most citedStochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

3 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Conformal Risk Minimization with Variance Reduction

Sima Noorani, Orlando Romero, Nicolo Dal Fabbro +2

Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent rese…

cs.MA20241 cited

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

Nicolò Dal Fabbro, Arman Adibi, Aritra Mitra +1

Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving agents, this benef…

cs.LG20243 cited

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…

cs.AI2024

DASA: Delay-Adaptive Multi-Agent Stochastic Approximation

Nicolò Dal Fabbro, Arman Adibi, H. Vincent Poor +3

We consider a setting in which agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume tha…

eess.SY2023

VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning

Luca Ballotta, Nicolò Dal Fabbro, Giovanni Perin +3

Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the…