Guillaume Rabusseau

Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models

Rademacher Random Projections with Tensor Networks

Lower and Upper Bounds on the VC-Dimension of Tensor Network Models.

Understanding Capacity Saturation in Incremental Learning

Extracting Weighted Automata for Approximate Minimization in Language Modelling.

Quantum Tensor Networks, Stochastic Processes, and Weighted Automata

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

Tensor Networks for Probabilistic Sequence Modeling

Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata

Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin.

Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application.

Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?

Approximate minimization of weighted tree automata

Quantum Tensor Networks, Stochastic Processes, and Weighted Automata.

Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning.

Laplacian Change Point Detection for Dynamic Graphs

Adaptive Learning of Tensor Network Structures

Adaptive Tensor Learning with Tensor Networks

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract).

Tensorized Random Projections

Tensor Networks for Language Modeling.

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning.

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.

Provably efficient reconstruction of policy networks.

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Neural Architecture Search for Class-incremental Learning.

Recognizable series on graphs and hypergraphs

On overfitting and asymptotic bias in batch reinforcement learning with partial observability

Clustering-Oriented Representation Learning with Attractive-Repulsive Loss.

Hierarchical Methods of Moments

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

Nonlinear Weighted Finite Automata

Sequential Coordination of Deep Models for Learning Visual Arithmetic