Matilde Gargiani

21 papers A* 3Journal 16Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Semismooth newton methods for risk-averse Markov decision processes.
Autom.
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis, John Lygeros
2025 J jnl
Inside madupite: Technical Design and Performance.
CoRR
Matilde Gargiani, Robin Sieber, Philip Pawlowsky, John Lygeros
2025 J jnl
madupite: A High-Performance Distributed Solver for Large-Scale Markov Decision Processes.
CoRR
Matilde Gargiani, Robin Sieber, Philip Pawlowsky, Václav Hapla, John Lygeros
2025 J jnl
madupite: A High-Performance Distributed Solver for Large-Scale Markov Decision Processes.
J. Open Source Softw.
Matilde Gargiani, Philip Pawlowsky, Robin Sieber, Václav Hapla, John Lygeros
2024 J jnl
Parallel and Flexible Dynamic Programming via the Mini-Batch Bellman Operator.
IEEE Trans. Autom. Control.
Matilde Gargiani, Andrea Martinelli, Max Ruts Martinez, John Lygeros
2022 J jnl
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective.
IEEE Control. Syst. Lett.
Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros
2022 J jnl
Data-driven optimal control of affine systems: A linear programming perspective.
CoRR
Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros
2022 J jnl
Data-driven optimal control with a relaxed linear program.
Autom.
Andrea Martinelli, Matilde Gargiani, John Lygeros
2022 J jnl
Dynamic Programming Through the Lens of Semismooth Newton-Type Methods.
IEEE Control. Syst. Lett.
Matilde Gargiani, Andrea Zanelli, Dominic Liao-McPherson, Tyler H. Summers, John Lygeros
2022 A* conf
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation.
ICML
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros
2022 J jnl
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation.
CoRR
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros
2022 — conf
Policy Iteration for Multiplicative Noise Output Feedback Control.
CDC
Benjamin Gravell, Matilde Gargiani, John Lygeros, Tyler H. Summers
2022 J jnl
Policy Iteration for Multiplicative Noise Output Feedback Control.
CoRR
Benjamin Gravell, Matilde Gargiani, John Lygeros, Tyler H. Summers
2021 — conf
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control.
CDC
Andrea Martinelli, Matilde Gargiani, John Lygeros
2021 J jnl
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control.
CoRR
Andrea Martinelli, Matilde Gargiani, John Lygeros
2020 J jnl
Convergence Analysis of Homotopy-SGD for non-convex optimization.
CoRR
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
2020 J jnl
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs.
CoRR
Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Frank Hutter
2020 A* conf
Transferring Optimality Across Data Distributions via Homotopy Methods.
ICLR
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
2019 J jnl
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings.
CoRR
Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter
2018 A* conf
A Distributed Second-Order Algorithm You Can Trust.
ICML
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi
2018 J jnl
A Distributed Second-Order Algorithm You Can Trust.
CoRR
Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi