1 code implementation • 26 Feb 2024 • Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg
We introduce a formal statistical definition for the problem of backdoor detection in machine learning systems and use it to analyze the feasibility of such problems, providing evidence for the utility and applicability of our definition.
1 code implementation • 2 Jun 2023 • Eduardo Dadalto, Marco Romanelli, Georg Pichler, Pablo Piantanida
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable.
1 code implementation • 30 Mar 2022 • Georg Pichler, Marco Romanelli, Leonardo Rey Vega, Pablo Piantanida
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server.
1 code implementation • CVPR 2022 • Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida
The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today.
1 code implementation • 14 Feb 2022 • Georg Pichler, Pierre Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida
Mutual Information (MI) has been widely used as a loss regularizer for training neural networks.
1 code implementation • 11 Sep 2021 • Günther Koliander, Georg Pichler
Although group testing can help to significantly increase testing capabilities, the (repeated) testing of entire populations can exceed the resources of any country.
no code implementations • 9 May 2021 • Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida
We present a novel formalism, generalizing membership and attribute inference attack setups previously studied in the literature and connecting them to memorization and generalization.
no code implementations • 6 Dec 2020 • Ganesh Del Grosso, Georg Pichler, Pablo Piantanida
However, the use of power consumption data raises significant privacy concerns, as this data usually belongs to clients of a power company.
no code implementations • 7 Feb 2020 • Georg Pichler, Pablo Piantanida, Günther Koliander
In particular, we provide confidence bounds for simple histogram based estimation of differential entropy from a fixed number of samples, assuming that the probability density function is Lipschitz continuous with known Lipschitz constant and known, bounded support.
no code implementations • MIDL 2019 • Georg Pichler, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida
We juxtapose our approach to state-of-the-art segmentation adaptation via adversarial training in the network-output space.
no code implementations • 15 Feb 2016 • Georg Pichler, Pablo Piantanida, Gerald Matz
We study a novel multi-terminal source coding setup motivated by the biclustering problem.