Matilde Gargiani

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 17

Top Topics

Optimal Control

Infinite Horizon

Policy Gradient

Top Venues

IEEE Control. Syst. Lett.

Publications

Matilde Gargiani, Andrea Martinelli, Max Ruts Martinez, John Lygeros
Parallel and Flexible Dynamic Programming via the Mini-Batch Bellman Operator. IEEE Trans. Autom. Control. 69 (1) (2024)
Benjamin Gravell, Matilde Gargiani, John Lygeros, Tyler H. Summers
Policy Iteration for Multiplicative Noise Output Feedback Control. CDC (2022)
Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros
Data-driven optimal control of affine systems: A linear programming perspective. CoRR (2022)
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation. ICML (2022)
Benjamin Gravell, Matilde Gargiani, John Lygeros, Tyler H. Summers
Policy Iteration for Multiplicative Noise Output Feedback Control. CoRR (2022)
Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective. IEEE Control. Syst. Lett. 6 (2022)
Matilde Gargiani, Andrea Zanelli, Dominic Liao-McPherson, Tyler H. Summers, John Lygeros
Dynamic Programming Through the Lens of Semismooth Newton-Type Methods. IEEE Control. Syst. Lett. 6 (2022)
Andrea Martinelli, Matilde Gargiani, John Lygeros
Data-driven optimal control with a relaxed linear program. Autom. 136 (2022)
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation. CoRR (2022)
Andrea Martinelli, Matilde Gargiani, John Lygeros
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control. CDC (2021)
Andrea Martinelli, Matilde Gargiani, John Lygeros
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control. CoRR (2021)
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
Convergence Analysis of Homotopy-SGD for non-convex optimization. CoRR (2020)
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR (2020)
Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Frank Hutter
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs. CoRR (2020)
Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. CoRR (2019)
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
A Distributed Second-Order Algorithm You Can Trust. ICML (2018)
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
A Distributed Second-Order Algorithm You Can Trust. CoRR (2018)