3 citations · 6 across the 16 of their papers we have counts for
16 papers
On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
Leonardo F. Toso, James Anderson, Nirupam Gupta +1
Federated learning (FL) is intrinsically heterogeneous: honest clients may have different data-generating models. On top of that, adversarial clients can make heterogeneity even mo…
Multitask LQG Control: Performance and Generalization Bounds
Leonardo F. Toso, Kasra Fallah, Charis Stamouli +2
We study multitask learning for stochastic and partially observed control systems, focusing on the linear quadratic Gaussian (LQG) problem. Our goal is to learn a common stabilizin…
Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models
Leonardo F. Toso, Davit Shadunts, Yunyang Lu +4
World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level reconstruction. However, recen…
Adversarially Robust Multitask Adaptive Control
Kasra Fallah, Leonardo F. Toso, James Anderson
We study adversarially robust multitask adaptive linear quadratic control; a setting where multiple systems collaboratively learn control policies under model uncertainty and adver…
Physics-informed learning under mixing: How physical knowledge speeds up learning
Anna Scampicchio, Leonardo F. Toso, Rahel Rickenbach +2
A major challenge in physics-informed machine learning is to understand how the incorporation of prior domain knowledge affects learning rates when data are dependent. Focusing on…
Policy Gradient Bounds in Multitask LQR
Charis Stamouli, Leonardo F. Toso, Anastasios Tsiamis +2
We analyze the performance of policy gradient in multitask linear quadratic regulation (LQR), where the system and cost parameters differ across tasks. The main goal of multitask L…