3 citations · 6 across the 16 of their papers we have counts for
18 papers
Preserving Unstable Modes Through Inverse Dynamics in JEPA World Models
Leonardo F. Toso, Yann LeCun, James Anderson +1
Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feedback. Controlling such system…
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…
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…
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…
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…
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…
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Donglin Zhan, Leonardo F. Toso, James Anderson
We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically assumes that all available tasks are equ…
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
Bruce D. Lee, Leonardo F. Toso, Thomas T. Zhang +2
Representation learning is a powerful tool that enables learning over large multitudes of agents or domains by enforcing that all agents operate on a shared set of learned features…
Asynchronous Heterogeneous Linear Quadratic Regulator Design
Leonardo F. Toso, Han Wang, James Anderson
We address the problem of designing an LQR controller in a distributed setting, where M similar but not identical systems share their locally computed policy gradient (PG) estimate…
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
Leonardo F. Toso, Donglin Zhan, James Anderson +1
We investigate the problem of learning linear quadratic regulators (LQR) in a multi-task, heterogeneous, and model-free setting. We characterize the stability and personalization g…
Oracle Complexity Reduction for Model-free LQR: A Stochastic Variance-Reduced Policy Gradient Approach
Leonardo F. Toso, Han Wang, James Anderson
We investigate the problem of learning an -approximate solution for the discrete-time Linear Quadratic Regulator (LQR) problem via a Stochastic Variance-Reduced Policy Gradient…
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach
Han Wang, Leonardo F. Toso, Aritra Mitra +1
We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minim…
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
Thomas T. C. K. Zhang, Leonardo F. Toso, James Anderson +1
A powerful concept behind much of the recent progress in machine learning is the extraction of common features across data from heterogeneous sources or tasks. Intuitively, using a…
Learning Personalized Models with Clustered System Identification
Leonardo F. Toso, Han Wang, James Anderson
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario whe…
FedSysID: A Federated Approach to Sample-Efficient System Identification
Han Wang, Leonardo F. Toso, James Anderson
We study the problem of learning a linear system model from the observations of clients. The catch: Each client is observing data from a different dynamical system. This work a…