5 papers · 1 filter
Byzantine-Robust Federated Representation Learning
Leonardo F. Toso, James Anderson, Rafael Pinot +1
We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients without knowing their identity. Un…
JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models
Leonardo F. Toso, Davit Shadunts, Yunyang Lu +5
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…
Learning Stabilizing Policies via an Unstable Subspace Representation
Leonardo F. Toso, Lintao Ye, James Anderson
We study the problem of learning to stabilize (LTS) a linear time-invariant (LTI) system. Policy gradient (PG) methods for control assume access to an initial stabilizing policy. H…