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A. Patané

18 papers hereh-index 16952 citations43 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author14
  • last author2

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.LG15
  • eess.SY1
  • physics.app-ph1
  • stat.ML1
same name
  • A. Patané — 25 papers, h 40
  • A. Patané — 5 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182024
most citedRobustness of Bayesian Neural Networks to Gradient-Based Attacks

27 citations · 74 across the 16 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

Probabilistic Reach-Avoid for Bayesian Neural Networks

Matthew Wicker, Luca Laurenti, Andrea Patane +3

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 s…

cs.LG2023★ 1 cited

Adversarial Robustness Certification for Bayesian Neural Networks

Matthew Wicker, Andrea Patane, Luca Laurenti +1

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}…

cs.LG2023

BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming

Steven Adams, Andrea Patane, Morteza Lahijanian +1

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 point…

cs.LG2023★ 1 cited

Individual Fairness in Bayesian Neural Networks

Alice Doherty, Matthew Wicker, Luca Laurenti +1

We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the ε-δ-individual fairness notion, which requires that, for any pair of input…

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