8 citations · 16 across the 8 of their papers we have counts for
8 papers
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Arman Adibi, Nicolo Dal Fabbro, Luca Schenato +5
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updat…
Humans-in-the-Building: Getting Rid of Thermostats for Optimal Thermal Comfort Control in Energy Management Systems
Jiali Wang, Yang Tang, Luca Schenato
Given the widespread attention to individual thermal comfort, coupled with significant energy-saving potential inherent in energy management systems for optimizing indoor environme…
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation
Alessio Maritan, Subhrakanti Dey, Luca Schenato
Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing ra…
Visibility-Constrained Control of Multirotor via Reference Governor
Dabin Kim, Matthias Pezzutto, Luca Schenato +1
For safe vision-based control applications, perception-related constraints have to be satisfied in addition to other state constraints. In this paper, we deal with the problem wher…
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Nicolò Dal Fabbro, Michele Rossi, Luca Schenato +1
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such a…
ZO-JADE: Zeroth-order Curvature-Aware Multi-Agent Convex Optimization
Alessio Maritan, Luca Schenato
In this work we address the problem of convex optimization in a multi-agent setting where the objective is to minimize the mean of local cost functions whose derivatives are not av…