8 papers
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.…
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 Discrete-Time Linear Quadratic Games
Chiara Cavalagli, Alberto Bemporad, Mario Zanon
For the characterization of Feedback Nash Equilibria (FNE) in linear quadratic games, this paper provides a detailed analysis of the discrete-time discounted coupled best-response…
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…