activity
20232025
most citedDynamic Brain Networks with Prescribed Functional Connectivity

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Context-Selective State Space Models: Feedback is All You Need

Riccardo Zattra, Giacomo Baggio, Umberto Casti +2

Transformers, powered by the attention mechanism, are the backbone of most foundation models, yet they suffer from quadratic complexity and difficulties in dealing with long-range…

math.OC2025

Anti-windup design for internal model online constrained optimization

Umberto Casti, Sandro Zampieri

This paper proposes a novel algorithmic design procedure for online constrained optimization grounded in control-theoretic principles. By integrating the Internal Model Principle (…

eess.SY2025

Geometric SSM: LTI State Space Models for Selective Tasks

Umberto Casti, Giacomo Baggio, Sandro Zampieri +1

A key claim in recent work on Selective State Space Models is that selectivity, the ability to focus on relevant information while filtering irrelevant inputs, requires breaking th…

eess.SY2025

Controllable Neural Architectures for Multi-Task Control

Umberto Casti, Giacomo Baggio, Sandro Zampieri +1

This paper studies a multi-task control problem where multiple linear systems are to be regulated by a single non-linear controller. In particular, motivated by recent advances in…

math.OC2024

Stochastic models for online optimization

Umberto Casti, Sandro Zampieri

In this paper, we propose control-theoretic methods as tools for the design of online optimization algorithms that are able to address dynamic, noisy, and partially uncertain time-…

eess.SY2023★ 2 cited

Dynamic Brain Networks with Prescribed Functional Connectivity

Umberto Casti, Giacomo Baggio, Danilo Benozzo +3

In this paper, we consider stable stochastic linear systems modeling whole-brain resting-state dynamics. We parametrize the state matrix of the system (effective connectivity) in t…

math.OC2023★ 2 cited

A Control Theoretical Approach to Online Constrained Optimization

Umberto Casti, Nicola Bastianello, Ruggero Carli +1

In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by lever…