Guillaume Rabusseau
TMLR 2025 Higher Order Transformers with Kronecker-Structured Attention Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
NeurIPS 2025 MiNT: Multi-Network Transfer Benchmark for Temporal Graph Learning Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani, Shenyang Huang, Farimah Poursafaei, Poupak Azad, Reihaneh Rabbany, Baris Coskunuzer, Guillaume Rabusseau, Cuneyt Gurcan Akcora
LoG 2025 UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, Guillaume Rabusseau, Emanuele Rossi
ICML 2024 A Tensor Decomposition Perspective on Second-Order RNNs Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal, Guillaume Rabusseau
MLJ 2024 Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks Through Spectral Learning Tianyu Li, Doina Precup, Guillaume Rabusseau
NeurIPS 2024 Efficient Leverage Score Sampling for Tensor Train Decomposition Vivek Bharadwaj, Beheshteh T. Rakhshan, Osman Asif Malik, Guillaume Rabusseau
AISTATS 2024 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
NeurIPS 2024 TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs Julia Gastinger, Shenyang Huang, Mikhail Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau
ICLR 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, Michael Craig, Michał 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
ICMLW 2023 Explaining Graph Neural Networks Using Interpretable Local Surrogates Farzaneh Heidari, Perouz Taslakian, Guillaume Rabusseau
ICMLW 2023 ROSA: Random Orthogonal Subspace Adaptation Marawan Gamal, Guillaume Rabusseau
NeurIPS 2023 Temporal Graph Benchmark for Machine Learning on Temporal Graphs Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
NeurIPS 2022 High-Order Pooling for Graph Neural Networks with Tensor Decomposition Chenqing Hua, Guillaume Rabusseau, Jian Tang
JAIR 2022 Low-Rank Representation of Reinforcement Learning Policies Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau
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 Quantum Tensor Networks, Stochastic Processes, and Weighted Automata Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots
AISTATS 2021 Tensor Networks for Probabilistic Sequence Modeling Jacob Miller, Guillaume Rabusseau, John Terilla
NeurIPSW 2021 Few Shot Image Generation via Implicit Autoencoding of Support Sets Andy Huang, Kuan-Chieh Wang, Guillaume Rabusseau, Alireza Makhzani
NeurIPS 2021 Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models Behnoush Khavari, Guillaume Rabusseau
AISTATS 2020 Efficient Planning Under Partial Observability with Unnormalized Q Functions and Spectral Learning Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau
IJCAI 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
AISTATS 2020 Tensorized Random Projections Beheshteh Rakhshan, Guillaume Rabusseau
AISTATS 2019 Connecting Weighted Automata and Recurrent Neural Networks Through Spectral Learning Guillaume Rabusseau, Tianyu Li, Doina Precup
JAIR 2019 On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau
AISTATS 2018 Nonlinear Weighted Finite Automata Tianyu Li, Guillaume Rabusseau, Doina Precup
NeurIPS 2017 Hierarchical Methods of Moments Matteo Ruffini, Guillaume Rabusseau, Borja Balle
NeurIPS 2017 Multitask Spectral Learning of Weighted Automata Guillaume Rabusseau, Borja Balle, Joelle Pineau
AISTATS 2016 Low-Rank Approximation of Weighted Tree Automata Guillaume Rabusseau, Borja Balle, Shay B. Cohen
NeurIPS 2016 Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri