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Leonardo F. Toso
Leonardo F. Toso
Columbia University
Correu electrònic verificat a columbia.edu - Pàgina d'inici
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Model-free learning with heterogeneous dynamical systems: A federated LQR approach
H Wang, LF Toso, A Mitra, J Anderson
arXiv preprint arXiv:2308.11743, 2023
182023
Fedsysid: A federated approach to sample-efficient system identification
H Wang, LF Toso, J Anderson
Learning for Dynamics and Control Conference, 1308-1320, 2023
172023
Sample-efficient linear representation learning from non-IID non-isotropic data
TTCK Zhang, LF Toso, J Anderson, N Matni
arXiv preprint arXiv:2308.04428, 2023
10*2023
Learning personalized models with clustered system identification
LF Toso, H Wang, J Anderson
2023 62nd IEEE Conference on Decision and Control (CDC), 7162-7169, 2023
82023
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
LF Toso, D Zhan, J Anderson, H Wang
6th Annual Learning for Dynamics & Control Conference, 2024
72024
Asynchronous Heterogeneous Linear Quadratic Regulator Design
LF Toso*, H Wang*, J Anderson
arXiv preprint arXiv:2404.09061, 2024
42024
Oracle complexity reduction for model-free lqr: A stochastic variance-reduced policy gradient approach
LF Toso, H Wang, J Anderson
2024 American Control Conference (ACC), 4032-4037, 2024
32024
Regional stability analysis of transitional fluid flows
LF Toso, R Drummond, SR Duncan
IEEE Control Systems Letters 6, 2287-2292, 2022
32022
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
BD Lee*, LF Toso*, TT Zhang*, J Anderson, N Matni
arXiv preprint arXiv:2407.05781, 2024
12024
Lyapunov function computation for periodic linear hybrid systems via Handelman, Polya and SoS approaches: A comparative study
LF Toso, G Valmorbida
IFAC-PapersOnLine 55 (16), 160-165, 2022
12022
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
D Zhan, LF Toso, J Anderson
arXiv preprint arXiv:2502.02332, 2025
2025
En aquests moments el sistema no pot dur a terme l'operació. Torneu-ho a provar més tard.
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