6 citations · 6 across the 7 of their papers we have counts for
8 papers
Active Learning MPC Objective Functions from Preferences
Hasna El Hasnaouy, Pablo Krupa, Mario Zanon +1
Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference…
Fast Gauss-Newton for Multiclass Cross-Entropy
Mikalai Korbit, Mario Zanon
In multiclass softmax cross-entropy, the full generalized Gauss-Newton (GGN) curvature couples all output logits through the softmax covariance, making curvature-vector products ha…
Second-Order, First-Class: A Composable Stack for Curvature-Aware Training
Mikalai Korbit, Mario Zanon
Second-order methods promise improved stability and faster convergence, yet they remain underused due to implementation overhead, tuning brittleness, and the lack of composable API…
On Two-Player Scalar Discounted Linear Quadratic Games
Chiara Cavalagli, Alberto Bemporad, Mario Zanon
This paper studies feedback Nash equilibria (FNE) in scalar two-player discounted linear-quadratic games. By explicitly discounting the objective function, we reveal the existence…
Learning the MPC objective function from human preferences
Pablo Krupa, Hasna El Hasnaouy, Mario Zanon +1
In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the…
Incremental Gauss-Newton Descent for Machine Learning
Mikalai Korbit, Mario Zanon
Stochastic gradient updates are widely used for their efficiency and scalability, but their effective step sizes can depend strongly on feature scaling and local model sensitivity.…
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Mikalai Korbit, Adeyemi D. Adeoye, Alberto Bemporad +1
We present Exact Gauss-Newton (EGN), a stochastic second-order optimization algorithm that combines the generalized Gauss-Newton (GN) Hessian approximation with low-rank linear alg…
Learning disturbance models for offset-free reference tracking
Pablo Krupa, Mario Zanon, Alberto Bemporad
This work presents a nonlinear control framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which…