Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Patanè, Andrea"'
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work we focus on discrete-time non-linear stochastic dynamical systems. We present a novel appro
Externí odkaz:
http://arxiv.org/abs/2403.15626
Autor:
Wicker, Matthew, Laurenti, Luca, Patane, Andrea, Paoletti, Nicola, Abate, Alessandro, Kwiatkowska, Marta
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the safety and robustness of sequential decisions made through a policy in
Externí odkaz:
http://arxiv.org/abs/2310.01951
We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations. Given a compact set of input points $T \subseteq \mathbb{R}^m$ and a set of output points $S \subseteq \mathbb{R}^n$, we define t
Externí odkaz:
http://arxiv.org/abs/2306.13614
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points $T\subset \mathbb{R}^n$, BNN-DP computes lower and upper bounds on t
Externí odkaz:
http://arxiv.org/abs/2306.10742
We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the $\epsilon$-$\delta$-individual fairness notion, which requires that, for any pair of input points that are $\epsilon$-similar according to a given si
Externí odkaz:
http://arxiv.org/abs/2304.10828
Autor:
Bortolussi, Luca, Carbone, Ginevra, Laurenti, Luca, Patane, Andrea, Sanguinetti, Guido, Wicker, Matthew
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial at
Externí odkaz:
http://arxiv.org/abs/2207.06154
We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the $\epsilon$-$\delta$-IF formulation, which, given a NN and a similarity metric learnt from data, requires that th
Externí odkaz:
http://arxiv.org/abs/2205.05763
Autor:
Wicker, Matthew, Laurenti, Luca, Patane, Andrea, Paoletti, Nicola, Abate, Alessandro, Kwiatkowska, Marta
Publikováno v:
In Artificial Intelligence September 2024 334
Autor:
Ojha, Varun, Jansen, Giorgio, Patane, Andrea, La Magna, Antonino, Romano, Vittorio, Nicosia, Giuseppe
Publikováno v:
Energy Systems, 2021
We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by v
Externí odkaz:
http://arxiv.org/abs/2109.07279
Autor:
Wicker, Matthew, Laurenti, Luca, Patane, Andrea, Paoletti, Nicola, Abate, Alessandro, Kwiatkowska, Marta
We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models. Specifically, we leverage bound propagation techniques and backward recursion to compute lower bounds for the pro
Externí odkaz:
http://arxiv.org/abs/2105.10134