Guillaume Rabusseau
23 papers · 2016–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π£ Hot Topic Early Bird
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Academic Marathon
(9)
π
Triple Crown
π₯
Mega-Team
(34)
π
Keyword Champion
(4)
ποΈ
Keyword Collector
(91)
β‘
Prolific Year
(6)
π
Trend Setter
π
Century Club
(23)
π₯
Unstoppable
(10)
Conferences
AISTATS (10)
NIPS (8)
ICML (2)
ACL (1)
ICLR (1)
IJCAI (1)
Top co-authors
Keywords
tensor decomposition
(6)
spectral learning
(4)
link prediction
(2)
weighted automaton
(2)
probabilistic modeling
(2)
partial observability
(2)
representation learning
(2)
weighted automata
(2)
recurrent neural network
(2)
node classification
(2)
matrix product state
(2)
weighted tree automata
(2)
generalization bound
(2)
tensor network
(2)
graph representation learning
(1)
graph classification
(1)
catastrophic forgetting
(1)
reinforcement learning theory
(1)
matrix factorization
(1)
continual learning
(1)
Papers
Grokking Beyond the Euclidean Norm of Model Parameters
ICML 2025
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
ICLR 2024
Simulating weighted automata over sequences and trees with transformers
AISTATS 2024
A Tensor Decomposition Perspective on Second-order RNNs
ICML 2024
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
NIPS 2024
Length independent PAC-Bayes bounds for Simple RNNs
AISTATS 2024
Efficient Leverage Score Sampling for Tensor Train Decomposition
NIPS 2024
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
NIPS 2023
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
NIPS 2022
Tensor Networks for Probabilistic Sequence Modeling
AISTATS 2021
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models
NIPS 2021
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
AISTATS 2021
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
AISTATS 2021
Tensorized Random Projections
AISTATS 2020
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)
IJCAI 2020
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
AISTATS 2020
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
AISTATS 2019
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges
ACL 2019
Nonlinear Weighted Finite Automata
AISTATS 2018
Multitask Spectral Learning of Weighted Automata
NIPS 2017
Hierarchical Methods of Moments
NIPS 2017
Low-Rank Regression with Tensor Responses
NIPS 2016
Low-Rank Approximation of Weighted Tree Automata
AISTATS 2016