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pro vyhledávání: '"Fabbro, Nicolò Dal"'
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 research efforts, on the other hand, have focused on optimizing CP efficie
Externí odkaz:
http://arxiv.org/abs/2411.01696
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving $N$ agents, this beneficial effect usually comes in the form of an $N$-fold linear convergen
Externí odkaz:
http://arxiv.org/abs/2407.20441
Autor:
Fabbro, Nicolò Dal, Adibi, Arman, Poor, H. Vincent, Kulkarni, Sanjeev R., Mitra, Aritra, Pappas, George J.
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous
Externí odkaz:
http://arxiv.org/abs/2403.17247
Autor:
Adibi, Arman, Fabbro, Nicolo Dal, Schenato, Luca, Kulkarni, Sanjeev, Poor, H. Vincent, Pappas, George J., Hassani, Hamed, Mitra, Aritra
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been exten
Externí odkaz:
http://arxiv.org/abs/2402.11800
Autor:
Ballotta, Luca, Fabbro, Nicolò Dal, Perin, Giovanni, Schenato, Luca, Rossi, Michele, Piro, Giuseppe
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 massive amount of data that smart vehicles will collect from onboard
Externí odkaz:
http://arxiv.org/abs/2311.18741
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and syst
Externí odkaz:
http://arxiv.org/abs/2305.10852
Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, whether similar speedups can be established for reinforcement learning
Externí odkaz:
http://arxiv.org/abs/2305.08104
Autor:
Meneghello, Francesca, Fabbro, Nicolò Dal, Garlisi, Domenico, Tinnirello, Ilenia, Rossi, Michele
Publikováno v:
IEEE Communications Magazine, 2023
In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still a
Externí odkaz:
http://arxiv.org/abs/2305.03170
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communica
Externí odkaz:
http://arxiv.org/abs/2202.05800
Autor:
Meneghello, Francesca, Garlisi, Domenico, Fabbro, Nicolò Dal, Tinnirello, Ilenia, Rossi, Michele
Publikováno v:
IEEE Transactions on Mobile Computing (2022)
In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across diffe
Externí odkaz:
http://arxiv.org/abs/2103.09924